BBBC-DDRL: A hybrid big-bang big-crunch optimization and deliberated deep reinforced learning mechanisms for cyber-attack detection

Dixit, 2021, Deep learning algorithms for cybersecurity applications: a technological and status review, Comput Sci Rev, 39, 10.1016/j.cosrev.2020.100317 Ferrag, 2021, Federated deep learning for cyber security in the internet of things: concepts, applications, and experimental analysis, IEEE Access, 9, 138509, 10.1109/ACCESS.2021.3118642 Salih, 2021, A survey on the role of artificial intelligence, machine learning and deep learning for cybersecurity attack detection, 61 Manvith, 2021, A performance comparison of machine learning approaches on intrusion detection dataset, 782 Dong, 2020, An intrusion detection model for wireless sensor network based on information gain ratio and bagging algorithm, Int J Netw Secur, 22, 218 Aburomman, 2017, A survey of intrusion detection systems based on ensemble and hybrid classifiers, Comput Security, 65, 135, 10.1016/j.cose.2016.11.004 Yusof, 2022, Cyber attack detection dataset: a review Ahsan, 2021, Enhancing machine learning prediction in cybersecurity using dynamic feature selector, J Cybersecurity Privacy, 1, 199, 10.3390/jcp1010011 Sivaprasad, 2018, Machine learning based traffic classification using statistical analysis, Int J Recent Innov Trends Comput Commun, 6, 187 Sivaprasad, 2017, Secured proactive network forensic framework, 695 Mohammed, 2021, Iot cyber-attack detection: a comparative analysis, 117 Kumar, 2021, Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for iot networks, Arab J Sci Eng, 46, 3749, 10.1007/s13369-020-05181-3 Miao, 2021, Machine learning–based cyber attacks targeting on controlled information: a survey, ACM Comput Surv (CSUR), 54, 1, 10.1145/3465171 Moustafa, 2019, New generations of internet of things datasets for cybersecurity applications based machine learning: tON_IoT datasets, 21 Salloum, 2020, Machine learning and deep learning techniques for cybersecurity: a review, 50 Shaukat, 2020, Performance comparison and current challenges of using machine learning techniques in cybersecurity, Energies, 13, 2509, 10.3390/en13102509 Shaukat, 2020, A survey on machine learning techniques for cyber security in the last decade, IEEE Access, 8, 222310, 10.1109/ACCESS.2020.3041951 Annamalai, 2022, Combinatorial and multinomial coefficients and its computing techniques for machine learning and cybersecurity, J Eng Exact Sci, 8, 10.18540/jcecvl8iss8pp14713-01i Nagpal, 2022, A feature reduced intrusion detection system with optimized SVM using big bang big crunch optimization, Wireless Personal Commun, 122, 1939, 10.1007/s11277-021-08975-2 Alterazi, 2022, Prevention of cyber security with the internet of things using particle swarm optimization, Sensors, 22, 6117, 10.3390/s22166117 Li, 2018, Cyber security meets artificial intelligence: a survey, Front Inf Technol Electron Eng, 19, 1462, 10.1631/FITEE.1800573 Mendonca, 2022, A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms, Expert Syst, 39, e12917, 10.1111/exsy.12917 Tiwari, 2021, An enhanced intelligent model: to protect marine IoT sensor environment using ensemble machine learning approach, Ocean Eng, 242, 10.1016/j.oceaneng.2021.110180 Liu, 2020, Anomaly detection on IoT network intrusion using machine learning, 1 Abdullahi, 2022, Detecting cybersecurity attacks in internet of things using artificial intelligence methods: a systematic literature review, Electronics, 11, 198, 10.3390/electronics11020198