Browsing by Author "Kiah, Miss Laiha Mat"
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Article Citation Count: 9Anomaly Detection using Fuzzy Q-learning Algorithm(Budapest Tech, 2014) Mısra, Sanjay; Anuar, Nor Badrul; Kiah, Miss Laiha Mat; Misra, Sanjay; Computer EngineeringWireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. In this paper, we propose an intrusion detection system called Fuzzy Q-learning (FQL) algorithm to protect wireless nodes within the network and target nodes from DDoS attacks to identify the attack patterns and take appropriate countermeasures. The FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and CAIDA DDoS Attack datasets during the simulation experiments. Experimental results show that the proposed FQL IDS has higher accuracy of detection rate than Fuzzy Logic Controller and Q-learning algorithm alone.Article Citation Count: 79Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks(Academic Press Ltd- Elsevier Science Ltd, 2014) Mısra, Sanjay; Anuar, Nor Badrul; Kiah, Miss Laiha Mat; Rohani, Vala Ali; Petkovic, Dalibor; Misra, Sanjay; Khan, Abdul Nasir; Computer EngineeringDue to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FATS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods. (C) 2014 Elsevier Ltd. All rights reserved.