1
Faculty of Artificial Intelligence, Data Science department, Egyptian Russian University (ERU), Cairo, Egypt
(mahmoud.zaher@eru.edu.eg)
2
Electrical Eng. Dept, Higher Institute of Engineering, Minia, Egypt
(dryahiabahaahassan@gmail.com)
3
Faculty of Computers and Information, Sadat Academy for Management Sciences, Cairo, Egypt & French University in Cairo, Egypt
(nabil.omr@sadatacademy.edu.eg)
Abstract :
The proliferation of botnet threats within Internet of Things (IoT) networks has underscored the critical need for robust detection mechanisms. This study addresses this imperative by presenting a comprehensive framework employing Machine Learning (ML) techniques for botnet detection. Leveraging a dataset sourced from authentically compromised IoT devices, the research delves into the intricate behaviors exhibited by botnets, emphasizing the encounters pretended by their polymorphic characteristics. A convolutional neural network architecture, featuring stacked layers with residual connections, serves as the cornerstone of the proposed detection system. The efficiency of the developed model is evaluated using meticulous visualization of data insights, learning behaviors, and detection performance, which demonstrate a great ability to discriminate between different botnet activities. This study presents a prominent improvement to the cybersecurity field by developing an effective solution for invigorating IoT network defenses against developing botnet threats, which highlights the essential role of ML-driven methods in the preservation of the integrity of interconnected devices.
Keywords :
Cybersecurity; Network Security; Intrusion Detection; Anomaly Detection; Machine Learning (ML); Threat Detection; Behavioral Analysis.
References :
[1] Chakraborty, Abhilash, Anupam Biswas, and Ajoy Kumar Khan. 2023. “Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation.” In Artificial Intelligence for Societal Issues, 3–25. Springer.
[2] Ahsan, Mostofa, Kendall E Nygard, Rahul Gomes, Md Minhaz Chowdhury, Nafiz Rifat, and Jayden F Connolly. 2022. “Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review.” Journal of Cybersecurity and Privacy 2 (3): 527–55.
[3] Gunduz, Muhammed Zekeriya, and Resul Das. 2020. “Cyber-Security on Smart Grid: Threats and Potential Solutions.” Computer Networks 169: 107094.
[4] AJAYI, Wumi, Obi Ibeto, Taiwo Olomola, and Mathias Madewa. 2022. “ANALYSIS OF MODERN CYBERSECURITY THREAT TECHNIQUES ANDAVAILABLE MITIGATING METHODS.” International Journal of Advanced Research in Computer Science 13 (2).
[5] Haddaji, Achref, Samiha Ayed, and Lamia Chaari Fourati. 2022. “Artificial Intelligence Techniques to Mitigate Cyber-Attacks within Vehicular Networks: Survey.” Computers and Electrical Engineering 104: 108460.
[6] Lykou, Georgia, Argiro Anagnostopoulou, and Dimitris Gritzalis. 2018. “Smart Airport Cybersecurity: Threat Mitigation and Cyber Resilience Controls.” Sensors 19 (1): 19.
[7] Kitchin, Rob, and Martin Dodge. 2020. “The (in) Security of Smart Cities: Vulnerabilities, Risks, Mitigation, and Prevention.” In Smart Cities and Innovative Urban Technologies, 47–65. Routledge.
[8] Tufail, Shahid, Imtiaz Parvez, Shanzeh Batool, and Arif Sarwat. 2021. “A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid.” Energies 14 (18): 5894.
[9] Zeadally, Sherali, Erwin Adi, Zubair Baig, and Imran A Khan. 2020. “Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity.” Ieee Access 8: 23817–37.
[10] Radoglou-Grammatikis, Panagiotis, Konstantinos Rompolos, Panagiotis Sarigiannidis, Vasileios Argyriou, Thomas Lagkas, Antonios Sarigiannidis, Sotirios Goudos, and Shaohua Wan. 2021. “Modeling, Detecting, and Mitigating Threats against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach.” IEEE Transactions on Industrial Informatics 18 (3): 2041–52.
[11] Alhayani, Bilal, Sara Taher Abbas, Dawood Zahi Khutar, and Husam Jasim Mohammed. 2021. “Best Ways Computation Intelligent of Face Cyber Attacks.” Materials Today: Proceedings, 26–31.
[12] Aljuhani, Ahamed. 2021. “Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments.” IEEE Access 9: 42236–64.
[13] Li, Zhiyi, Dong Jin, Christopher Hannon, Mohammad Shahidehpour, and Jianhui Wang. 2016. “Assessing and Mitigating Cybersecurity Risks of Traffic Light Systems in Smart Cities.” IET Cyber-Physical Systems: Theory \& Applications 1 (1): 60–69.
[14] Chehri, Abdellah, Issouf Fofana, and Xiaomin Yang. 2021. “Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence.” Sustainability 13 (6): 3196.
[15] Ismail, M. and F.Abd El-Gawad , A. (2023) “Revisiting Zero-Trust Security for Internet of Things”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33106.
[16] Sai, Chennu Naga Venkata, Rangu Jaswanth, Avula Manasa, Yaramakula Sai Pranathi Reddy, Suryakanth V Gangashetty, and D Govind. 2023. “Assessing the Effectiveness of Artificial Intelligence Techniques in Mitigating Cyber Security Risks.” International Journal of Intelligent Systems and Applications in Engineering 11 (4): 763–71.
[17] Marble, Julie L, William F Lawless, Ranjeev Mittu, Joseph Coyne, Myriam Abramson, and Ciara Sibley. 2015. “The Human Factor in Cybersecurity: Robust \& Intelligent Defense.” Cyber Warfare: Building the Scientific Foundation, 173–206.
[18] Harel, Yaniv, Irad Ben Gal, and Yuval Elovici. 2017. “Cyber Security and the Role of Intelligent Systems in Addressing Its Challenges.” ACM Transactions on Intelligent Systems and Technology (TIST). ACM New York, NY, USA.
[19] Syrmakesis, Andrew D, Cristina Alcaraz, and Nikos D Hatziargyriou. 2022. “Classifying Resilience Approaches for Protecting Smart Grids against Cyber Threats.” International Journal of Information Security 21 (5): 1189–1210.
[20] M. Abdel-Basset, H. Hawash and K. Sallam, "Federated Threat-Hunting Approach for Microservice-Based Industrial Cyber-Physical System," in IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1905-1917, March 2022, doi: 10.1109/TII.2021.3091150.
| Style | # |
|---|---|
| MLA | Mahmoud A. Zaher, Yahia B. Hassan, Nabil M. Eldakhly. "Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach." International Journal of Wireless and Ad Hoc Communication, Vol. 7, No. 2, 2023 ,PP. 56-63 (Doi : https://doi.org/10.54216/IJWAC.070204) |
| APA | Mahmoud A. Zaher, Yahia B. Hassan, Nabil M. Eldakhly. (2023). Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach. Journal of International Journal of Wireless and Ad Hoc Communication, 7 ( 2 ), 56-63 (Doi : https://doi.org/10.54216/IJWAC.070204) |
| Chicago | Mahmoud A. Zaher, Yahia B. Hassan, Nabil M. Eldakhly. "Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach." Journal of International Journal of Wireless and Ad Hoc Communication, 7 no. 2 (2023): 56-63 (Doi : https://doi.org/10.54216/IJWAC.070204) |
| Harvard | Mahmoud A. Zaher, Yahia B. Hassan, Nabil M. Eldakhly. (2023). Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach. Journal of International Journal of Wireless and Ad Hoc Communication, 7 ( 2 ), 56-63 (Doi : https://doi.org/10.54216/IJWAC.070204) |
| Vancouver | Mahmoud A. Zaher, Yahia B. Hassan, Nabil M. Eldakhly. Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach. Journal of International Journal of Wireless and Ad Hoc Communication, (2023); 7 ( 2 ): 56-63 (Doi : https://doi.org/10.54216/IJWAC.070204) |
| IEEE | Mahmoud A. Zaher, Yahia B. Hassan, Nabil M. Eldakhly, Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 7 , No. 2 , (2023) : 56-63 (Doi : https://doi.org/10.54216/IJWAC.070204) |