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Sabotage and fortification:
The intriguing domain of cyber security “A rise in the threat of cyber attacks has increased the demand of cybersecurity professionals. Read on to learn more about the intriguing field of cybersecurity. Also meet Bytebandits, the team from India which secured the 8th position in CSAW CTF 2020.” Written by DANYAL SHAHID SHAMSI Designed by NIDAMANURI CHANIKYA GUPTA
The worm that mushroomed
n November 2nd, 1988, a worm released by the Massachusetts Institute Of Technology (MIT) to probe and find vulnerabilities in the systems of that time, went berserk because of an error in the code. What followed was a cyber apocalypse that left around 6000 UNIX machines connected to the NSFNet (National Science Foundation Network) infected with multiple copies of the virus, estimated costs of the damage ran over $100,000, and internet was left partitioned for several days as the regional networks disconnected from the NSFNet to prevent the spread of, and to clean the infection. Robert Tappan Morris, a graduate student at Cornell university, who later went on to become a tenured professor at MIT and wrote the code, was sentenced to three years of probation, 400 hours of community service, and a fine of $13,326. The worm, named ‚Morris worm‘, was designed to exploit the vulnerabilities present in the system and the dangers of using weak passwords. Before infecting the system, the worm checked if it had already infected it. To prevent instances where the system administrators countered it by sending a ‘False positive’ report to present infection, Morris thought of infecting the system 14% of the time regardless of the status of infection on the device. This subsequently led to the worm getting copied on a device multiple times, rendering the device unusable.
This incident would set the stage for presentday DoS attacks.
Why care about cyber security? With systems becoming increasingly dependent and inextricably linked to the internet, concerns about privacy and security of these systems have increased. Let us look into some common cyber-attacks that are prevalent in the present times. Cyber attacks can be broadly bifurcated into two categories, one that occurs on web applications, the other which compromises a computer connected to a network.
Web based cyber attacks :
As the name suggests, they occur on websites or on web applications. A panoply of attacks fall under this classification. Under injection attacks, a person can inject a code into a web application, which either alters the way in which it functions, or fetches sensitive data. There can be SQL injections, code injections, log injections and XML injections under this category. Under phishing, an attacker masquerades as a credible entity, and obtains sensitive and confidential information. Under DoS (Denial of service) attacks, an attacker floods a target with requests, thus crashing the server. This makes web resources on the server inaccessible to the users. Under ‘Man in the middle attacks’, a person intercepts the connection between a client and a server,
and is thus able to read, delete or modify the information being sent over the intercepted network.
System based attacks : Under this category, the attacks compromise a device connected to a network. Viruses are pieces of code that can self-replicate on uninfected computers, while executing instructions that harm a device. Worms are similar, and are primarily meant to replicate themselves on uninfected devices connected to a network. As the case of Morris worm indicated, cyberattacks and cyber crimes can cause massive damage. In fact, estimated losses because of cyber crimes in 2021 amount to 6 trillion USD, an amount that would rank third after the US and China, if it were a nation’s economy. Cybersecurity Ventures estimates that the losses caused by cybercrimes would reach 10.5 trillion USD by 2025. Cybercrime losses are expected to cost more than the losses incurred during natural disasters. These figures are based on historical trends indicating annual growth of cybercrime figures, which includes attacks sponsored by hostile states as well as malicious organisations. Cyber attacks can lead to loss and deletion of sensitive data, including personal data of several users. Using ransomwares and denial of service attacks, criminals can hold institutions such as hospitals, industries and businesses hostage. Cyber embezzlement and frauds are becoming increasingly common each day contributing to further losses. Cyber attacks can jeopardise a business, by leading to an increase in system downtime, thus reducing productivity. This also means additional costs for the restoration of systems, as well as the reputation of the organisation. With organised cybercrime rearing its head, it is becoming increasingly difficult to prosecute the offenders. In fact, in the US, the likelihood of prosecution of the saboteurs is less than 0.05%, according to the World Economic Forum’s 2020 global risk report.
Ransomwares are malicious pieces of code that restrict a user’s access to crucial resources on their system, often threatening the deletion of aforesaid resources, unless a ransom is paid. Estimates point to an exponential increase in ransomware casualties over the years. For instance, while the losses attributed to ransomware were $325 million in 2015, they rose over to $11.5 billion by 2019, and are expected to overshoot $20 billion in 2021. Just a few months back, ransomware attacks claimed their first life. In an ostensible ransomware attack on a hospital in Dusseldorf, a woman who needed immediate attention couldn’t be admitted because of the failure of IT systems, and had to be rerouted to another hospital. According to another estimate by Cybersecurity ventures, the world would store astronomical quantities of data (around 200 zettabytes) by 2025. This data would be stored on public and private IT infrastructures, public and private cloud services, IOT devices, and several user devices across the world. While it presents immense opportunity of transforming and influencing every profession, it also holds within an increased susceptibility to sabotage and cyber attacks. This explains the dire need for businesses, as well as institutions such as healthcare, banking, airports, and governments to beef up their cyber defences.
Cybersecurity competitions, what are they? Capture the flag(CTF) is a traditional outdoor sport involving two teams, with delineated territories and flags, where the objective of the game is for players to obtain the flag of the contending team and bring it to their territory. Well, your usual cybersecurity competitions are similar, just the flag in this case is a snippet of code, a software, or a piece of hardware, the contending team mustn’t have access to. The team is supposed to employ their expertise in cybersecurity concepts, and gain access to the flag. CTF competitions are organised in the following three formats:
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In this format, teams are supposed to defend their ‘flag’ against the contending team, finding and fixing vulnerabilities within a stipulated time before the contending team strikes. The contending team tries to find the same vulnerabilities, but for attacking the ‘flag’ in this case.
The Jeopardy style requires the team to complete a sequence of tasks in a specified order. For completing a task, the teams are provided with questions which reveal clues that help them in solving the tasks at hand. The tougher the task that you solve, the higher are the points you get. It includes tasks typically involving the following four categories, Binary exploitation, Reverse engineering, Web exploitation and Cryptography. More on that soon.
As the name suggests, Mixed style competitions involve both attack-defence and jeopardy formats. It can have an attack-defence competition having a couple of jeopardy tasks as bonus, or the other way around.
Getting started Participating in CTFs often requires you to have a certain skill set, as is the case for all competitions. You have to know stuff like Cryptography, Binary Exploitation, Steganography, Reverse Engineering, and Web exploitation. While the names might sound highfalutin and daunting, all one needs is a little programming experience, and interest in problem solving to get started. picoCTF is a CTF competition aimed at middle and high school students. They have a primer which helps you get started with the concepts. The rest is simply a matter of practice. The more problems you solve, the more you grow in your experience. Let’s now probe a little more deeply into the problem categories:
Cryptography is the process of ‘encoding’ a stream of information into an ‘apparently’ meaningless stream of information, which can be ‘decoded’ to
obtain the required information, provided the key is known. Encryption is what makes our banking transactions and online communications secure. In a cryptography problem, you might be provided with a stream of apparently meaningless text, say something like, “003c27262a3b2a6f206f1c27262a3b2a6f3620 636f242021206f3c2726243a22266f3820” You might be provided with some hint within the problem, if you are attempting a beginner level CTF. For instance, you might be provided with the information that the above string is hex coded, and encrypted with a key using a XOR cipher (Which means every byte in the above hex string was obtained by performing the XOR operation over a byte from a string of the original message and the key). You would be required to find the flag, which in this case would be the text that was encrypted to obtain the above text. For advanced level CTFs, competitors require tools such as Cyberchef, Hash Extender, FeatherDuster, XORTool and several others.
Steganography, like Cryptography, is a technique of hiding information. But instead of encoding the information directly, it is hidden within an apparently innocuous file. For instance, a piece of text might be hidden within other files such as images or videos. In addition, one might encrypt the bit of text as well.Steganography problems might require one to use tools such as StegCracker and Steganography online.
Binaries are machine codes that can run on a system. Under this category, you are provided with Linux ELF files (Executable and Linkable format, which is a standard format for executable files, object codes, linkable files and core dumps) or windows executable files. One is required to find the vulnerabilities and bugs within a program and modify its contents.
One can send in a certain ‘input’to the program, which makes it behave in a way other than it was originally intended to. Such inputs are called ‘payloads’. formatStringExploiter (a python module), DLL injector, libformatstr are a few of the tools used by competitors in advanced level CTFs.
Participants in advanced CTFs often use tools such as BurpSuite, Commix, Hackbar (available as a chrome extension), SQLMap to help themselves with this problem category.
Let‘s look into some of the popular CTFs conducted around the world.
Reverse engineering, as we know it, is a process of deconstructing machines and circuits to obtain information about how the system was designed and perhaps, functions. In the context of CTFs, Reverse engineering problems require one to do something very similar. A compiled program, which is understandable by the computer is called a machine code. Of course, a human being can’t glean out much from the machine code itself. The idea is to decompile the machine code to obtain a more humancomprehensible format, whereupon a human being can probe deeper into the vulnerabilities in the program. Ltrace, IDA, Ghidra, and Binary Ninja are some of the tools used by participants in advanced level CTFs.
Web exploitation: Web exploitation requires the participants to figure out vulnerabilities and bugs in websites. The basic architecture of any web resource involves a client (for instance a browser) that requests for the resource from a server (where the resources are stored). Websites are often integrated with relational databases, which makes it easier to store and modify the data displayed over it. Oftentimes, such an architecture, if lacking in proper safeguards, ends up being susceptible to attacks such as SQL injection, which involves adding additional ‘payloads’ to a SQL query thus making the database return information that wasn’t intended to be returned. Other common vulnerabilities are command injection, directory traversal, cross site request forgery, cross site scripting, and server side request forgery. Participants try to discover and exploit these vulnerabilities.
The big names
DEF CON is one of the world’s largest hacker conventions, held annually in Las Vegas, Nevada. The attendees include students, cyber security professionals, hackers, and federal government employees. The event includes talks about cybersecurity, and several cybersecurity challenges and competitions, of which CTFs are quite popular. The qualifier round for the CTFs involves a jeopardy style competition. The qualifiers make their way to the finals to compete in an attackdefence format competition.
It is a global ethical hacking competition, where teams from around the world take part in a 12 hour long elimination round. Two teams from each continent qualify for the finals. The challenge involves categories like digital forensics and network exploitation.
CSAW CTF: CSAW is the most comprehensive student-run cybersecurity event boasting of being hosted across 6 global regions, and over 6000 annual competitors. The event receives participants from USACanada, Israel, India, Middle East and North Africa (MENA), Mexico and Europe. The event includes several workshops, talks, and cybersecurity competitions, of which CSAWCTF is quite popular.
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The international Capture the Flag competition is organised by UC Santa Barbara. It is a traditional attack-defence style CTF competition. It is the world’s largest and longest-running educational hacking competition. The competing teams are provided with identical copies of a virtual host containing some services. The teams use their knowledge of cybersecurity concepts to find the vulnerabilities within their systems, and fix it. At the same time, they can attempt to compromise other teams’ systems with the knowledge of the vulnerability they uncovered. The aim is to maintain the services on the virtual host for the entire duration of the event.
Panoply: Panoply is quite similar in format
to the iCTF, where contending teams defend the available resources from each other. The main difference is that the resources aren’t made available to all the teams, rather they have to compete for their control. It’s a timed event, in which teams have common resources made available to scan, assess, and penetrate. Once a team captures a service, they can plant their ‘flag’ which is used by a scanner to grant them the ownership of the service. Following it, they have to secure the resources, or else have the ownership taken away by rival teams. As long as a team maintains ownership of a service, while defending it against attacks by contending teams, they continue to gain points. More resources are made available during the competition for teams to penetrate and take control of. The team with the highest score wins the event.
In India CTFs are pretty popular in India too. Several cybersecurity enthusiasts, beginners and professionals alike, participate in these competitions to assess their skills. CTFs provide them with a safe and legal environment to test and improve their skill set. For several participants, CTFs can open a panoply of career opportunities, if they are recognised. Amrita University organises
CTFs for university and high school students (InCTF and InCTF junior respectively). It is one of the oldest and prestigious CTFs organised in India. Nullcon’s HackIM and EMC defender’s league are a few other popular CTFs organised in India. Several institutes are involved in serious research in this field. The Interdisciplinary centre for cybersecurity and cyber defence of critical infrastructures, abbreviated as C3I, at IIT Kanpur, was the first research centre set-up in India for education, research and training in developing safeguards for critical infrastructures. The centre is building India’s first cybersecurity test bed for critical infrastructure, along the lines of Idaho national labs, Sandia national labs, and NIST in the US. Having partnerships with institutes in Israel and the US, the centre indulges in exchange of technology and research collaborations, as well as organises workshops, conferences and cyber security competitions to spread awareness about cyber threats, and to develop interest among students in the field.
ByteBandits The origin of this team, and the inception of the culture of CTFs at IIT Indore began with a guy in the 2013 batch, Sudhakar. He was immensely interested in CTFs, but since there wasn’t any established ‚cybersecurity wing‘ within the Programming club, he used to participate in the events with his friends from other colleges. Kunal and Bhor, from the 2015 batch were the earliest members of the team, apart from Sudhakar. Presently, the team consists of four members, Vishnu, Mrigank, Vaibhav and Sarthak. Currently, a large section of the members of the Programming club are interested in competitive programming, and cybersecurity and CTFs remain interests followed by a small esoteric group. The lukewarm interest in CTFs, the members believe, is because the students haven’t yet tried their hand at CTFs.
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According to them, CTFs might appear more daunting to several people, because of a comparatively more vague roadmap than CP, and a tonne of concepts to master. They believe the best way to approach CTF is to try one’s hand at the problems, and learn stuff along the way. The ByteBandits organise several workshops, and conduct their own CTF (ByteBandits CTF) for teaching cybersecurity concepts, and have an active discord server available for dispelling doubts. The team has participated in several CTFs, and achieved the feat of securing the first position in CSAW CTF among all Indian teams for two consecutive years, and the 8th position in CSAW CTF 2020.
Looking ahead: A career in cybersecurity With an ever-increasing dependence of devices and systems on the internet, especially with the advent of IOT, cybersecurity has become, and would continue to be, pertinent in the decades to come. An increase in the ‘surface web’ (The portion of the world wide web accessible to the general public) means an increase in susceptibility to cybercrimes. NASSCOM predicted an increase in the demand for cybersecurity professionals, but reported that India lacked sufficiently skilled cybersecurity professionals to meet the demand. Clearly, professionals experienced in cybersecurity are going to remain relevant, and possibly, increase in importance. Here are some of the highest paying jobs one might secure in the field.
Network security engineer:
Being responsible for overlooking the security of the systems present within any institution, a network security engineer’s position is crucial in an organisation. He tracks down the vulnerabilities, and ensures that they are resolved. They oversee the maintenance of firewalls, routers, switches, network monitoring tools, and VPNs.
Cyber security analyst: A cyber security analysis is concerned with planning and implementation of security measures for an organisation. They do periodic internal and external vulnerability testing, risk assessment, and security fortifications. They also train the employees and inform them about secure practices while using the internet to avoid security breach. Security architect: They work with the programming team while designing the network and computer security architecture for an organisation. They are involved with planning, researching and designing security elements. They also delineate the company policies that guide the internet usage, and the punitive action to be taken against employees for infringements. Chief information security officer (CISO): The CISO has come to be an
ubiquitous position in the management team of around 80% of organisations, according to an estimate by PWC. They are responsible for seeing the planning and implementation of the cyber security plan, and ensuring that it is in accordance with the technologies used by the business’ visions and operations. They work with a dedicated staff to deploy security processes for an organisation, and ensure that there is either none or limited downtime in the event of a security breach and sabotage. Thus cybersecurity is going to remain a fecund field, and can be a good career option for students interested in problem solving. CTF competitions are an excellent way to learn cybersecurity concepts and polish learned skills.
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Novel AI based framework speeds up
Nanophotonics To enable faster and efficient study of the interaction of metasurfaces with light, IIT Bombay researchers developed AI algorithms that can predict the optical response given surface design and vice versa with successful rapid validation. Let us see how they did it and get a deeper understanding behind their powerful numerical method based on deep learning. Written by SHIVPRASAD KATHANE and HARSH RAJ Designed by SHALMALI SRIRAM
aterials found in nature often aren’t directly utilised in their existing forms for engineering purposes. Instead, scientists find ways to modify them to make the best use of their unique properties. For instance, in communication technology, the size, shape and orientation of materials is altered to change their interaction with electromagnetic waves of different wavelengths. Today, it is possible to change the properties of light using nano-sized metasurfaces instead of optical lenses. Metasurfaces are actually thin layers of twodimensional metamaterials (artificially crafted materials) that allow or inhibit the propagation of electromagnetic waves in desired directions. Optical metasurfaces can locally impart changes to phase, amplitude, and polarization of propagating waves. A significant advancement in nanophotonics was achieved by researchers at IIT Bombay as they developed a novel AI based numerical method to study the interaction of metasurfaces with light.
Link to research paper: https://www.nature.com/articles/s41598-020-76400-y
How did the research come about?
by human intuition, and it is incredibly hard to develop the intuition in accurately solving the equations,” says Prof Kumar.
This interdisciplinary research was a result of a collaboration between researchers of Physics department Prof. Abhishek Mall & Prof. Anshuman Kumar and Electrical department Prof. Abhijeet Patil & Prof. Amit Sethi at the Indian Institute of Technology Bombay, Mumbai, India. Prof. Mall initially came up with an idea for this research and discussed its implementation with his colleges. He generated the training dataset and then all of them participated in the data analysis, writing, and reading of the research paper. Prof. Sethi and Prof. Kumar managed the project and finally their study was published in the journal Scientific Reports by Springer Nature. The project was funded by the Department of Science and Technology, Government of India, and the Industrial Research and Consultancy Centre, IIT Bombay. The researchers were interviewed and an article highlighting their research was written by Mr. Debdutta Paul and published under R&D dissemination initiative of IRCC.
Developing the AI algorithms
Background Work The team of researchers created a metasurface consisting of a layer of silicon sandwiched between two layers of gold. Four different designs were created by modifying the geometric shapes of the topmost gold layer, which is a few nanometres thick. Researchers noticed that as metamaterials interact with the incoming beam of light, it gets amplified. In practical purposes, “the amplification makes it much easier to detect small changes in the light shone on the surface,” says Mr. Mall. It was of interest to study how the metasurfaces affect the ‘polarization’ of light. The researchers used a computer to virtually shine a light with certain polarization and aimed to generate light with the opposite polarization. To serve this purpose, ideally, “one needs to solve the Maxwell’s equations for input to each metasurface until we get the desired output,” Mr. Mall continued. However, solving them consumes a lot of time as well as computing power. “Moreover, the design process is limited
In order to overcome the issues associated with the existing process, the team decided to create a method to study the interaction between the light and the metasurfaces without the need to solve complex Maxwell’s equations. The first step for the team was to develop an algorithm that predicts the geometrical design of the topmost gold layer in the metasurface necessary to generate the projected output beam. The algorithm is designed such that it produces geometrical designs based on optical response and gradually improves its design by comparing it with the actual design. For the algorithm to work well, it needed to predict results as we get after solving the Maxwell’s equations. So, the team solved the Maxwell’s equations for the required geometries and successfully verified that algorithm appropriately predicted the optical response. But, every time verifying the results from the algorithm by solving Maxwell’s equations was not a good idea. In order to avoid this, the team created another algorithm to mimic the solution of Maxwell’s equations itself. They just needed to feed the geometry of the nano-sized gold layer to the algorithm to get the corresponding optical response. “This technique is a form of Artificial Intelligence. Such AI based technologies are everywhere today from our shopping lists, video recommendations, online advertising, assisted medical diagnosis, self-driving cars, and search engines,” says Prof Kumar revealing that these algorithms are based on AI while acknowledging the critical role AI is playing in modern society. The above two algorithms were combined to carry out the entire process of identifying the correct geometrical pattern for the required response and validating it against the Maxwell’s equations. Unbelievingly, this complete process
takes only about 11 milliseconds to predict the correct geometric configuration! One more advantage of this algorithm is that the user can define any thickness as needed. “Currently, we are implementing our algorithm to work with more stringent requirements that the user would like to specify. We also want these metasurfaces to be very light and thin, to achieve the best of both worlds,” Prof. Kumar signs off.
Understanding in-depth The conventional approach for designing and optimizing the nanophotonic metasurface as per the target electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error which is computationally costly and time consuming. This problem is also challenging because the structural designs aren’t unique and their relationship with the corresponding electromagnetic responses is non-linear. The researchers have modelled this ‘unintuitive’ relationship as a probability distribution in the design space by introducing a framework based on cyclical deep learning (DL).
Now DL is a data-driven method. It can learn highly non-linear functions mapping the inputs to the outputs in a training dataset by using deep artificial neural networks (NNs) with layer architectures which allow training using convex optimization technique despite their depth. With a sufficient amount of training data and regularization techniques, the learned representation from DL can generalize to unseen datasets. However, earlier methods using fully connected (FC) NNs and convolutional neural networks (CNNs) either encounter the limitation of optimizing a single candidate design or the requirement of a large dataset for training. Other NN architectures address the inverse design of nanophotonic metasurface as a regression problem, mapping optical response to structural design space. This approach though, forces the network to converge to one of the several solutions and so, no new designs are generated. Thus, more flexible DL methods were desired.
The team proposed cyclical generation and discovery of metasurfaces for user-defined optical response, with no structural design restrictions. Their framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization to generate optimized structural designs while simultaneously performing forward and inverse design tasks.
For the training and evaluation, the researchers generated a dataset of 1500 Aluminum (Al)nanoantennae samples with four classes of structural design ofdifferent dimensions. To predict the optical response (forward design) they used Simulation Neural Network (SNN) because an efficiently trained SNN acts as an efficient numerical electromagnetic simulation solver that can predict an optical response in a few milliseconds on even a general purpose laptop. It takes 2D cross-sectional structural design image as input and predicts its optical response.
SNN model architecture composed of CNNs and FC-NNs Source: nature.com https://www.nature.com/articles/s41598-020-76400-y
A conditional generative adversarial network (cGAN) is used to model the probabilistic distribution of the design space (inverse design). This is because, upon training, it not only generates new structural designs but also ensures that these have optical response with features similar to the EM simulated dataset samples. The cGAN model architecture
consists of two networks: a generator (G) and a discriminator (D). G accepts optical response and a random noise distribution z to generate structural design. D evaluates real structural designs from generated structural designs as real or fake. G consists of five transposed convolutional layers, while D consists of five convolutional and two FC layers.
cGAN model architecture consisting of G & D networks Source: nature.com https://www.nature.com/articles/s41598-020-76400-y
The pseudo genetic algorithm (pGA) is applied to the optical responses to sort designs by minimal variation between their predicted and desired optical responses through selection and evaluation process. This along with the models together forms a pseudo generation framework: It takes as input a batch of userdefined optical responses and generates good-
quality structural designs with authentic optical response using cGAN and SNN respectively, and is characterized into desired response and design space via pGA. In each cycle, the EM simulation and new generated data samples are used as an updated dataset for training.
Cyclic DL Framework Source: nature.com https://www.nature.com/articles/s41598-020-76400-y
For the evaluation of trained DL models, they measured the accuracy of the predicted optical response by SNN and authenticity of optical response of generated designs through cGAN, using two metrics: MSE and cosine similarity. They found that their optimized framework generates structural designs with 0.021 mean square error and 0.968 cosine similarity, while also discovering new metasurface designs. Indeed, the prediction of optical responses
using SNN took only a few milliseconds per sample. The results demonstrate that the design of metasurfaces by this technique is rapid and accurate. Their framework is a powerful tool to reduce the cost of computation and optimize nanophotonic design efficiency while exploring new designs. This can have significant importance in speeding up several technological applications in optics.
Finance during the pandemic: Understanding some basic instruments Written by PRATIM MAJUMDAR Designed by SHARVARI SRIRAM
inancial markets have taken a tumultuous turn since the invasion of COVID-19, with major world economies going haywire and any algorithm to predict the volatility and returns of any financial instruments being rendered moot, since no algorithm could predict the current downturn the markets have taken. Common investors, who used to park some portion of their savings in financial instruments in hope of appreciable returns have their prerogatives shifted to exit their equities positions and instead go for passive instruments such as bonds or Exchange Traded Funds (ETFs). Some astute investors have also shifted to investing in future contracts and options, while others have prioritised tackling this volatile situation by investing in insurance. Yet again, some have shifted to investing in global financial markets as their last resort. Any novice in the investors’ battlefield may either not be aware of the aforementioned technicalities or may not have any prior experience of investing in these instruments. So let’s embark on our journey to get a fair idea of what these instruments are and how can they prevent you from plunging into risk during and after this pandemic?
Future contracts and Options
A fixed income instrument wherein you invest a lump sum into a certain project and are entitled to fixed periodic payments. You are regarded as a creditor or debt holder of that particular project, and your stream of income remains fixed irrespective of the status and the revenue generation of the project. On very basic terms, it is very similar to you taking a loan from the bank. In that case, the bank is the creditor who has invested a lump sum on you, and is entitled to periodic payments as mentioned in the loan’s terms and conditions. Since bonds (and other debt instruments) offer a minimal risk, they are generally among the lowest bracket in terms of returns.
Future contracts or “futures” in short is a drawn-out legal agreement between two parties regarding the purchase/sale of some particular underlying commodity in the future at a predetermined price, as mentioned in the contract. The parties involved in the contract are obliged to see the contract through, or they may be subjected to legal actions. An option, on the other hand, is a financial instrument similar to a future contract minus the element of obligation. In case of an option, it entirely depends upon you whether to exercise the option or not. Let’s quote a simple example. Imagine you are a textile merchant. You have drawn out a futures contract with a customer that you will deliver 75 Kgs of cotton one year later at a price of INR 30 per Kg. This contract was drawn by you in order to hedge yourself against the volatility you observed in the price of cotton. Now, you are obliged to deliver the aforementioned quantity of Cotton at the aforementioned price, regardless of the movement in the price of cotton. If the spot price (current price) of cotton after 1 year is INR 40, you have suffered a loss, while if the spot price after 1 year is INR 20, you have gained immensely. By now, you may have already achieved an idea of how the equations would have changed if you instead had bought an option. You now are not obliged to deliver the quantity of cotton. Instead, you can closely track the prices of cotton, and reach a decision on your own accord whether you should exercise the option or instead sell it in the open market.
Exchange Traded Funds (ETFs) An Exchange-Traded Fund is a pool of various debt and equity securities, which closely tracks an underlying index or a commodity. It is christened “Exchange-Traded”, because it trades on a common stock exchange just like everyday stocks, and is subject to intraday shocks similar to stocks. ETFs probably are the most sureshot mechanism to diversify your portfolio and thus minimize your risk. The portfolio of an ETF contains varied financial securities for various risk classes, and offer much lower risk than general stocks do.
Insurance As the name suggests, insurance is a mechanism to shield you in any high-pressure risk situation. In order to exercise any particular type of insurance, you need to buy out an insurance policy and pay periodic premiums, so that in case of any insecure circumstances in the future, you may be covered under the insurance policy accordingly. Different types of policies exist to insure you against different types of situations which include life insurance, motor insurance, health insurance etc.
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In the next article in this series, we try to venture into the macroeconomic aspects of the pandemic, and later decipher how these instruments can be employed to maximize your
returns with optimum risk while the pandemic persists, while also further traversing to how you can exploit offshore securities and global financial markets. Till then, Au Revoir !!!
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World’s first SDS Biosensor How a team from IIT Roorkee was successful in developing the world’s first ‘specific bacterial biosensor’ for detection of environmental detergent pollutant — SDS (Sodium Dodecyl Sulphate)
Written by MOHIT SHARMA Designed by SHALMALI SRIRAM
iosensors are novel engineered machines used to measure biomarkers analytically. These markers are a combination of biological receptor compounds (antibody, enzyme, nucleic acid, etc.) that are directed during specific biological events (e.g., antibodyantigen interaction). They allow the detection of a broad spectrum of analytes in complex sample matrices. They have also shown great promise in areas such as clinical diagnostics, food analysis, bioprocess, and environmental monitoring.
SDS (Sodium Dodecyl Sulphate) is one such synthetic organic compound that generates biomarkers. It is extensively used in soaps, toothpaste, creams, shampoos, laundry detergents in households, agricultural operations, laboratories, and industries. But it’s notoriously famous for its toxic effects when disposed of in waterways and deteriorating drinking water quality. Therefore it becomes a top priority to create a biosensor that could precisely detect SDS in different samples.
Prof. Naveen K. Navani, Department of Biotechnology, IIT Roorkee, and Sourik Dey, a postgraduate student at the Department of Biotechnology MSc program at IIT Roorkee, acknowledged the threat associated with SDS and decided to work on creating the world’s first-ever state-ofthe-art biosensor to measure it. Along with their group-members, Shahnawaz Ahmad Baba, Ankita Bhatt, and Rajat Dhyani, the team successfully created this novel biosensor. But their journey wasn’t easy from the start. Before we dive into their research work, let’s understand how to measure a biomarker and the problems associated with it. Biomarkers are infamous for their extreme tolerance to being tracked by sensory devices. Let me give you a taste of today’s biosensor technology. When an average person sweats, it generates approximately 800 unique biomarkers that can give us feedback on things like muscular exertion, fatigue, hydration level, or electrolytic levels. Yet our modern-day smartwatch bio-sensors can only do one of two things — They can either measure steps or heart rate. These devices are literally stuck in the Stone Age because they don’t have any chemical sensing capabilities.
The pipeline of a biomarker being converted into a data signal Source:TEDxSanDiego
voltages or currents that can be read out on a scoring device. This device could be later imprinted on a mass scale for detecting biomarkers in a wide variety of applications. Bio-engineers have been working continuously to find techniques to detect SDS. Unlike conventional methods, SDS can easily be confused with closely related detergents like SDBS (Sodium Dodecylbenzenesulfonate). The IIT Roorkee team came with the idea of using Pseudomonas Aeruginosa PAO1 strain as the framework (chassis) to construct a whole-cell biosensor for detection. The system involves a highly specific regulator and a fluorescent protein produced only when SDS is presenting the sample. The system can even detect 0.1 ppm of SDS in aquatic samples! This biosensor is highly specific for SDS and has minimal interference from other detergents, metals, and inorganic ions present in the environment. To get a deeper insight into this unique approach, we asked a series of questions with the team.
Vital Signs conversion to Biomarkers through Biosensor Source:TEDxSanDiego
The modern advent of biosensor technology works on the principle that surfaces are selective to biomarkers. And through the innovations in Electrochemistry, we can translate these markers into either
Fluorescence activation of Pseudomonads on contact with SDS Strains Source: IIT Roorkee
1. How does the Biosensor work and accurately able to predict SDS?
4. What are the applications of your biosensor?
Pseudomonads have an inherent capability to be used as an optimal destination framework for synthetic biology applications. The selected species of Pseudomonas can be engineered to detect various chemicals owing to their resilient nature to survive and adapt to harsh environmental conditions. This research highlights the development of the world’s first whole-cell bacterial biosensor for the direct, specific, and efficient detection of SDS without involving sample preparation steps, toxic chemicals, sophisticated polymers, and sensor development steps. — Sourik Dey, Final year MSc student at IIT Roorkee.
Due to the specificity of this Biosensor, it can show accurate and precise analysis of sewage water plant treatment. This biosensor’s genetic modification to produce green fluorescence signals gives it an edge over other conventional biosensors. It can be easily installed at various inlet and outlet pipelines of sewage plants to determine SDS concentration. Moreover, the biosensor can be easily grown and distributed so that people can test SDS concentration in water samples from different samples. — Sourik Dey
2. What makes your biosensor different from other conventional approaches? SDS, if untreated, can harm the marine biodiversity and cause pollution of land and water-bodies. The highlight of this biosensor is its sensitivity to even minute quantities of SDS in the environment and its ability to distinguish between SDS and SDBS. — Prof Naveen Kumar Navani, Department of Biotechnology, IIT Roorkee. 3. What makes SDS so dangerous? The constant deterioration of the quality of drinking water and the harm caused to marine life are some of the major concerns. SDS has harmful effects on the survival and breeding of organisms in the aquatic ecosystem. It hampers their biological processes, such as solubilization of phosphate, reduction of ammonia, nitrogen fixation, and photosynthesis. It can cause dermal and ocular irritation, cardiac anomaly, hemolysis, tachycardia, kidney failure, and even death. SDS can also disrupt biological wastewater treatment processes and cause problems in sewage aeration and treatment facilities owing to its high foaming capacity. — Prof Naveen Kumar Navani.
5. What role can biosensors play in the future of diagnostics? Bio-sensors have the capability to detect various kinds of chemical receptivity and give real-time data analysis. Early-stage lung cancer, one of the most common and aggressive cancers, kills around 1.4 million people worldwide every year, so the pursuit of new techniques to detect it accurately remains a global challenge. Now, a highly sensitive graphene biosensor has shown potential in electronic nose devices, which analyze the components of vapor mixtures such as the breath. Artificial intelligence could soon be used to predict the spread of melanoma by using microscopic cameras to analyze the appearance and behavior of cells. Robotic sensor technology that can be used to measure hormones quickly and cheaply could pave the way for the diagnosis of reproductive health issues in real-time. The future holds various possibilities for the application of biosensors. — Prof Naveen Kumar Navani
Conclusion Biosensors represent the technological counterpart of living senses. These technologies have already reached a highly advanced level and need minor improvement at most. They have the potential to substitute antibodies in biomarkers and biochips for the measurement of low-molecular-weight substances, proteins, viruses, and living cells. The dream of the “100-dollar” personal genome may come true in the next few years, provided that the technological hurdles of nanopore technology or of polymerase-based singlemolecule sequencing can be overcome.
In the future, individual therapy will include genetic profiling of isoenzymes and polymorphic forms of drugmetabolizing enzymes. For decentralized online patient control or the integration into every day “consumables” such as drinking water, foods, hygienic articles, clothing, or for control of air conditioners in buildings and cars and swimming pools, a new generation of “autonomous” biosensors will emerge.
Source: unsplash.com 32
Automated Lung Ultrasound for Covid-19 Screening The automated lung ultrasound tool is a viable option for the screening and monitoring of Covid-19 patients. It presents noteworthy improvements over the usual options i.e, CT scans and X-rays, making it possibly the best choice. The LUS tool’s scope isn’t restricted to just Covid-19 screening, it has the potential to detect other lung infections.
Edited by VARUN GANTA Designed by AVANTI HARGUDE
About the principal investigator:
r. Mahesh Raveendranatha Panicker received his Ph.D. degree from the School of Computer Engineering, Nanyang Technological University, Singapore in 2009. Dr. Mahesh is a senior member in the IEEE and is a Six Sigma Green Belt certified TRIZ practitioner. While working with GE and Samsung, Dr. Mahesh worked on a range of projects, which include a portable foetal ECG platform, a compressor blade health monitoring program, fMRI based neuro analytics. His research interests include signal processing and analytics, reconfigurable low power circuits and systems, and machine/deep learning for imaging.
Dr. Mahesh Raveendranatha Panicker
Reasoning Behind the Project: It
has been reported that abnormalities in the lungs due to Covid-19 may be developed before clinical manifestations and nucleic acid detection, and due to this, it’s recommended to screen suspected patients with a diagnostic i m a g i n g technique. The SARSCov-2 infection is highly contagious. The risk of transporting patients with low levels of oxygen in their blood along with blood circulation problems makes chest CT scans less viable. Lung ultrasound gives similar results as that of a chest CT scan (while being better than X-rays) with the added benefits of being easier to use at the point of care, real-time dynamic imaging, repeatability, and the absence of ionizing radiation, all while being available at a lower cost. The promise shown by the results from Italy on lung ultrasound served as a subsidiary motive for the development of this project.
Amidst t h e pandemic, almost e v e r y research and development institute is looking to contribute and benefit the world. IIT Palakkad in collaboration with a few others did exactly that in the months April-June. IIT Palakkad had developed an automated lung ultrasound(LUS) for Covid-19 screening and monitoring, through a cloudbased image analysis and scoring system. The tool, which is the first of its kind in India and maybe even the world, was made available for use by clinicians to obtain an automated analysis after uploading an ultrasound video and is being constantly updated to get more accurate analyses.
The team, led by Dr. Mahesh R. Panicker started their investigation which would eventually lead them to develop the automated LUS tool. Lung ultrasound images provided by the director of the Ultrasound Division at Hospital Universitario, La Paz were used to investigate. The director, Dr. Yale Tung Chen, who had tested positive for Covid-19 shared LUS images of himself as the disease progressed. The data processing tool categorized the condition of various parts of his lungs into three: normal, viral, and bacterial infected. The conclusion is reached by first processing the images and then letting neural networks do their thing.
the required data is provided, the tool could be extended to recognize other lung infections such as pulmonary oedema, pulmonary embolism, pneumothorax, chronic obstructive pulmonary disease and asthma. The tool also enables clinicians to study the severity of the infection. Normal lungs are depicted by A-lines, slightly infectious lungs by A+B lines, infectious lungs by B-lines or B-patches, and seriously infected lungs by consolidations. The values pertaining to the data processing tool’s results will always change based on the method of acquiring ultrasound images and the expertise of clinicians. So, it is very well understood that the tool is meant to aid a clinician in making his/her judgement, and not replace him/her. Currently, the classification of infection type has an approximate accuracy, sensitivity and specificity of 96%, 95% and 97% respectively, whereas the severity classification has accuracy, sensitivity and specificity of 97 %, 92% and 98% respectively. Currently, steps are being taken to extend the tool beyond Covid-19 detection, to act as a reliable comprehensive lung ultrasound image analysis tool during emergencies.
Functioning of the Tool: Working with Dr. Mahesh was Professor Vinod A. Prasad, and he put into words the working of the tool. The LUS analysis tool could be used by a nurse, in the absence of a skilled clinician by following a couple of steps: i) acquiring the lung images and ii) transferring the images to the cloud. These images are then analysed over the cloud and scored based on the type of infection and the severity of the infection. Professor Vinod specifically mentioned that a tool like this will be very valuable when there’s a time constraint. Manually evaluating many LUS images would take quite a bit of time, but when there are multiple lives at stake, using an automated tool that is very accurate is the way to go. If