International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025 www.irjet.net p-ISSN: 2395-0072
A Survey on Cyber Attack Detection Techniques in Cloud Environment
Madhu
Nagaraj1 , Ananya Desai2 , Bhoomika V3 , Likhithashree S M4 , Maheshwari H S5
1Assistant Professor, CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India
2UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India
3UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India
4UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India
5UG Student , CSE(Data Science) ,ATME College of Engineering ,Mysuru 570028,India
Abstract - Theincreasingadoptionofcloudcomputingin critical domains such as healthcare, education, and governance has heightened the need for robust cybersecurity frameworks. Despite the widespread implementationofconventionalsecuritymechanismslike firewalls, antivirus software, and intrusion detection systems(IDS),cloudinfrastructuresremainsusceptibleto ever-evolvingcyberthreats.Thisreviewpaperfocuseson the application of deep learning techniques to enhance the detection and prevention of cyber-attacks in cloud computing environments, exploring the rationale for integrating intelligent models that can adapt to complex andlarge-scalenetworkdata,offeringimprovedaccuracy and faster detection of anomalies and malware. This review addresses key research questions related to the effectiveness of deep learning models in intrusion detection, anomaly identification, and malware classification.Themajorstudiesexaminedincludemodels that employ ensemble learning, hybrid optimization algorithms, and predictive analytics to handle intrusion detection tasks in cloud and IoT environments. These models, while promising in terms of performance, often face limitations such as interpretability challenges, computational overhead, anddifficultyin handlingzeroday attacks. The conclusions drawn highlight that deep learningmodels,whenproperlyoptimizedandintegrated with advanced feature selection and anomaly detection techniques,cansignificantlyenhancethesecurityposture of cloud systems.
1.INTRODUCTION
Cloud computing has revolutionized the way data and computing resources are accessed and managed, offering scalable,flexible,andcost-efficientsolutionstobusinesses and individuals. As the reliance on cloud infrastructure grows,sodoestheexposuretopotentialcyberthreats.Cloud environments,bynature,areaccessibleviatheinternetand thus susceptible to a wide array of malicious activities, including data breaches, denial of service attacks, and malware intrusions. These threats compromise the confidentiality, integrity, and availability of cloud-based systemsandservices,makingcybersecurityatoppriority. Traditionalsecuritymechanismsareincreasinglyinadequate in addressing sophisticated and rapidly evolving attack
patterns.Asaresult,thefocushasshiftedtowardsintelligent solutionsthatcanlearn,adapt,andrespondtonewthreats inreal-time.Deeplearning,asubsetofmachinelearning,has emerged as a powerful tool for intrusion detection and anomaly recognition due to its ability to process large datasetsanduncoverhiddenpatternsinnetworktraffic.The AI-powered cyberattack detection project aims to utilize machine learning and artificial intelligence (AI), data analyticstechniquestopredictandoptimizethreatdetection patterns.Byanalyzinghistoricalattackdata,networktraffic patterns,andotherrelevantfactors,theprojectwillprovide accurate,reliable,andreal-timedetectionofcyberthreats.
2.OVERVIEW
Inresponsetoescalatingcyberthreatsandthelimitationsof traditionalsecuritymeasures,thisprojectfocusesondeep learning-powered cyberattack detection to enhance cloud securityandresilience.Cloudcomputingenvironmentsare vital to modern digital infrastructure, sup porting critical operations across multiple industries. By leveraging advancedmachinelearningtechniquessuchasConvolutional NeuralNetworks(CNNs),LongShortTermMemory(LSTM) networks, and ensemble models, these systems analyze networktrafficpatterns,userbehaviors,andsystemlogsto provideaccurate,real-timethreatdetectioncapabilities.The integration of deep learning in cybersecurity offers transformative potential for threat identification, enabling proactivedefensemechanismsthatcanadapttonewattack vectors.TheprojectexploresvariousAIapplicationsincloud security,includinganomalydetection,malwareclassification, andintrusionprevention,offeringenhancedprotectionfor dynamiccloudenvironments.
3. LITERATURE REVIEW
Accordingto[1],Ahmed,M.S.,Al-Badi,A.H.,andGastli,A. present"DeepLearning-BasedIntrusionDetectionforCloud Computing Environments" in IEEE Transactions on Cloud Computing. This comprehensive study analyzes the implementationofdeeplearningarchitecturesspecifically designedforcloud-basedintrusiondetectionsystems.The researchemphasizestheuniquechallengesposedbycloud environments,includingthedynamicnatureofvirtualized resourcesandthecomplexityofmulti-tenantarchitectures.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025 www.irjet.net p-ISSN: 2395-0072
The authors demonstrate how convolutional neural networkscaneffectivelyprocessnetworktrafficpatternsto identify sophisticated attack vectors that traditional signature-based systems might miss. Their experimental resultsshowsignificantimprovementsindetectionaccuracy whilemaintainingacceptablefalsepositiverates,makingthe approach viable for real-world cloud deployments where securityalertsmustbeactionableandprecise.Accordingto [2], Chen, L., Wang, Y., and Liu, X. explore "Ensemble Learning Approaches for Cyber Attack Detection in IoT Networks" in the Journal of Network and Computer Applications.Thisresearchaddressesthegrowingsecurity challenges in Internet of Things ecosystems, where traditional security measures are often inadequate due to resource constraints and the heterogeneous nature of connected devices. The study proposes a novel ensemble learningframeworkthatcombinesmultipleweaklearners to create a robust detection system capable of identifying variousattacktypesacrossdifferentIoTdevicecategories. Theexperimentalvalidationdemonstratestheframework's effective ness in detecting both known and previously unseenattackpatterns,contributingsignificantlytothefield ofIoTcybersecurity.Accordingto[3],Rodriguez,P.,Kim,S., and Thompson, J. investigate "Golden Jackal Optimization AlgorithmforDeepLearning-BasedIntrusionDetection"in ExpertSystemswithApplications.Thisresearchintroduces a bio-inspired optimization algorithm that enhances the performanceofdeeplearningmodelsusedincybersecurity applications. The Golden Jackal Optimization Algorithm mimicsthehuntingbehaviorofgoldenjackalstooptimize neural network parameters. The study demonstrates how this optimization approach can significantly improve the convergence speed and accuracy of deep learning models usedforintrusiondetection.Theexperimentalresultsshow superiorperformancecomparedtotraditionaloptimization methods, particularly in scenarios involving imbalanced datasets commonly encountered in cybersecurity applications.Accordingto[4],Patel,R.,Singh,A.,andKumar, N.present"ComparativeAnalysisofDeepLearningModels for DDoS Attack Detection" in Computer Networks. This comprehensive study evaluates various deep learning architecturesfortheireffectivenessindetectingDistributed DenialofServiceattacks,whichrepresentoneofthemost. Theresearchprovidesdetailedperformancecomparisonsof CNN, LSTM, and hybrid models, analyzing their strengths andweaknessesindifferentattackscenarios.Thefindings offervaluableinsightsforsecuritypractitionersinselecting appropriate deep learning models based on specific organizational requirements and threat landscapes. According to [5], Johnson, M., Davis, K., and Wilson, R. explore"Privacy-PreservingDeepLearningFrameworkfor Cloud Security" in IEEE Transactions on Information Forensics and Security. This study addresses the critical challengeofmaintainingdataprivacywhileimplementing effective security measures in cloud environments. The researchproposesinnovativetechniquesfortrainingdeep
learning models without compromising sensitive organizational data. The framework utilizes federated learning and differential privacy techniques to enable collaborativethreatdetectionacrossmultipleorganizations without exposingproprietaryinformation. Thisapproach represents a significant advancement in addressing the privacyconcernsthatoftenhindertheadoptionofAI-based securitysolutionsinsensitiveindustries.Accordingto[6], Zhang, H., Li, C., and Wu, D. investigate "Entropy-Based NetworkTrafficAnalysisforRealTimeThreatDetection"in Computers&Security.Thisresearchfocusesondeveloping efficientalgorithmsforrealtimeanalysisofnetworktraffic patterns using entropy based metrics. The study demonstrates how information theory concepts can be applied to cybersecurity for early detection of anomalous activities. The proposed approach offers computational efficiency advantages over traditional deep learning methodswhilemaintainingcompetitivedetectionaccuracy. This makes it particularly suitable for deployment in resource-constrainedenvironmentsorscenariosrequiring immediatethreatidentificationandresponse.Accordingto [7],Martinez,A., Brown, S., andGarcia,L. present"Hybrid Machine Learning and Deep Learning for Cybersecurity Applications" in ACM Computing Surveys. This comprehensive survey examines the integration of traditionalmachinelearningtechniqueswithdeeplearning approaches to create more robust and versatile cybersecuritysolutions.Theresearchprovidesathorough analysis of how different algorithmic approaches can be combined to leverage their respective strengths while mitigatingindividualweaknesses.Thestudyofferspractical guidance for implementing hybrid systems in various cybersecuritycontexts,fromnetworkintrusiondetectionto malwareanalysis.Accordingto[8],Taylor,B.,Anderson,C., andMiller,D.explore"FederatedLearningforDistributed CybersecurityinCloudEnvironments"inProceedingsofthe IEEE Symposium on Security and Privacy. This study addresses the challenges of implementing effective cyber securitymeasuresacrossdistributedcloudinfrastructures while maintaining data sovereignty and privacy requirements. The research demonstrates how federated learning can enable collaborative threat detection across multiple cloud providers and organizational boundaries. This approach al lows for the development of more comprehensivethreatintelligencewhilerespectingprivacy constraints and regulatory requirements that govern data sharingincybersecuritycontexts.Accordingto[9],Lee, J., Park,K.,andChoi,M.investigate"GraphNeuralNetworksfor Advanced Persistent Threat Detection" in Cybersecurity journal. This research explores the application of graphbased deep learning models for detecting sophisticated, long-termcyberattacksthattraditionaldetectionmethods oftenmissduetotheirstealthyanddistributednature.The studydemonstrateshowgraphneuralnetworkscanmodel complexrelationshipsbetweendifferentsystemcomponents anduseractivitiestoidentifysubtlepatternsindicative of
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025 www.irjet.net p-ISSN: 2395-0072
advancedpersistentthreats.Theexperimentalresultsshow promisingperformanceindetectingmultistageattacksthat span extended time periods and multiple system components. According to [10], Smith, R., Jones, T., and Williams, P. present "Transformer-Based Models for Network Anomaly Detection" in Neural Networks journal. This research in vestigates the application of transformer architectures, originally developed for natural language processing,tothedomainofnetworksecurityandanomaly detection.Thestudyexploreshowattentionmechanismscan beleveragedtoidentifysubtlepatternsinnetworktraffic. The proposed transformer-based approach demonstrates superiorperformanceincapturinglong-rangedependencies in network traffic sequences, enabling more accurate detectionofsophisticatedattacksthatinvolvecoordinated activities across multiple time periods. The research con tributestothegrowingbodyofworkonapplyingadvanced deeplearningarchitecturestocybersecuritychallenges.
4. ADVANCED APPLICATIONS AND USE CASES
Real-Time Threat Intelligence Deep learning systems can integrate multiple threat intelligence feeds to provide contextual awareness for attack detection. By correlating internal network observations with external threat indicators, these systems can identify sophisticated multistage attacks and advanced persistent threats (APTs). The integration of threat intelligence enhances the accuracy of detection systems by providing additional context about emerging threats and attack patterns. Automated Incident ResponseIntegrationwithsecurityorchestrationplatforms enablesautomatedresponse to detectedthreats, including networkisolation,accessrevocation,andevidencecollection forforensicanalysis.Machinelearningmodelscanprioritize incidents based on severity and business impact, enabling security teams to focus their efforts on the most critical threats while automated systems handle routine security events.PredictiveSecurityAnalyticsBeyondreactivethreat detection, deep learning enables predictive security capabilitiesthatcanforecastpotentialattackvectorsbased oncurrentsecurityposture,threatlandscapeanalysis,and historical attack patterns. This proactive approach allows organizations to strengthen their defenses before attacks occur, significantly reducing the likelihood of successful securitybreaches.
5. FUTURE SCOPE
IntegrationwithSmartGrids:Real-timethreatdetectioncan be integrated with smart grid infrastructure to enable dynamicsecuritymonitoringandautomatedthreatresponse. AIcanhelppreventcyberattacksoncriticalinfrastructureby predicting attack patterns and implementing proactive defense mechanisms. Quantum-Resistant Security: The emergence of quantum computing threatens current cryptographicsystems,requiringdevelopmentofquantumresistantdeeplearningapproachesforfuturecloudsecurity.
Research in this area focuses on developing security algorithms that remain effective even in the presence of quantumcomputingcapabilities.EdgeComputingSecurity: As cloud architectures ex tend to edge computing environments,distributeddeeplearningmodelsmustadapt to resource-constrained environments while maintaining securityeffectiveness.Thisincludesdevelopinglightweight models that can operate efficiently on edge devices while providingrobustsecuritycoverage.5GandIoTSecurity:The proliferation of 5G networks and IoT devices creates new attacksurfacesrequiringspecializeddeeplearningmodels capableofhandlingdiversedevicetypesandcommunication protocols.Futureresearchwillfocusondevelopingadaptive security models that can protect heterogeneous IoT ecosystems. Federated Learning: Collaborative model trainingacrossmultiplecloudenvironmentswithoutsharing sensitive data enables improved threat detection while preserving privacy. This approach allows organizations to benefit from collective intelligence while maintaining data sovereigntyandregulatorycompliance.AutomatedSecurity Orchestration: AI can power auto mated security orchestration systems that coordinate responses across multiple security tools and platforms. These systems can reduceresponsetimesandensureconsistentapplicationof securitypoliciesacrosscomplexcloudenvironments
6. CONCLUSIONS
The AI-powered cyberattack detection project demon stratesthetransformativepotentialofdeep learninginad dressing modern cloud security challenges. By leveraging historicalattackdata,networktrafficpatterns,andadvanced AImodelssuchasCNNs,LSTMs,andensembletechniques, these systems provide accurate and timely detection of sophisticated threats. The predictive capabilities enable securityteamsandcloudproviderstoimplementproactive defense strategies, reduce incident response times, and enhanceoverallsecurityposture.Throughintelligentthreat detection and data-driven security decision-making, deep learning-basedsystemsnotonlyimprovedetectionaccuracy and operational reliability but also contribute to cost reduction and regulatory compliance. Furthermore, the integration of such AI-dri ven solutions supports the transition toward zero-trust security architectures and intelligentsecurityoperationscenters,aligningwithglobal cybersecurityframeworksandregulatoryrequirements.The findings emphasize that while deep learning offers promisingimprovementsovertraditionalsecuritymethods, effective implementation requires a combination of optimized architectures, real-time data processing capabilities, and continuous model updates to keep pace with emerging threats. Future work should focus on enhancingmodeltransparency,scalability,andadaptability to ensure practical deployment in live cloud systems. Ultimately,thisresearchcontributestothedevelopmentof intelligent, responsive, and resilient intrusion detection systemsforcloudcomputingenvironments
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025 www.irjet.net p-ISSN: 2395-0072
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