Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security  

作　　者：M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui 

机构地区：[1]Department of Electronics and Communication Engineering,Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology,Chennai 600069,India [2]Department of Computer Science and Engineering,Saveetha School of Engineering,SIMATS,Chennai 602105,India [3]JNIAS School of Planning and Architecture,Hyderabad 500034,India [4]Department of Computer Science and Engineering,NPR College of Engineering and Technology,Natham Dindigul 624401,India [5]Department of Computer Science and Engineering,PSNA College of Engineering and Technology,Poolangulathupatti 620009,India [6]Department of Computer Science and Engineering,Sri Muthukumaran Institute of Technology,Chennai 600069,India [7]Department of Computer Science,Moulay Ismail University,Meknes 5003,Morocco

出　　处：《Intelligent and Converged Networks》2024年第1期1-18,共18页智能与融合网络（英文）

摘　　要：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.

关 键 词：Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]