• Samson Isaac Kaduna State University
  • Damilola Kolawole Ayodeji
  • Yusuf Luqman
  • Solomon Mathew Karma
  • Jibril Aminu
Keywords: Cyber Security, Communication, Risk, Semi-Supervised Learning


The increasing digitalization of our society has brought about numerous benefits, enabling seamless communication, convenient transactions, and efficient operations. However, with this growing reliance on interconnected systems and information technology, the risk of cyber-attacks has also surged. Cyber threats, such as data breaches, ransomware, and sophisticated malware, have become more prevalent, threatening the confidentiality, integrity, and availability of critical data and services. Organizations across industries face the daunting challenge of defending against a wide array of cyber-attacks that continue to evolve in complexity and stealth. In response to this ever-changing cyber threat landscape, Cyber Security Risk Management (CSRM) and attack detection have become critical components of any comprehensive cybersecurity strategy. The ability to identify and mitigate cyber risks and swiftly detect malicious activities is paramount for safeguarding sensitive information, preserving business continuity, and maintaining the trust of customers and stakeholders. A novel approach to Cyber Security Risk Management through an Attack Detection Model that utilizes Semi-Supervised Learning Auto-Encoders in conjunction with Probabilistic Bayesian Networks. The study compares the performance of Multi Connect Variational Auto-Encoder (MC-VAE), Probabilistic Bayesian Networks (PBN), and a combined model of MC-VAE and PBN. The study employs the NUSW-NB15_GT dataset for training and evaluation purposes. Notably, the Semi-Supervised Learning with Probabilistic Bayesian Networks (SSL-PBN) model demonstrates exceptional results, achieving a precision rate of 94% and a recall rate of 90%. The F1 score of 0.9191 highlights the SSL-PBN model's efficacy in achieving a balanced trade-off between precision and recall, critical for minimizing false positives and false negatives...


Abdelaty, M., Scott-Hayward, S., & Sezer, S. (2021). Gadot: Gan-based adversarial training for robust DDoS attack detection. IEEE Transactions on Network and Service Management, 18(3), 1544-1556. DOI:

Ahmad Z, Khan AS, Nisar K, Haider I, Hassling R, Haque MR, et al. (2021). Anomaly detection using Deep Neural Network for Iot Architecture. Application Science; 11:7050. DOI:

Aiken, J., & Scott-Hayward, S. (2019). Investigating adversarial attacks against network intrusion detection systems in SDNs. In 2019 IEEE Conference on Network Softwarization (NetSoft) (pp. 221-225). Institute of Electrical and Electronic Engineering. DOI:

Al-Abassi, A., Karimipour, H., Dehghantanha, A., & Parizi, R. M. (2020). An ensemble deep learning-based cyber-attack detection in industrial control system. Institute of Electrical and Electronic Engineering (IEEE) Access, 8, 83965-83973. DOI:

Al-Abassi, A., Karimipour, H., Dehghantanha, A., & Parizi, R. M. (2020). An ensemble deep learning-based cyber-attack detection in industrial control system. IEEE Transactions on Industrial Informatics, 16(1), 366-374.

Aldhaheri S., Alghazzawi D., Cheng L., Barnawi A., & Alzahrani B.A. (2020). Artificial immune systems approach to secure the internet of things: a systematic review of the literature and recommendations for future study. Journal of Network Computer Applications,157:102537. DOI:

Anthi, E., Williams, L., Rhode, M., Burnap, P., & Wedgbury, A. (2021). Adversarial attacks on machine learning cybersecurity defences in industrial control systems. Journal of Information Security and Applications, 58, 102717. DOI:

Apruzzese, G., Andreolini, M., Colajanni, M., & Mastroianni, C. (2020). Hardening random forest cyber detectors against adversarial attacks. IEEE Transactions on Emerging Topics in Computing, 8(4), 849-858. DOI:

Apruzzese, G., Andreolini, M., Ferretti, L., & Mastroianni, C. (2022). Modelling realistic adversarial attacks against network intrusion detection systems. ACM Transactions on Cyber-Physical Systems, 6(1), 1-26. DOI:

Ashraf J, Bakhshi AD, Moustafa N, Khurshid H, Javed A, Beheshti A. (2021) Novel deep Learning-Enabled LSTM Autoencoder Architecture for discovering anomalous events from intelligent transportation systems. IEEE Trans Intelligent Transport System,22(7):4507–18. DOI:

Atul DJ, Kamalraj R, Ramesh G, Sakthidasan Sankaran K, Sharma S, Khasim S. A machine learning based IoT for providing an intrusion detection system for security. Microprocess Microsystem 2021; 82:103741. DOI:

Banitalebi Dehkordi, A., Soltanaghaei, M. R., & Meybodi, M. R. (2021). The DDoS attacks detection through machine learning and statistical methods in SDN. The Journal of Supercomputing, 77(2), 1755-1781. DOI:

Benzaïd, C., & Taleb, T. (2020). AI for beyond 5G networks: a cyber-security defense or offense enabler? IEEE Network, 34(6), 66-72. DOI:

Bland JA, Petty MD, Whitaker TS, Maxwell KP, Cantrell WA. Machine learning cyberattack and defense strategies. Computer Security 2020; 92:101738. DOI:

Chauhan, R., & Heydari, S. S. (2020). Polymorphic Adversarial DDoS attack on IDS using GAN. In 2020 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE. DOI:

Chen T, Liu X, Xia B, Wang W, Lai Y. Unsupervised anomaly detection of industrial robots using sliding-window convolutional variational autoencoder. IEEE Access 2020; 8:47072–81. DOI:

Coulter R, Han QL, Pan L, Zhang J, Xiang Y. (2020). Code analysis for intelligent cyber systems: a data driven approach. Information Science,524:46–58 DOI:

Doriguzzi-Corin, R., Millar, S., & Giordano, S. (2020). LUCID: A practical, lightweight deep learning solution for DDoS attack detection. IEEE Transactions on Network and Service Management, 17(4), 2582-2593. DOI:

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