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Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.
M. Prasad, S. Tripathi, and K. Dahal, A probability estimation-based feature reduction and Bayesian rough set approach for intrusion detection in mobile ad-hoc network, Appl. Intell., vol. 53, no. 6, pp. 7169–7185, 2023.
C. Edwin Singh and S. M. Celestin Vigila, Fuzzy based intrusion detection system in MANET, Meas. Sens., vol. 26, p. 100578, 2023.
M. Prasad, S. Tripathi, and K. Dahal, An enhanced detection system against routing attacks in mobile ad-hoc network, Wirel. Netw., vol. 28, no. 4, pp. 1411–1428, 2022.
T. K. Jebuer, An IDS based on modified chaos Elman’s neural network approaches for securing mobile ad hoc networks against DDoS attack, J. Discrete Math. Sci. Cryptogr., vol. 25, no. 8, pp. 2759–2764, 2022.
S. Ajjaj, S. El Houssaini, M. Hain, and M. A. El Houssaini, A new multivariate approach for real time detection of routing security attacks in VANETs, Information, vol. 13, no. 6, p. 282, 2022.
M. Prasad, S. Tripathi, and K. Dahal, An intelligent intrusion detection and performance reliability evaluation mechanism in mobile ad-hoc networks, Eng. Appl. Artif. Intell., vol. 119, p. 105760, 2023.
M. Mayuranathan, S. K. Saravanan, B. Muthusenthil, and A. Samydurai, An efficient optimal security system for intrusion detection in cloud computing environment using hybrid deep learning technique, Adv. Eng. Softw., vol. 173, p. 103236, 2022.
M. M. Rathore, A. Ahmad, and A. Paul, Real time intrusion detection system for ultra-high-speed big data environments, J. Supercomput., vol. 72, no. 9, pp. 3489–3510, 2016.
X. Zhang, L. Jiao, A. Paul, Y. Yuan, Z. Wei, and Q. Song, Semisupervised particle swarm optimization for classification, Math. Probl. Eng., vol. 2014, pp. 1–11, 2014.
S. Gbetondji Michoagan, S. M. Mali, and S. Gore, Salient features selection techniques for instruction detection in mobile ad hoc networks, Teh. Glas, no. Online, pp. 40–46, 2022.
R. Rai, K. G. Dhal, A. Das, and S. Ray, An inclusive survey on marine predators algorithm: Variants and applications, Arch. Comput. Methods Eng., vol. 30, no. 5, pp. 3133–3172, 2023.
A. Mughaid, S. AlZu’bi, A. Alnajjar, E. AbuElsoud, S. El Salhi, B. Igried, and L. Abualigah, Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches, Multimed. Tools Appl., vol. 82, no. 9, pp. 13973–13995, 2023.
E. Gyamfi and A. Jurcut, Intrusion detection in Internet of Things systems: A review on design approaches leveraging multi-access edge computing, machine learning, and datasets, Sensors, vol. 22, no. 10, p. 3744, 2022.
S. A. Asra, Security issues of vehicular ad hoc networks (VANET): A systematic review, TIERS Inf. Technol. J., vol. 3, no. 1, pp. 17–27, 2022.
S. Laqtib, K. El Yassini, and M. L. Hasnaoui, A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET, Int. J. Electr. Comput. Eng. IJECE, vol. 10, no. 3, p. 2701, 2020.
Z. Ali Khan and P. Herrmann, Recent advancements in intrusion detection systems for the Internet of Things, Secur. Commun. Netw., vol. 2019, pp. 1–19, 2019.
H. Moudni, M. Er-rouidi, H. Mouncif, and B. El Hadadi, Black Hole attack Detection using Fuzzy based Intrusion Detection Systems in MANET, Procedia Comput. Sci., vol. 151, no. C, pp. 1176–1181, 2019.
U. Ali Zardari, J. He, N. Zhu, K. H. Mohammadani, M. S. Pathan, M. I. Hussain, and M. Q. Memon, A dual attack detection technique to identify black and gray hole attacks using an intrusion detection system and a connected dominating set in MANETs, Future Internet, vol. 11, no. 3, pp. 1–17, 2019.
M. Prasad, S. Tripathi, and K. Dahal, Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection, Comput. Secur., vol. 99, p. 102062, 2020.
R. M. Hadi, S. H. Abdullah, and W. M. S. Abedi, Proposed neural intrusion detection system to detect denial of service attacks in MANETs, Period. Eng. Nat. Sci. PEN, vol. 10, no. 3, p. 70, 2022.
R. Meddeb, F. Jemili, B. Triki, and O. Korbaa, A deep learning-based intrusion detection approach for mobile Ad-hoc network, Soft Comput. A Fusion Found. Methodol. Appl., vol. 27, no. 14, pp. 9425–9439, 2023.
J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider, and A. Wahab, A review of intrusion detection systems using machine and deep learning in Internet of Things: Challenges, solutions and future directions, Electronics, vol. 9, no. 7, p. 1177, 2020.
M. Ramezani, D. Bahmanyar, and N. Razmjooy, A new improved model of marine predator algorithm for optimization problems, Arab. J. Sci. Eng., vol. 46, no. 9, pp. 8803–8826, 2021.
D. S. A. Elminaam, A. Nabil, S. A. Ibraheem, and E. H. Houssein, An efficient marine predators algorithm for feature selection, IEEE Access, vol. 9, pp. 60136–60153, 2021.
A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, Marine predators algorithm: A nature-inspired metaheuristic, Expert Syst. Appl., vol. 152, p. 113377, 2020.
P. Kumar, G. P. Gupta, and R. Tripathi, Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for IoT networks, Arab. J. Sci. Eng., vol. 46, no. 4, pp. 3749–3778, 2021.
Y. Farhaoui, Design and implementation of an intrusion prevention system, Int. J. Netw. Secur., vol. 19, no. 5, pp. 675–683, 2017.
Y. Farhaoui, Intrusion prevention system inspired immune systems, Indones. J. Electr. Eng. Comput. Sci., vol. 2, no. 1, p. 168, 2016.
S. S. Alaoui, Y. Farhaoui, and B. Aksasse, Hate speech detection using text mining and machine learning, Int. J. Decis. Support. Syst. Technol., vol. 14, no. 1, pp. 1–20, 2022.
S. S. Alaoui, Y. Farhaoui, and B. Aksasse, Data openness for efficient e-governance in the age of big data, Int. J. Cloud Comput., vol. 10, no. 5/6, p. 522, 2021.
Y. Farhaoui, Securing a local area network by IDPS open source, Procedia Comput. Sci., vol. 110, pp. 416–421, 2017.
This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
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