INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.03 MARCH 2019
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: firstname.lastname@example.org Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.03 MARCH 2019
InternatIonal Journal of InnovatIve technology & creatIve engIneerIng Vol.9 No.03 March 2019
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO.03 MARCH 2019
From Editor's Desk Dear Researcher, Greetings! Scientists are hoping to stretch the periodic table even further, beyond tennessine and three other recently discovered elements that completed the table’s seventh row. Producing the next elements will require finessing new techniques using ultra powerful beams of ions, electrically charged atoms. Not to mention the stress of shipping more radioactive material across borders. At the far edge of the periodic table, elements decay within instants of their formation, offering very little time to study their properties. In fact, scientists still know little about the latest crew of newfound elements. So while some scientists are hunting for never-before-seen elements, others want to learn more about the table’s newcomers and the strange behaviors those super heavy elements may exhibit. Scientists keep pushing these super heavy elements further as part of the search for what’s poetically known as the island of stability. Atoms with certain numbers of protons and neutrons are expected to live longer than their fleeting friends, persisting perhaps for hours rather than fractions of a second. Such an island would give scientists enough time to study those elements more closely and understand their quirks. The first glimpses of that mysterious atoll have been spotted, but it’s not clear how to get a firm footing on its shores. Driving all this effort is a deep curiosity about how elements act at the boundaries of the periodic table.
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Contents Secured Efficient Emergency Message Transmission in VANET for Route Redirection during Road Accidents using Cloud Servers
Digital Evidence Collection Framework for Internet of Things
Secured Efficient Emergency Message Transmission in VANET for Route Redirection during Road Accidents using Cloud Servers A.Selvakumar
Assistant Professor, Department of Computer Science, Sree Saraswathi Thyagaraja College, (Autonomous), Pollachi, Tamil Nadu, India E-mail ID: email@example.com
M.Phil.,Research Scholar, Department of Computer Science, Sree Saraswathi Thyagaraja College, (Autonomous), Pollachi, Tamil Nadu, India E-mail ID: firstname.lastname@example.org Abstract- The system proposes a spatial multicast Protocol for supporting applications which require spatial coordination in vehicular ad hoc networks (VANETs). The spatial character of an emergency forward message to vehicles located in some physical region is denoted as region of significance (ROS). Vehicles located in ROS at the time must keep the connectivity to maintain the real-time data communication between all vehicles in ROS. The connectivity is kept of all vehicles in ROS through the vehicular ad hoc networks (VANETs). The connectivity of ROS is lost if any vehicle in ROS suddenly accelerates or decelerates its velocity. The temporal network fragmentation problem is occurred such that vehicle in ROS cannot successfully receive the emergency messages. To solve the problem, a new spatial multicast protocol is presented in this work to successfully disseminate emergency messages to all vehicles in ROS via a special geographic zone, called as region of promoting (ROP). The main contribution of this work is to develop a new spatial multicast protocol to dynamically estimate the accurate ROP to successfully disseminate emergency messages to all vehicles in ROP. To illustrate the performance achievement, simulation results are examined in terms of dissemination successful rate, packet overhead multiplication, packet delivery delay and throughput. Keywords: Vehicle Ad hoc Network (VANET) ROS, ROP.
1. INTRODUCTION Vehicular ad hoc networks (VANETs) are created by applying the principles of mobile ad hoc networks (MANETs) – the spontaneous creation of a wireless network for knowledge exchange – to the domain of vehicles. VANETs were introduced in 2001 underneath "car-to-car ad hoc mobile
communication and networking" applications, wherever networks are often shaped and knowledge are often relayed among cars. It absolutely was shown that vehicle-to-vehicle and vehicle-to-roadside communications architectures can co-exist in VANETs to supply road safety, navigation, and different wayside services. VANETs are a key a part of the intelligent transportation systems (ITS) framework. Sometimes, VANETs are referred as Intelligent Transportation Networks While, within the early 2000s, VANETs were seen as a mere matched application of Manet principles, they need since then developed into a field of analysis in their title. By 2015,(p3) the term VANET became principally synonymous with the additional generic term inter-vehicle communication (IVC), though the main focus remains on the side of spontaneous networking, abundant less on the utilization of infrastructure like Road side Units (RSUs) or cellular networks. The applications of VANET’S areas used Electronic brake lights - which permit a driver (or associate degree autonomous automobile or truck) to react to vehicles braking even if they may be obscured (e.g., by different vehicles). Platooning - that permits vehicles to closely (down to many inches) follow a number one vehicle by wirelessly receiving acceleration and steering info, so forming electronically coupled "road trains" Traffic info systems that use VANET communication to supply up-to-the minute obstacle reports to a vehicle's satellite navigation system On-The-Road Services - VANETs will facilitate the driving force to find services (shops, gas stations, etc.) on that street, and even be notified of any sale occurring at that moment.
2. RELATED WORKS A. Benslimane, T. Taleb, et. al.  developed for bunch entrance candidates (i.e., to be delineated later) consistent with key relevant metrics and choosing out of every cluster, a cluster head that is the entrance to interface VANET with the 3G network. Within the existing literature, bunch inside VANETs was performed primarily based upon metrics like vehicle speed, inter-vehicular distance, and therefore the direction of movement. Regarding the speed, the variance within the speed of vehicles at totally different instances isn't consistent. This variance in speed ends up in forceful changes within the inter-vehicular distance attributable to the unpredictable behaviour of drivers. G. Zhioua, N. Tabbane, et. al.  introduced an Intelligent transportation systems are presently attracting the eye of the analysis community and therefore the automotive business, that each aim to produce not solely a lot of safety within the transportation systems however different highquality services and applications for his or her customers still. During this paper, we tend to propose a cooperative traffic transmission algorithmic program in a very joint transport unintended network-Long Term Evolution Advanced (LTE Advanced) hybrid specification that elects an entrance to attach the supply vehicle to the LTE Advanced infrastructure beneath the scope of vehicle-to-infrastructure (V2I) communications. Q. Zhao, Y. Zhu, et. al.  described to contemplate a sensory knowledge gathering application of a transport unintended network (VANET) within which vehicles manufacture sensory knowledge, that ought to be gathered for knowledge analysis and creating choices. Knowledge delivery is especially difficult attributable to the distinctive characteristics of VANETs, like quick topology amendment, frequent disruptions and rare contact opportunities. D. Jia et al.  revealed transport safety applications, it's crucial to timely And dependably deliver transmission knowledge from a traveling vehicle to a wayside access purpose (AP) in an earring transport unintended network (VANET), that could be a typical transmission situation for drive-thru net. 3. METHODOLOGY ROS t (Region of Significance): Given an event vehicle Ve, ROS t is an elliptic region determined by Ve at time t, such that vehicle Vi must be successfully received the mobicast message from Ve at time t, where each Vi is located in the ROS t. In this work, ROS t is split into four quarters, each one is a sub-zone of relevance.
Our proposed algorithm consists of three parts: BEACON MESSAGE: Each vehicle broadcasts beacon messages periodically to obtain the information of the neighboring vehicles. Therefore, the beacon message includes the position, velocity and direction acquired from GPS. Eg There are five cars in the figure: vehicle A, B, C, D and E. After broadcasting the beacon message, vehicle C shows up in vehicle A, B, D and Eâ€™s neighbor list tables, and vehicles A, B, D and E show up in vehicle Câ€™s neighbor list table. Therefore, each vehicle uses beacon messages to maintain its own neighbor list table. STRAIGHT ROADS: while receiving the packet that needs to be forwarded to the destination, the vehicle takes itself as the center of coordinate axis and calculates the vector from itself to the destination. After that, the vehicle starts to calculate the vectors of all vehicles in the transmission range and figures out which vehicle is the closest to the destination. Greedy forwarding routing protocol is our chosen strategy. The difference between our proposed greedy forwarding and GPSR greedy forwarding is that we use the concept of vector to choose the next hop so that the accuracy can be improved. Eg-vehicle A receives a packet that needs to be forwarded to the destination vehicle D. The next hop, vehicle A first compares whether the vector of vehicle A is close to that of vehicle D. Thus, vehicle B will be chosen as the next hop. If the vehicle remains on straight roads, the packet will be forwarded in the same way. After receiving the signal that there is a coordinator ahead, greedy mode will change to predictive mode.
INTERSECTIONS: When a vehicle broadcasts the signal that it is a coordinator, the neighboring nodes will change to predictive mode to predict the movement of the neighboring nodes.
RECOVERY STRATEGY: Nevertheless, our proposed algorithm still cannot completely prevent vehicles from local maximum and the recovery strategy is unquestionably necessary. Thus, the right hand rule is adopted to forward the packet to the intersection for the decisionmaking. The example displays when vehicle S falls to local maximum, it will change to recovery mode and use the righthand rule to forward the packet to the intersection.
3.1 PROPOSED METHODOLOGY Initialize adhoc Network
initiate sender (S)
Step 6 : When failure is encountered, use re-routing to continue to transmit the packet, else go to step2. 4. ALGORITHM Input : IC , Infrastructure Connectivity Output: V2V, if a vehicle communicates via V2V V2I, if a vehicle communicates via V2I while IC=0 do A vehicle is connected via V2V, V2V end else if IC=1 then optimal path detection, V2I or V2V end end if a vehicle communicates with an RSU via V2I then the RSU tracks the destination position, if destination vehicle is inside the actual RSUs coverage then Direct link from RSU to B else The actual RSU will forward the message to next RSU. end end end
Find shortest path using improved path re-routing based hybrid ACO-PCO based routing algorithm
Evaluate delay & power
Figure 5 .Illustration of communication in transport Cloud computing 5. EXPERIMENTAL RESULTS The comparison based on the parameters between existing and proposed results are
is any failure ?
Table 1: Comparison Table Existing Result Proposed Result Protocol Used IEEE 802.11n IEEE 802.11p Transmission Range 100 m 250m Data Rate - Sending 2 mbps 11mbps Safety Message Size 1 Mb 512 Bytes Feed Back 64 kb 14 bytes Message Size 10 Bytes Confirm Message Size 50kb Number of Vehicles on 100 600 Road Bit Rate 4Mbps 18 Mbps Parameters
Apply re-routing to transmit packet
Step 1: The initialization of Ad-hoc network circle along with its parameter values. Step 2: Initialization of Sender(s) and multicasting of sender data to its defined nodes. Step 3: Calculation of the Distance Matrix. Step 4: Application of Emergency Messaging based routing technique improved way routing. Step 5: Evaluation of power consumption and end-to-end delay
5.1 COMPARISON CHART
6. CONCLUSION This paper focused, route redirection during road accidents effective communication can improve the protection, capacity, and convenience of machine systems whereas at identical time lowering ancient barriers to adoption, like infrastructure price and quality. This type of researcher to handle the application desires legion comprehensively, whereas taking under consideration realistic operational environments. 
REFERENCES Mikhail Buinevich, Konstantin Izrailov, Ekaterina Stolyarova, Andrei Vladyko, “Combine Method of Forecasting VANET Cyber security for Application of High Priority Way”, International Conference onAdvanced Communications Technology, ICACT2018 February 14, 2018 H. Hartenstein, and L. P. Laberteaux, “A Tutorial Survey on Vehicular Ad Hoc Networks,” IEEE Communications Magazine, vol. 46, iss.6, pp. 164171, 2008. J. Jakubiak, and Y. Koucheryavy, “State of the Art and Research Challenges for VANETs,” in Proc. 5th IEEE Consumer Communications and Networking Conference, pp. 912-916, 2008. F. J. Martinez, C. K. Toh, J. C, Cano, C, T. Calafate, and P. Manzoni, “A Survey and Comparative Study of Simulators for Vehicular Ad Hoc Networks (VANETs),” Wire. Commun.Mob. Compute.vol. 11, iss.7, pp. 813-828, 2011. A. S. K. Pathan, Security of self-organizing networks: MANET, WSN, WMN, VANET. Boston, USA: Auerbach Publications, 2010.
DIGITAL EVIDENCE COLLECTION FRAMEWORK FOR INTERNET OF THINGS N.Kala1
Centre for Cyber Forensics and Information Security University of Madras, Chennai, Tamil Nadu, India E-mail ID:email@example.com
Student, MSc, Cyber Forensics and Information Security, Centre for Cyber Forensics and Information Security University of Madras, Chennai, Tamil Nadu, India E-mail ID:firstname.lastname@example.org Abstract- Today is the era of Internet of Things (IoT) where overwhelming entities with embedded computing functionalities with interoperability and communication ability are interlinked to provide a convenient service to the owner. It makes human life more convenient and dynamic. As with every industrial revolution emerges a new type of crime and associated challenges, IoT also raises issues on security and creates opportunities for cybercriminals to attack these areas, resulting in a direct impact on users. Several challenges are posed before the forensic investigator due to the complexity of IoT technology. Hence there is a dire need for digital forensic framework in IoT to tackle these challenges. This paper focuses on a methodology of collecting evidence from IoT devices and concerned networks. Keywords: IoT, Digital Forensics, Evidence Collection.
INTRODUCTION In the past several years technology became an inevitable part of human life. The dependency on cyberspace (the Internet) has been increasingly growing and pervading every of life. This means there is higher demand and usage of electronic devices. Life transformed when these devices became capable of inter-operating and communicating through internet which facilitated and speeded up daily activities. This led the scientific community to further develop electronic devices to cope with activity transformation. The last two decades represents an instance of the aforementioned facts where all these technological breakthroughs such as smartphones, sensors and tablets came in an assorted manner to formulate the Internet of Things concept. The positive impact of this advancement is evident in almost every discipline of human life and one cannot imagine abandoning or working without digital devices for asingle day. However, there is a dark side of using digital technology which comes in form of security breaches and vulnerabilities. These breaches may vary in types and ramifications and are extremely serious and may cause a massive loss if not addressed. The advent of IoT increased the concern as it creates opportunities for cybercriminals to attack these areas, resulting in a direct
impact on users. Several numbers of challenges are posed before the forensic investigator investigating an IoT crime due to the complexity of IoT technology. Hence there is a dire need for digital forensic framework in IoT to tackle these challenges. PROBLEM STATEMENT A complex technology such as Internet of things poses several challenges in front of forensic investigator in collecting the evidences from an Internet of Things environment. It is hard to find where the IoT data exists, from where it comes from and how to scope down to potential evidence. 1. OBJECTIVES OF THE STUDY i. To identify the IoT devices that is available. ii. To identify the technology behind implementation of IoT devices. iii. To study the emerging cybercrimes that is occurring in IoT environment. iv. To study the issues and challenges in collecting the evidence from IoT devices for digital forensics investigation. v. To identify the existing methods of collection of evidence from IoT devices. vi. To propose a framework for digital forensics in the IoT environment. 2. RESEARCH QUESTIONS Based on the above objectives following research questions were raised: 1. What all are the IoT devices that are available in market? 2. What kind of technology is being implemented in IoT environment? 3. What are the emerging crimes targeting IoT environment? 4. What issues and challenges are faced by forensic investigators while collecting the evidence from IoT devices? 5. What are the existing methods for collection of evidence from IoT devices? 6. How to cope up with these challenges by framing a Digital Forensics framework customized for IoT environment?
3. INTERNET OF THINGS The Internet of Things is a novel paradigm shift in Information Technology arena. The phrase “Internet of Things” which is alsowell-known as IoT is coined from the two words “Internet” and “Things”. The Internet is a global system of interconnected computer networks that use the standard Internetprotocol suite (TCP/IP) to serve billions of users worldwide. It is a network of networks that consists of millionsof private, public, academic, business, and government networks, of local to global scope, that are linked by abroad array of electronic, wireless and optical networking technologies. While the Things can be any object or person which can be distinguishable by the real world. Objects include not only electronic devices we encounter and use daily and technologically advanced products such as equipment and gadgets, but “things” that we do normally think of as electronic at all such as furniture, equipment’s, materials etc. Things can be both living things like person, animals, plants or Non-living things such as home appliances or industry apparatus. So at this point, things are real objects in this physical or material world. IoT can be defined as an open and comprehensive network of intelligent objects that have the capacity to autoorganize, share information, data and resources, reacting and acting in face of situations and changes in the environment. It is maturing and continues to be the latest, most hyped concept in the Information Technology world. Over the last decade the term Internet of Things (IoT) has attracted attention by projecting the vision of a global infrastructure of networked physical objects, enabling anytime, anyplace connectivity for anything and anyone. The Internet of Things can also be considered as a global network which allows the communication between human-to-human, human-to-things and things-to-things, which is anything in the world by providing unique identity to each and every object. IoT describes a world where just about anything can be connected and communicates in an intelligent fashion than ever before. Most of us think about “being connected” in terms of electronic devices such as servers, computers, tablets, telephones and smart phones. But in the Internet of Things, sensors and actuators embedded in physical objects from roadways to pacemakers are linked through wired and wireless networks, often using the same Internet IP that connects the Internet. These networks churn out huge volumes of data that flow to computers for analysis. When objects can both sense the environment and communicate, they become tools for understanding complexity and responding to it swiftly. What’s revolutionary in all this is that these physical information systems are now beginning to be deployed, and some of them even work largely without human intervention. The “Internet of Things” refers to the coding and networking of everyday objects and things to render them individually machine-readable and traceable on the Internet.
4. IoT key features The most important features of IoT include artificial intelligence, connectivity, sensors, active engagement, and use of small devices. A brief review of these features is given below: Artificial Intelligence: IoT essentially makes virtually anything “smart” meaning it enhances every aspect of life with the power of data collection, artificial intelligence algorithms, and networks. This can mean something as simple as enhancing refrigerator and cabinets to detect when milk and favourite cereal run low, and to then place an order with preferred grocer. Connectivity: New enabling technologies for networking, and specifically IoT networking, mean networks are no longer exclusively tied to major providers. Networks can exist on a much smaller and cheaper scale while still being practical. IoT creates these small networks between its system devices. Sensors: IoT loses its distinction without sensors. They act as defining instruments which transform IoT from a standard passive network of devices into an active system capable of real-world integration. Active Engagement: Much of today's interaction with connected technology happens through passive engagement. IoT introduces a new paradigm for active content, product, or service engagement. Small Devices: Devices have become smaller, cheaper, and more powerful over time. IoT exploits purpose built small devices to deliver its precision, scalability, and versatility. A. Harnessing the advantages from IoT The advantages of IoT span across every area of lifestyle and business. Some of the advantages that IoT has to offer are: Improved Customer Engagement: Current analytics suffer from blind-spots and significant flaws in accuracy; and as noted, engagement remains passive. IoT completely transforms this to achieve richer and more effective engagement with audiences. Technology Optimization: The same technologies and data which improve the customer experience also improve device use, and aid in more potent improvements to technology. IoT unlocks a world of critical functional and field data. Reduced Waste: IoT makes areas of improvement clear. Current analytics give us superficial insight, but IoT provides real-world information leading to more effective management of resources. Enhanced Data Collection: Modern data collection suffers from its limitations and its design for passive use. IoT breaks it out of those spaces, and places it exactly where humans really want to go to analyse our world. It allows an accurate picture of everything.
B. Significant set of challenges in IoT Though IoT delivers an impressive set of benefits, it also presents a significant set of challenges. Some of the major issues are: Security: IoT creates an ecosystem of constantly connected devices communicating over networks. The system offers little control despite any security measures. This leaves users exposed to various kinds of attackers. Privacy: The sophistication of IoT provides substantial personal data in extreme detail without the user's active participation. Complexity: IoT systems are complicated in terms of design, deployment, and maintenance given their use of multiple technologies and a large set of new enabling technologies. Flexibility: Flexibility of an IoT system to integrate easily with another is a concern. Compliance: IoT, like any other technology in the realm of business, must comply with regulations. Its complexity makes the issue of compliance seem incredibly challenging when many consider standard software compliance a battle. C. The four-stage architecture of an IoT system: Stage 1 of IoT architecture consists of networked things, typically wireless sensors and actuators. Stage 2 includes sensor data aggregation systems and analog-to-digital data conversion. In Stage 3, edge IT systems perform preprocessing of the data before it moves on to the data center or cloud. Finally, in Stage 4, the data is analyzed, managed, and stored on traditional back end data center systems. Clearly, the sensor/actuator state is the province of operations technology (OT) professionals. So is Stage 2. Stages 3 and 4 are typically controlled by IT, although the location of edge IT processing may be at a remote site or nearer to the data center.
Figure 1: Stages for IoT architecture D. IoT Sensors, Wearables and devices The hardware utilized in IoT systems includes devices for a remote dashboard, devices for control, servers, a routing or bridge device, and sensors. These devices manage key tasks and functions such as system activation, action specifications, security, communication, and detection to support-specific goals and actions.
E. IoT Sensors The most important hardware in IoT might be its sensors. These devices consist of energy modules, power management modules, RF modules, and sensing modules. RF modules manage communications through their signal processing, Wi-Fi, ZigBee, Bluetooth, radio transceiver duplexer, and BAW. The sensing module manages sensing through assorted active and passive measurement devices. Given below is a list of some of the measurement devices used in IoT. Table 1: Various types of sensors Sensing Devices Accelerometers
Gas RFID Sensors
Micro Flow Sensors
F. Smart wearable devices Head :Helmets,glasses Neck :Jewellery,collars Arm :Watches,wristbands,rings Torso :Clothing,backpacks Feet : Socks, shoes IoT Devices Smart cities: Sensors monitoring vibration of building, bridges etc. Sensors monitoring trash condition Transportation: Sensors for monitoring parking space Sensors for monitoring traffic conditions Smart car Home automation: Smart TV Smart refrigerator Smart oven Smart meter for monitoring energy usage EHealth: Smart caregiver wearables heart monitor Pain relief wearable Smart chair Smart scale Smart environment: Sensor for monitoring forest fire Sensor for monitoring air condition Sensor for monitoring earthquake zone
Smart water: Sensor for monitoring rivers for chemical leakage Sensor for monitoring pipes for water leakage Sensor for monitoring river for flood Retail: Sensor, RFID for storage conditions Sensor, RFID for controlling rotation of products Industrial control: Sensor for controlling temperature during manufacturing Smart agriculture: Sensor for controlling amount of sugar in grapes Sensor for controlling conditions in greenhouses Sensor for tracking locations of animals After reviewing the above literatures and studying the IoT technology, it was learned that there are lot of issue and challenges in IoT forensics so there is a dire need in proposing a framework for evidence collection in IoT environment. So the next chapter deals with IoT evidence collection framework. 5. IoT EVIDENCE COLLECTION FRAMEWORK The Internet of Things (IoT) has facilitated the creation of numerous inter-connected smart gadgets like toasters, refrigerators, thermostats, locks, washing machines, car, garage doors, motion detectors etc. that connect to online services and platform. These devices being always connected produces new types of cyber physical evidentiary data. The acquisition of forensically relevant data and its analysis pose a challenge as the devices don't have a common interface, internal storage or standard protocol. Nevertheless, this shift towards inter-connected devices also bring new avenues for digital evidence like pinpointing the exact date and time a door was opened or locked, the temperature change, or when a car was parked, which in turn could help find digital evidence of forensic value in a potential case. In this project an IoT evidence collection framework is proposed to facilitate forensic collection evidences from multiple IoT devices. A. Overview of the framework This framework for IoT evidence collection undertakes two approaches that is preparing the IoT environment for Pre-Investigative readiness and preparing the IoT environment for real time smart forensics. In PreInvestigative readiness preparation three approaches are suggested. Zone-based method for approaching IoT related investigations Establishing a central evidence collection point Integrating the IoT environment details into the Building Information Model In preparation of IoT environment for real time forensics an automated forensics system is suggested which provides Forensics as a Service (FaaS) to a certain degree until it is necessary to involve external experts. This
framework integrates all these approaches and feeds the data from one system to another in an effort to scope down the collection of potential evidence in aIoT environment. B. Framework Core IoT evidence collection framework which is proposed to facilitate effective forensic collection of evidences from IoT environment consisting of multiple IoT devices revolves around two approaches that forms the core of the entire evidence collection process. They are: Preparing the IoT environment for Pre-Investigative readiness Preparing the IoT environment for real time smart forensics C. Preparing the IoT environment for PreInvestigative readiness The Pre-investigative readiness is very essential step in ensuring the preparedness before any incident and also to enable the effective investigation. It involves preparation of entire infrastructure in such a manner as to ease the efforts of identifying and collecting the evidence for forensic investigation. Thus in actual sense it’s a process of scoping down the essentialities which will help investigator to collect evidence in a faster and efficient manner. This research suggests and focuses on three key concepts that needs to be essentially be set up in an earlier stage prior to incident in order to achieve Pre-Investigative readiness. These concepts are: 1. Zone-based method for approaching IoT related investigations 2. Establishing a central evidence collection point 3. Integrating the IoT environment details into the Building Information Model Though several researches have been done on these key concepts earlier, this approaches where visualized individually. This project presents a unified approach that combines all these key concepts and interlinks them to demonstrate the flow of information from one concept to another thus helping in scoping down the potential evidence for investigative purpose. The information from this phase will cater as input for a smart real time forensics in the later stage. Pre-Investigative readiness addresses the following questions: What the investigator as to identify? How the investigator will identify the potential evidence/devices? What the investigator as to collect? Where the investigator will find potential evidence/device? How the investigator will collect the potential evidence/devices?
D. Zone-based method for approaching IoT related investigations Digital Forensics in IoT will involve knowing where the investigator has to look for evidence. Without any formal way for evidence collection valuable time will be wasted looking in the wrong places for irrelevant evidence. This framework proposes a zone based method for approaching IoT related investigations. Zone 1: This is the internal zone where all hardware, software and networks (e.g. Bluetooth and Wi-Fi) exits. This zone comprises of devices such as mobile devices, smart wearable gadgets, gaming consoles, smart fridge, thermostat & heat controllers, locks, doors, camera, motion detectors, smart lightings, RFID tags, embedded systems and other house retrofits. In this zone the devices that relates to a crime scene is catalogued and a decision is made about what is relevant to the case and what may hold evidence that will be useful to the case. These devices are identified using tag identifications (tag ID) and their state i.e. asleep, awake, active/transmitting, ON, OFF etc. FIGURE 2: IOT EVIDENCE COLLECTION FRAMEWORK
FIGURE 3: ZONES (1-2-3)- DIGITAL FORENSICS
TABLE 2: ZONE 1 DETAILS Zone
Zone 1 (Internal devices)
Mobile devices, Smart wearable gadgets, Gaming consoles, Smart fridge, Thermostat & heat controllers, Locks, Doors, Camera, Motion detectors, Smart lightings, RFID tags, Embedded systems, House retrofits.
Denial of service, Tampering of output, Distribution of malicious content, Data theft etc.
Sensor data (IP address, sensor ID)
State change data, Logs, etc.
Zone 2: All devices and software that are at the edge of the network and that provide a communication medium between the internal and external networks comes in Zone 2. This zone consists of all public-facing devices of the networks in question such as Intrusion Detection System, Intrusion Prevention System, Network Firewall, Routers, Access points
and Authentication systems. Forensics investigations will typically involve identifying these elements, cataloguing them and retrieving any available relevant evidence from them. Table 3: Zone 2 Details Zone
Zone 2 (Edge devices)
Intrusion Detection System, Intrusion Prevention System, Network Firewall, Routers, Access points, Authentication systems etc.
Denial of service, Unauthorized intrusions, Data modifications etc.
Network logs, intrusion traces etc.
Zone 3: This zone covers all hardware and software that is outside of the network. This zone includes evidence from all cloud, social network, Internet Service Provider (ISP), mobile network providerâ€™s data, internet and web based services and other external party networks (such as neighboursIoT network, hospital network etc.). Table 4: Zone 3 Details Zone Zone 3 (External network)
Cloud, Social network, Internet Service Provider (ISP), Mobile network providerâ€™s data, Internet and web based services and other external party networks (such as neighbours IoT network, hospital network etc.).
Privacy issues, Data theft, Impersonation, Fraud
User activity data and logs
It is at the discretion of digital forensic investigator to apply this approach. Investigation can be done in parallel (all zones at same time) or zone of greatest priority can be identified based on the evidence gathered from the Central logging system. This approach reduces the complexity that will be encountered in IoT environments and ensures that investigators can focus on clearly identified areas and objects in preparation for investigations. E. Establishing a Central Evidence Collection Point This framework consists of a central evidence collection point known as Central logging system that collects data from the three different zones. Data such as state change (e.g. Door closed or opened) is a vital clue for the investigation. This evidence collection point collects data from the sensors, controllers and internal devices, edge network device such as firewall and user activities and state change logs form cloud. A controller can be used as a Central logging system. IoT controllers provide a centralized mechanism by which multiple IoT devices can connect to, be
managed and controlled. Through a controller, developers and engineers can support a variety of hardware and communication platforms. These controllers can typically acquire and modify the state of an IoT device. A Central logging system should have the same functionality as an IoT controller but with forensics in mind.
Figure 4: Sensors data to central logging system Sensors data to Central logging system: This is just like how a sensor gets connected to a controller. An example would be like an IP camera and an application residing on a computer within the same network used to control it. The camera registers a state change when it detects motion. This state change can be reported back to the central logging system where further actions can be taken based on the identified state change.
Figure 5: Controller data to central logging system Controllers to Central logging system: Controllers connect to internet to allow control of connected devices through web interfaces. Thus it is proposed to input its data to the central logging system. By accessing the controller, the state change logs of multiple devices can be acquired. This is a lucrative data collection point. As the number IoT devices increase their will be a dire need for such a centralized control. It provides a consolidated state for collecting state data of multiple devices.
Figure 6: Edge Network Device data to Central Logging System Edge devices to Central logging system: The Intrusion Prevention System, Intrusion Detection System, Network Firewalls sends their logs, details of malicious traffic detected, their state information to the central logging system. This information is vital for the system to analyse for abnormalities.
Figure 7: Cloud Data to Central Logging system External network (Cloud) to Central logging system: Today IoT devices use cloud services as point of control and data collection. This provides a possibility of obtaining user activity details and change logs from cloud data by leveraging APIs which are used to manage IoT devices over internet. Thus by constructing individual scripts central logging system will receive its communication through calls to the cloud. F. Characteristics of a Central logging system: Forensic soundness: A Central logging system can only acquire state data from IoT devices and is not allowed to change the state of an IoT device. Date & time logging: A Central logging system should be capable of accurately logging the dates and times of state changes. Secure storage and integrity: A Central logging system should embody secure storage of the collected IoT state data, and should also hash the collected states at the time of collection for later validation. Time Synchronization: As in the real-time approach for IoT forensic, the clock of the IoT devices, data storages, and detection mechanism must be timely synchronize. Therefore, these components must be able to meet the timing requirement, for instance, the deadline, period time and jitter. In the IoT context, the process usually ties with the deadlines and limited resources. And sometimes they need to run continuously for long periods of time without maintenance. Memory and Storage Requirement: Real-time computing requires having enough memory and storage capacity to accommodate the excessive processing and memory requirements and timing characteristics. In this approach, since the IoT devices have limitation in components, all the possible evidence is collected and stored in the central logging system. Communication Requirement: Strong and stable communication among component is vital to ensure that all the potential evidence can be extract and store in a timely manner. G.Integrating the IoT and the Building Information Modelling “Building Information Modelling (BIM) is a digital representation of physical and functional characteristics of a facility. A BIM is a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life-cycle; defined as existing from earliest conception to demolition” (NBIMS-US, 2016). BIM is
the process spanning the generation and management of the physical and functional information of a project. The output of the process is what we refer to as BIMs or building information models which are ultimately digital files that describe every aspect of the project and support decisionmaking throughout a project cycle. BIM and the subsets of BIM systems and similar technologies feature are more than just 3D (width, height, and depth) but may include further dimensions such as 4D (time), 5D (cost), and even 6D (asbuilt operation) (Smith, 2014).
Figure 8: BIM Benefits This framework suggests the integration of IoT environment details with the Building Information Modelling. By combining the information about the IoT capabilities of a building or structure, it may be possible to answer the questions of; where has the information come from? Where is the information stored? It is also crucial to identify in what format the data is stored or encoded. This would narrow the scope of the investigation, and enable the selection of features or data that identifies an individual user from a much smaller data set. A composite picture of the data gathered about an individual user could be constructed from the data stored or forwarded by the buildings they have inhabited. Advantage to the investigation process: BIMs presents the entire network architecture of IoT devices present in the building. The different technology used and their interconnection. It will also detail the various controls implemented for achieving the pre investigative preparedness. All the documentation reading IoT implementation will be integrated with details about device specifications, locations, vendor details. BIMs will help the investigator to study the entirety of the environment (building), the various IoT network implemented, its corresponding details thus coping down to potential locations for evidence collection.
H. Preparing the IoT environment for real time smart forensics This framework suggests a real-time approach for IoT forensics. Real-time in this context is referred as an automatic investigation on the IoT device. This approach is undertaken to accommodate the issue of handling the diversity of devices and to deal with various IoT constraints. The detection mechanism is deployed in this context will trigger the forensic phase if there are any abnormal activities detected on the IoT devices. Once detected the system start its process and takes necessary actions. The framework takes into cognizance the nature of the IoT and provides Forensics as a Service (FaaS) to a certain degree until it is necessary to involve external experts. The Real-time approach is essential as speed of response is crucial in IoT. User must be enabled to keep personal IoT under forensics surveillance by the use of adaptable and commercially forensics solutions.
Figure 12: Real time smart forensics The steps involved in real time smart forensics is clearly illustrated in Figure 12. This framework takes into cognisance the nature of the IoT and provides Forensics as a Service (FaaS) to a certain degree until it is necessary to involve external experts. i. The central Logging System collects and monitors the data (State change, alert logs) form the different zones of IoT environment. ii. Since large amount of data is transferred every moment, a continuous analysis is required to find abnormal activities. If no such activities detected, then the data is stored till retention period. If there is false alert, a report is generated and sent to user/owner. iii. On identification of an abnormal activity, an automated analysis is carried out and the severity of the incident is determined. iv. Based on the analysis, incident that doesn’t require expertise is handled by the system itself by initiating recover. Report is generated and sent to user/owner. v. If expertise is needed to handle that particular incident, then alert is generated and the law enforcement agency or other relevant agencies are informed. A report is also sent to user/owner.
Advantages of Preparing the IoT environment for real time smart forensics: Adaptable and easy to use/manage by end users themselves in the event of a security incident. Easy as changing light bulbs for home owners and as deploying their own forensics centres for large businesses. Can be integrated into a forensics system which can be deployed in homes and similar environments to carry out basic forensics (and security) functions including capturing and sifting through relevant data and producing evidence that is understandable and useful to home owners and law enforcement. Framework Workflow
Figure 13: Classifying into Zones and integration with Building Information Modelling The first phase proposed by this framework is classification of IoT environment into three zones. This will help investigator in knowing where to look for the evidence. Without any formal way for evidence collection valuable time will be wasted looking in the wrong places for irrelevant evidence. IoT environment is divided into three zones: Zone 1: This zone consists of internal devices such as mobile devices, smart wearable gadgets, gaming consoles, smart fridge, thermostat & heat controllers, locks, doors, camera, motion detectors, smart lightings, RFID tags, embedded systems, house retrofits. Zone 2: This zone consists of network edge devices such as intrusion detection system, intrusion prevention system, network firewall, routers, access points, authentication systems etc. Zone 3: This zone consists of external network such as cloud, social network, internet service provider (ISP), mobile network provider’s data, internet and web based services and other external party networks (such as neighbours IoT network, hospital network etc.). All these details are integrated into the Building Information Modeling. By combining the information about the IoT capabilities of a building or structure, it may be possible to
answer the questions of; where has the information come from? Where is the information stored? It is also crucial to identify in what format the data is stored or encoded. This would narrow the scope of the investigation, and enable the selection of features or data that identifies an individual user from a much smaller data set. By combining the information about a composite picture of the data gathered about an individual user could be constructed from the data stored or forwarded by the buildings they have inhabited. BIMs presents the entire network architecture of IoT devices present in the building. The different technology used and their interconnection. It will also detail the various controls implemented for achieving the pre investigative preparedness. All the documentation reading IoT implementation will be integrated with details about device specifications, locations, vendor details. It will help the investigator to study the entirety of the environment (building), the various IoT network implemented, its corresponding details thus scoping down to potential locations for evidence collection.
Figure 14: Feeding data into Central Logging System In the next phase a Central Logging System is proposed which will record the dataâ€™s (State change logs and alerts) from the different zones for analysis. E.g. When zone 1 devices register a state change it is reported back to the central logging system where further actions can be taken based on the identified state change. In same way data is fed from controllers and network edge devices like firewall. It shall also use cloud services as point of control and data collection. This provides a possibility of obtaining user activity details and change logs from cloud data by leveraging APIs which are used to manage IoT devices over internet.
Figure 15: Initiating automated analysis and reporting This framework takes into cognisance the nature of the IoT and provides Forensics as a Service (FaaS) to a certain degree until it is necessary to involve external experts. The central Logging System collects and monitors the data (State change, alert logs) form the different zones of IoT environment. Since large amount of data is transferred every moment, a continuous analysis is required to find abnormal activities. If no such activities detected, then the data is stored till retention period. If there is false alert, a report is generated and sent to user/owner. On identification of an abnormal activity, an automated analysis is carried out and the severity of the incident is determined. Based on the analysis, incident that doesnâ€™t require expertise is handled by the system itself by initiating recover. Report is generated and sent to user/owner. If expertise is needed to handle that particular incident, then alert is generated and the law enforcement agency or other relevant agencies are informed. A report is also sent to user/owner. This sets the investigation process.
Figure 16: Initiating automated analysis and reporting In the last phase investigator interrogates the relevant parties, then studies the BIMs for better understanding of the environment. This gives investigator insight into not only the IoT environment but also the entire physical, functional aspects of the environment such as building and also an insight into the life of person interacting with that environment. With this information in hand investigator analyse and collects evidence from the central logging system. Since the alert was initiated with a report from the same system, investigator can straight way scope down to objects of forensic interest. Investigator will then analyse the remaining data to gather potential relevant evidence. If a situation arises in need of great degree of information, investigator will
conduct investigation into the individual zones and collect evidence form IoT devices by image acquisition or memory forensics. At last evidences will be gathered, event will be reconstructed, examined and the investigator will present the report to court. J. LIMITATION OF THIS FRAMEWORK The forensic framework proposed here has not been implemented, deployed and tested. It was assumed that implementation of the model will be scalable for growing number of devices. This framework is through the view point of a smart building only. The framework does not address the issue of connectivity to different IoT devices. There is a lack of standardization due to different wireless communication technologies, operating software and interfaces. So it is necessary to have hardware that supports these technologies. The challenge of examination of individual IoT devices is outside the ambit of the proposed framework. The framework does not address how exactly a malicious incident can be contained from spreading across the IoT network and the investigators decision regarding the state of devices (ON/OFF). 6. CONCLUSION Is this era of Internet of Things (IoT) where overwhelming entities with embedded computing functionalities with interoperability and communication ability are interlinked to provide a convenient service to the owner. It makes human life more convenient and dynamic. As with every industrial revolution emerges a new type of crime and associated challenges, IoT also raises issues on security and creates opportunities for cybercriminals to attack these areas, resulting in a direct impact on users. Several number of challenges are posed before the forensic investigator due to the complexity of IoT technology. Hence there is a dire need for digital forensic framework in IoT to tackle these challenges. So this project focused on a methodology of collecting evidence from IoT devices and concerned networks. In view of this, a study was done on various IoT devices available in market. It was learned that all these devices are basically combination of sensors, actuators embedded into devices with processing power. Learning the technology behind implementation of IoT framework was an objective. It was learned that Radio Frequency Identification (RFID), Wireless Fidelity (Wi-Fi), ZigBee, Near Filed Communication (NFC), Artificial Intelligence (AI), Sensor technology etc. forms the backbone of IoT network. IoT architecture models were discussed in detail and the concept is implemented in the framework.
Analysis was done on various emerging crime in IoT environment. It looked into the possibility of crime in various stages of IoT life cycle such as manufacturing, installation and operations. A reference is made to United Kingdom’s government publication educating the citizen about various crime that can take place in individual’s space and business environment utilizing IoT technology. This research focused on various issues and challenges in collecting the evidence from IoT devices for digital forensics investigation. It was learned that enormous amount data flow poses a challenge to investigator in scoping down to potential evidence. The lack of standardization of IoT technology, lack of tools to interface with the IoT devices, issue of trans-border data flow are some of the challenges. Present day forensic framework developed by NIST and McKemmish were studied and it was identified that these existing framework could not cater well to the growing needs of IoT forensics which is again a driving force for the need to have a forensic framework customized for IoT environment. Hence to address these challenges this project suggests an evidence collection framework for IoT environment. The proposed framework is based on two approaches that is preparing the IoT environment for PreInvestigative readiness and preparing the IoT environment for real time based forensic method for approaching IoT related investigations. To achieve Pre-Investigative readiness, the framework suggests implementation of Zone-based method for approaching IoT related investigations where the IoT environment is divided into three zones based on the internal devices, edge network devices and external environment. This approach helps in scoping down the potential evidence for forensic investigator. Another suggestion is to establish a central evidence collection point which will collect state change logs and other alerts from these IoT devices and analyse the data for any possible abnormality. In case of any abnormality a smart forensic process is initiated to restore the issue or alert the relevant parties based on criticality of incident. This approach saves lot of time for forensic investigator and supports quick response initiation in IoT environment. Lastly the framework suggests the integration of IoT environment details with the Building Information modeling process. These BIMs serves as a vital information repository for the forensic investigator who is absolutely new to the IoT crime scene. By studying the BIMs, the investigator updates his/her knowledge about the facility, its functional, operational components, the life of the occupants and the various IoT devices that are established in that environment. Lastly, the proposed framework aims at facilitating easy and less cumbersome method for collecting evidence in IoT environment and provides Forensics as a Service (FaaS) to a certain degree until it is necessary to involve external experts.
3. 4. 5.
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Published on Mar 31, 2019