| References: [1] Talaei Khoei, Tala, and Naima Kaabouch. (2023). A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems. Information 14, no. 2: 103.
[2] Debicha, Islam, Richard Bauwens, Thibault Debatty, Jean-Michel Dricot, Tayeb Kenaza, and Wim Mees (2023). TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems. Future Generation Computer Systems 138: 185-197.
[3] Almiani, Muder, Alia AbuGhazleh, Amer Al-Rahayfeh, Saleh Atiewi, and Abdul Razaque (2020). Deep recurrent neural network for IoT intrusion detection system. Simulation Modelling Practice and Theory 101: 102031.
[4] Linka, Kevin, and Ellen Kuhl (2023). A new family of Constitutive Artificial Neural Networks towards automated model discovery. Computer Methods in Applied Mechanics and Engineering 403: 115731.
[5] Ali, Mohsin, Nurgul Nalgozhina, Olzhas Tasmagambetov, and Yerzhan N. Seitkulov (2022). Homomorphic Encryption with Enhanced Elliptic Curve for Mobile Wireless Network Security.
[6] Khan, Muhammad Ashfaq (2021). HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system. Processes 9, no. 5: 834.
[7] Alamleh, Amneh, O. S. Albahri, A. A. Zaidan, A. H. Alamoodi, A. S. Albahri, B. B. Zaidan, Sarah Qahtan et al. (2023). Multi-attribute decision-making for intrusion detection systems: A systematic review. International Journal of Information Technology & Decision Making 22, no. 01: 589-636.
[8] Razaque, Abdul, Bandar Alotaibi, Munif Alotaibi, Fathi Amsaad, Ansagan Manasov, Salim
Hariri, Banu B. Yergaliyeva, and Aziz Alotaibi (2021). Blockchain-enabled deep recurrent neural network model for clickbait detection. IEEE Access 10: 3144-3163.
[9] Capuano, Nicola, Giuseppe Fenza, Vincenzo Loia, and Claudio Stanzione (2022). Explainable
artificial intelligence in cybersecurity: A survey. IEEE Access 10: 93575-93600.
[10] Ajmal, Abdul Basit, Shawal Khan, Masoom Alam, Abolfazl Mehbodniya, Julian Webber, and
Abdul Waheed (2023). Towards Effective Evaluation of Cyber Defense: Threat Based Adversary Emulation Approach. IEEE Access.
[11] Razaque, Abdul, Bandar Alotaibi, Munif Alotaibi, Shujaat Hussain, Aziz Alotaibi, and Vladimir Jotsov (2022). Clickbait detection using deep recurrent neural network. Applied Sciences 12, no. 1: 504.
[12] He, Ke, Dan Dongseong Kim, and Muhammad Rizwan Asghar (2023). Adversarial machine
learning for network intrusion detection systems: a comprehensive survey. IEEE Communications Surveys & Tutorials.
[13] Balla, Asaad, Mohamed Hadi Habaebi, Elfatih AA Elsheikh, Md Rafiqul Islam, and F. M. Suliman (2023). The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems. Sensors 23, no. 2: 758.
[14] Folino, Francesco, Gianluigi Folino, Massimo Guarascio, Francesco Sergio Pisani, and Luigi
Pontieri (2021). On learning effective ensembles of deep neural networks for intrusion
detection. Information Fusion 72: 48-69.
[15] Srilatha, Doddi, and Gopal K. Shyam (2021). Cloud-based intrusion detection using kernel
fuzzy clustering and optimal type-2 fuzzy neural network. Cluster Computing 24, no. 3:
2657-2672.
[16] Varun, P., Ashokkumar, K. (2022). Intrusion detection system in cloud security using deep
convolutional network. Appl. Math. Inf. Sci., 16, 581–588.
[17] Shyla, S.I., Sujatha, S.S. (2020). Cloud security: LKM and optimal fuzzy system for intrusion detection in cloud environment. J. Intell. Syst., 29, 1626–1642.
[18] Mishra, P.; Varadharajan, V.; Pilli, E.S.; Tupakula, U. (2018). Vmguard: A vmi-based security
architecture for intrusion detection in cloud environment. IEEE Trans. Cloud Comput., 8,
957–971.
[19] Aoudni, Y., Donald, C., Farouk, A., Sahay, K.B., Babu, D.V., Tripathi, V., Dhabliya, D. (2022).
Cloud security based attack detection using transductive learning integrated with Hidden
Markov Model. Pattern Recognit. Lett., 157, 16–26.
[20] Velliangiri, S., Premalatha, J. (2019). Intrusion detection of distributed denial of service attack in cloud. Clust. Comput., 22, 10615–10623.
[21] Sathiyadhas, S. S., Soosai Antony, M. C. V. (2022). A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network. International Journal of Adaptive Control and Signal Processing, 36, 1060–1076.
[22] Geetha, T. V., Deepa, A. J. (2022). A FKPCA-GWO WDBILSTM classifier for intrusion
detection system in cloud environments. Knowledge-Based Systems, 253, 109557.
[23] Ayachi, Y., Mellah, Y., Saber, M., Rahmoun, N., Kerrakchou, I., Bouchentouf, T. (2022). A survey and analysis of intrusion detection models based on information security and object technology-cloud intrusion dataset. IAES International Journal of Artificial Intelligence, 11(4), 1607.
[24] Zhang, B., Zhu, J., Su, H. (2023). Toward the third generation artificial intelligence. Science China Information Sciences, 66(2), 121101.
[25] Amitay, Y., Bussi, Y., Feinstein, B., Bagon, S., Milo, I., Keren, L. (2023). CellSighter: a neural network to classify cells in highly multiplexed images. Nature Communications, 14(1), 4302.
[26] Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., Bennamoun, M. (2022). Hands-on Bayesian neural networks—A tutorial for deep learning users. IEEE Computational Intelligence Magazine, 17(2), 29-48.
[27] Dasgupta, D., Akhtar, Z., Sen, S. (2022). Machine learning in cybersecurity: a comprehensive survey. The Journal of Defense Modeling and Simulation, 19(1), 57-106.
[28] Al-A’araji, N. H., Al-Mamory, S. O., Al-Shakarchi, A. H. (2021). Classification and clustering based ensemble techniques for intrusion detection systems: A survey. In Journal of Physics: Conference Series, 1818(1), 012106. IOP Publishing.
[29] Aslan, Ö., Aktuð, S. S., Ozkan-Okay, M., Yilmaz, A. A., Akin, E. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333. |
