Volume 14 , Issue 2 , PP: 132-147, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Vivek alias M. Chidambaram 1 * , Karthik Painganadu Chandrasekaran 2
Doi: https://doi.org/10.54216/JCIM.140209
In new technologies like fog computing, edge computing, cloud computing, and the Internet of Things (IoT), cybersecurity concerns and cyber-attacks have surged. The demand for better threat detection and prevention systems has increased due to the present global uptick in phishing and computer network attacks. In order to identify irregularities and attacks on the network, which have increased in scale and prevalence, threat identification is essential. However, the community is forced to investigate and create novel threat detection approaches that are capable of detecting threats using anomalies due to the increase in network threats, the growth of new methods of attack and computations, and the requirement to ensure security measures. A novel mechanism is employed to identify threats in a data based on optimized deep learning. The main aim of this paper is the usage of data classification system based on Deep Learning (DL). The proposed mechanism employed the TCP (Transmission Control Protocol) communication protocol to extract data from loud IoT (Internet of Things) networks for the purpose of threat detection. To perform feature extraction an Ant Colony Optimization (ACO) is utilised, through Recurrent Neural Network (RNN), the attacks in data are classified and detected. Additionally, the suggested approach has been evaluated and trained using the BOUN DDoS contemporary dataset, which comprises a variety of attack types and allows for the effectiveness of the framework to be determined to compare it to previous approaches. The Findings indicate that the suggested approach achieved higher accuracy in DDoS attack identification in comparison with Traditional deep learning methods. The existing method detects the generic attack with lower efficiency however; the proposed mechanism achieves better accuracy in both the detection of the DDoS attack and the detection of regular traffic.
Threat Detection , Data Classification , Deep Learning (DL) , Recurrent Neural Network (RNN) , Ant Colony Optimization (ACO)
[1] Alwasel, Bader, Abdulaziz Aldribi, Mohammed Alreshoodi, Ibrahim S. Alsukayti, and Mohammed Alsuhaibani. 2023. “Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection.” Applied Sciences 13 (13): 7774. https://doi.org/10.3390/app13137774.
[2] Arul, Easwaramoorthy, and A Punidha. 2021. “Supervised Deep Learning Vector Quantization to Detect MemCached DDOS Malware Attack on Cloud.” SN Computer Science 2 (2): 85.
[3] Ambeth Kumar, V.D. Ramakrishnan,M. Ashok Kumar, V.D. Malathi,S. (2015). Performance Improvement using an Automation System for Recognition of Multiple Parametric Features based on Human Footprint. kuwait journal of science .42(1), 109-132.
[4] De Neira, Anderson Bergamini, Burak Kantarci, and Michele Nogueira. 2023. “Distributed Denial of Service Attack Prediction: Challenges, Open Issues and Opportunities.” Computer Networks 222 (February): 109553. https://doi.org/10.1016/j.comnet.2022.109553.
[5] Fahad, Labiba Gillani, Syed Fahad Tahir, Waseem Shahzad, Mehdi Hassan, Hani Alquhayz, and Rabia Hassan. 2020. “Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Data Diagnosis.” Scientific Programming 2020: 1–10. https://doi.org/10.1155/2020/1064934.
[6] Fatani, Abdulaziz, Mohamed Abd Elaziz, Abdelghani Dahou, Mohammed A. A. Al-Qaness, and Songfeng Lu. 2021. “IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization.” IEEE Access 9: 123448–64. https://doi.org/10.1109/ACCESS.2021.3109081.
[7] Gaurav, Akshat, Brij B. Gupta, Wadee Alhalabi, Anna Visvizi, and Yousef Asiri. 2022. “A Comprehensive Survey on DDoS Attacks on Various Intelligent Systems and It’s Defense Techniques.” International Journal of Intelligent Systems 37 (12): 11407–31. https://doi.org/10.1002/int.23048.
[8] Abhishek Kumar, Rini Dey, G. Madhukar Rao, Saravanan Pitchai, K. Vengatesan, V. D Ambeth Kumar, " 3D Animation and Virtual Reality Integrated Cognitive Computing for Teaching and Learning in Higher Education", Advances in Parallel Computing, 2021, 39, pp. 615 - 620.
[9] Kumar, A., Singh, K.U., Hsieh, SY., Kumar, V.D.A., Kumar, A. (2021). Distribution Key Scheme for Secure Group Management in VANET Using Polynomial Interpolation. In: Lin, L., Liu, Y., Lee, CW. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2021. Communications in Computer and Information Science, vol 1495. Springer, Singapore. https://doi.org/10.1007/978-981-16-7913-1_1
[10] Lee, Jonghoon, Jonghyun Kim, Ikkyun Kim, and Kijun Han. 2019. “Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles.” IEEE Access 7: 165607–26. https://doi.org/10.1109/ACCESS.2019.2953095.
[11] Liu, Hongyu, and Bo Lang. 2019. “Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey.” Applied Sciences 9 (20): 4396.
[12] Merrill, William, Gail Weiss, Yoav Goldberg, Roy Schwartz, Noah A. Smith, and Eran Yahav. 2020. “A Formal Hierarchy of RNN Architectures.” arXiv. https://doi.org/10.48550/arXiv.2004.08500.
[13] Mittal, Meenakshi, Krishan Kumar, and Sunny Behal. 2022. “Deep Learning Approaches for Detecting DDoS Attacks: A Systematic Review.” Soft Computing, January. https://doi.org/10.1007/s00500-021-06608-1.
[14] Najafimehr, Mohammad, Sajjad Zarifzadeh, and Seyedakbar Mostafavi. 2023. “DDoS Attacks and Machine‐learning‐based Detection Methods: A Survey and Taxonomy.” Engineering Reports, May, e12697. https://doi.org/10.1002/eng2.12697.
[15] Nishant, Rohit, Mike Kennedy, and Jacqueline Corbett. 2020. “Artificial Intelligence for Sustainability: Challenges, Opportunities, and a Research Agenda.” International Journal of Information Management 53 (August): 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104.
[16] Rahman, Md Arafatur, A. Taufiq Asyhari, Ong Wei Wen, Husnul Ajra, Yussuf Ahmed, and Farhat Anwar. 2021. “Effective Combining of Feature Selection Techniques for Machine Learning-Enabled IoT Intrusion Detection.” Multimedia Tools and Applications 80 (20): 31381–99. https://doi.org/10.1007/s11042-021-10567-y.
[17] Rao, Sunil, Gowtham Muniraju, Cihan Tepedelenlioglu, Devarajan Srinivasan, Govindasamy Tamizhmani, and Andreas Spanias. 2021. “Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays.” IEEE Access 9: 120034–42. https://doi.org/10.1109/ACCESS.2021.3108684.
[18] Shieh, Chin-Shiuh, Thanh-Tuan Nguyen, and Mong-Fong Horng. 2023. “Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric.” Mathematics 11 (9): 2145. https://doi.org/10.3390/math11092145.
[19] Sommese, Raffaele, Kc Claffy, Roland Van Rijswijk-Deij, Arnab Chattopadhyay, Alberto Dainotti, Anna Sperotto, and Mattijs Jonker. 2022. “Investigating the Impact of DDoS Attacks on DNS Infrastructure.” In Proceedings of the 22nd ACM Internet Measurement Conference, 51–64. Nice France: ACM. https://doi.org/10.1145/3517745.3561458.
[20] Sousa, Bruno, Miguel Arieiro, Vasco Pereira, Joao Correia, Nuno Lourenco, and Tiago Cruz. 2021. “ELEGANT: Security of Critical Infrastructures With Digital Twins.” IEEE Access 9: 107574–88. https://doi.org/10.1109/ACCESS.2021.3100708.
[21] Talasila, Vamsidhar, Kotakonda Madhubabu, K Madhubabu, M Mahadasyam, N Atchala, and L Kande. 2020. “The Prediction of Diseases Using Rough Set Theory with Recurrent Neural Network in Big Data Analytics.” Al-Saedi, W., S. Lachowicz, D. Habibi, and O. Bass. 2013. Power flow control in grid-connected microgrid operation using particle swarm optimization under variable load conditions. International Journal Of Electrical Power & Energy Systems 49:76–85. doi:10.1016/j.ijepes.2012.12.017.
[22] Toldinas, Jevgenijus, Algimantas Venčkauskas, Agnius Liutkevičius, and Nerijus Morkevičius. 2022. “Framing Network Flow for Anomaly Detection Using Data Recognition and Federated Learning.” Electronics 11 (19): 3138.
[23] Ullah, Farhan, Shamsher Ullah, Muhammad Rashid Naeem, Leonardo Mostarda, Seungmin Rho, and Xiaochun Cheng. 2022. “Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Data Representation.” Sensors 22 (15): 5883. https://doi.org/10.3390/s22155883.
[24] Wang, Zhendong, Yong Zeng, Yaodi Liu, and Dahai Li. 2021. “Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection.” IEEE Access 9: 16062–91. https://doi.org/10.1109/ACCESS.2021.3051074.
[25] Wu, Jiashu, Yang Wang, Binhui Xie, Shuang Li, Hao Dai, Kejiang Ye, and Chengzhong Xu. 2022. “Joint Semantic Transfer Network for IoT Intrusion Detection.” arXiv. http://arxiv.org/abs/2210.15911.
[26] Yang, Jiwon, and Hyuk Lim. 2021. “Deep Learning Approach for Detecting Malicious Activities over Encrypted Secure Channels.” IEEE Access 9: 39229–44.
[27] Balakrishnan, Chitra, and V. D. Ambeth Kumar. (2023). IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics 13(4), 775
[28] Hemamalini, Selvamani, and Visvam Devadoss Ambeth Kumar. (2022). Outlier Based Skimpy Regularization Fuzzy Clustering Algorithm for Diabetic Retinopathy Image Segmentation. Symmetry, 14(12), 2512
[29] S. Hemamalini ,V. D. Ambeth Kumar ,R. Venkatesan,S. Malathi. (2023). Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification. Journal of Fusion: Practice and Applications, 11 ( 2 ), 90-110.
[30] C. S. Manigandaa,V. D. Ambeth Kumar,G. Ragunath,R. Venkatesan,N. Senthil Kumar. (2023). De-Noising and Segmentation of Medical Images using Neutrophilic Sets. Journal of Fusion: Practice and Applications, 11 ( 2 ), 111-123.
