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A COMPARATIVE STUDY ON CYBER SECURITY THREATS DETECTION IN INTERNET OF THINGS


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1 P K R Arts College for Women, India
 

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Internet of Things (IoT) is an evolving digital technology, which is mainly meant to bridge physical and virtual world. New business model has been emerged because of people, objects, machines and Internet connectivity along with new interactions amid humanity and remaining world. IoT is considered as a gateway for cyber-attacks since various resources such as systems, applications, data storage, and services are connected through IoT that relentlessly provide services in the organization. IoT security is challenging factor due to prevailing software piracy and malware attacks presently. The economic and reputational damages are caused by these threats due to crucial information burglary. IoT malware detection is yet another challenging factor due to security design deficiency besides IoT devices specific characteristics such as processor architecture heterogeneity, particularly on identifying cross-architecture IoT malware. Hence, IoT malware detection area is main objective of this research by security community recently. The familiar dynamic or static analyses to detect IoT malware is greatly deployed in various researches with its benefits. A systematic review relating to latest research studies and technologies of classical, Deep Learning (DL) and Machine Learning (ML) methodologies for cyber security threats recognition are outlined and are view is given in this paper. Every approach pertaining to its objective, approach and outcomes have been examined for every selected work. Deep Learning (DL) approach is greatly utilized for malware infected files and pirated software recognition in IoT network in cloud. Several software piracy and malware attack detection methods has been analyzed in this paper with respect to its advantages and disadvantages. The source code plagiarism is detected through DL methodology and dataset collection is done from Google Code Jam (GCJ) for software piracy investigation. Rather than this, Deep Convolutional Neural Network (DCNN) is mainly involved in identifying malicious infections in IoT network. Mailing dataset is utilized for obtaining malware samples which is used for experimental purpose. It is thereby substantiated that suggested method namely Tensor Flow Deep Neural Network (TF-DNN) classification performance for assessing cyber security threats in IoT are enhanced when compared with classic approaches such as Support Vector Machine (LBP+SVM), Gray Level Cooccurrence Matrix with Support Vector Machine (GLCM+SVM) pertaining to F-measure (F1) and Classification Accuracy (CA).
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  • G.J. Joyia, R.M. Liaqat, A. Farooq and S. Rehman, “Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain”, Journal of Communication, Vol. 12, No. 4, pp. 240-247, 2017.

  • A. Zanella, N. Bui, A. Castellani, L. Vangelista and M. Zorzi, “Internet of Things for Smart Cities”, IEEE Internet of Things, Vol. 1, No. 1, pp. 22-32, 2014.

  • Q.D. Ngo, H.T. Nguyen, L.C. Nguyen and D.H. Nguyen, “A Survey of IoT Malware and Detection Methods based on Static Features”, ICT Express, Vol. 6, No. 4, pp. 280-286, 2020.

  • V. Ramalingam, D.B. Mariappan, R. Gopal and K.M. Baalamurugan, “An Effective Social Internet of Things (SIoT) Model for Malicious Node Detection in Wireless Sensor Networks”, CRC Press, 2020.

  • J. Granjal, E. Monteiro and J.S. Silva, “Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues”, IEEE Communications Surveys and Tutorials, Vol. 17, No. 3, pp. 1294-1312, 2015.

  • D.E. Kouicem, A. Bouabdallah and H. Lakhlef, “Internet of Things Security: A Top-Down Survey”, Computer Networks, Vol. 141, pp. 199-221, 2018.

  • S. Jabbar, K.R. Malik, M. Ahmad, O. Aldabbas, M. Asif, S. Khalid, K. Han and S.H. Ahmed, “A Methodology of Real Time Data Fusion for Localized Big Data Analytics”, IEEE Access, Vol. 6, pp.24510-24520, 2018.

  • F. Ullah, J. Wang, M. Farhan, M. Habib and S. Khalid, “Software Plagiarism Detection in Multiprogramming Languages using Machine Learning Approach”, Concurrency and Computation: Practice and Experience, Vol. 33, No. 4, pp. 1-12, 2018.

  • I. Ghafir, J. Saleem, M. Hammoudeh, H. Faour, V. Prenosil, S. Jaf, S. Jabbar and T. Baker, “Security Threats to Critical Infrastructure: the Human Factor”, Journal of Supercomputing, Vol. 74, No. 10, pp. 4986-5002, 2018.

  • K.M. Baalamurugan, R. Gopal and V. Ramalingam, “An Energy-Efficient Quasi-Oppositional Krill Herd Algorithm Based Clustering Protocol for Internet of Things Sensor Networks”, CRC Press, 2020.

  • H. Sun, X. Wang, R. Buyya and J. Su, “CloudEyes: Cloudbased Malware Detection with Reversible Sketch for Resource-Constrained Internet of Things (IoT) Devices”, Software: Practice and Experience, Vol. 47, No. 3, pp. 421- 441, 2017.

  • S. Shen, L. Huang, H. Zhou, S. Yu, E. Fan and Q. Cao, “Multistage Signaling Game-based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog Cloud-based IoT Networks”, IEEE Internet of Things, Vol. 5, No. 2, pp. 1043-1054, 2018.

  • H. Naeem, “Detection of Malicious Activities in Internet of Things Environment Based on Binary Visualization and Machine Intelligence”, Wireless Personal Communications, Vol. 108, No. 4, pp. 2609-2629, 2019.

  • Q. Yan, W. Huang, X. Luo, Q. Gong and F.R. Yu, “A Multi Level DDoS Mitigation Framework for the Industrial Internet of Things”, IEEE Communications Magazine, Vol. 56, No. 2, pp. 30-36, 2018.

  • M.S. Hossain and G. Muhammad, “Cloud-Assisted Industrial Internet of Things (IIoT)-Enabled Framework for Health Monitoring”, Computer Networks, Vol. 101, pp. 192- 202, 2016.

  • K.M. Baalamurugan and D.S.V. Bhanu, “Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, No. 1, pp. 303-307, 2018.

  • J.W. Son, T.G. Noh, H.J. Song and S.B. Park, “An Application for Plagiarized Source Code Detection based on a Parse Tree Kernel”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 8, pp. 1911-1918, 2013.

  • A. Modiri, N. Dehghantanha and K. Parizi, “Fuzzy Pattern Tree for Edge Malware Detection and Categorization in IoT”, Journal of System Architecture, Vol. 5, No. 2, pp. 1- 19, 2018.

  • D. Yin, L. Zhang and K. Yang, “A DDoS Attack Detection and Mitigation with Software-Defined Internet of Things Framework”, IEEE Access, Vol. 6, pp. 24694-24705, 2018.

  • G. Cosma and M. Joy, “An Approach to Source-Code Plagiarism Detection and Investigation using Latent Semantic Analysis”, IEEE Transactions on Computers, Vol. 61, No. 3, pp. 379-394, 2012.

  • W. Zhou and B. Yu, “A Cloud-Assisted Malware Detection and Suppression Framework for Wireless Multimedia System in IoT based on Dynamic Differential Game”, China Communications, Vol. 15, No. 2, pp. 209-223, 2018.

  • P.K. Sharma, J.H. Park, Y.S. Jeong and J.H. Park, “Shsec: SDN based Secure Smart Home Network Architecture for Internet of Things”, Mobile Networks and Applications, Vol. 24, No. 3, pp. 913-924, 2019.

  • M. Shafiq, Z. Tian, Y. Sun, X. Du and M. Guizani, “Selection of Effective Machine Learning Algorithm and Bot-IoT Attacks Traffic Identification for Internet of Things in Smart City”, Future Generation Computer Systems, Vol. 107, pp. 433-442, 2020.


Abstract Views: 597

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  • A COMPARATIVE STUDY ON CYBER SECURITY THREATS DETECTION IN INTERNET OF THINGS

Abstract Views: 597  |  PDF Views: 232

Authors

P Vijayalakshmi
P K R Arts College for Women, India
D Karthika
P K R Arts College for Women, India

Abstract


Internet of Things (IoT) is an evolving digital technology, which is mainly meant to bridge physical and virtual world. New business model has been emerged because of people, objects, machines and Internet connectivity along with new interactions amid humanity and remaining world. IoT is considered as a gateway for cyber-attacks since various resources such as systems, applications, data storage, and services are connected through IoT that relentlessly provide services in the organization. IoT security is challenging factor due to prevailing software piracy and malware attacks presently. The economic and reputational damages are caused by these threats due to crucial information burglary. IoT malware detection is yet another challenging factor due to security design deficiency besides IoT devices specific characteristics such as processor architecture heterogeneity, particularly on identifying cross-architecture IoT malware. Hence, IoT malware detection area is main objective of this research by security community recently. The familiar dynamic or static analyses to detect IoT malware is greatly deployed in various researches with its benefits. A systematic review relating to latest research studies and technologies of classical, Deep Learning (DL) and Machine Learning (ML) methodologies for cyber security threats recognition are outlined and are view is given in this paper. Every approach pertaining to its objective, approach and outcomes have been examined for every selected work. Deep Learning (DL) approach is greatly utilized for malware infected files and pirated software recognition in IoT network in cloud. Several software piracy and malware attack detection methods has been analyzed in this paper with respect to its advantages and disadvantages. The source code plagiarism is detected through DL methodology and dataset collection is done from Google Code Jam (GCJ) for software piracy investigation. Rather than this, Deep Convolutional Neural Network (DCNN) is mainly involved in identifying malicious infections in IoT network. Mailing dataset is utilized for obtaining malware samples which is used for experimental purpose. It is thereby substantiated that suggested method namely Tensor Flow Deep Neural Network (TF-DNN) classification performance for assessing cyber security threats in IoT are enhanced when compared with classic approaches such as Support Vector Machine (LBP+SVM), Gray Level Cooccurrence Matrix with Support Vector Machine (GLCM+SVM) pertaining to F-measure (F1) and Classification Accuracy (CA).

References