A HYBRID FAULT-TOLERANT ENERGY MINIMIZATION TECHNIQUES FOR SECURITY ATTACKS IN WIRELESS SENSOR NETWORK

Abstract

Ensuring energy efficiency and attack identification in wireless sensor networks (WSNs) is critical due to the limited power resources of the sensor nodes. However, various security threats can compromise energy efficiency in WSNs. The proposed approach for improving energy efficiency by addressing three major security threats: Distributed Denial of Service (DDOS), wormhole attacks, and clone node attacks. Proposed a system for identifying and isolating malicious nodes that launch DDOS attacks, detecting and preventing wormhole attacks by leveraging location information, detecting and isolating clone nodes using a time-synchronization approach. Additionally, propose a node isolation and rerouting approach that enables the network to bypass nodes with low energy levels and reroute data through more energy-efficient nodes. Experiments demonstrate that our suggested strategy effectively boosts energy efficiency while preserving the WSN's security. The Proposed scheme has two new energy-efficient intrusion detection systems Energy Efficient Intrusion Detection System (EE-IDS) and Energy Efficient Intrusion Detection System with Energy Prediction (EE-IDSEP) are presented to safeguard wireless sensor networks from wormhole and DDOS assaults. In order to improve security against wormhole attacks and reduce the energy consumption of the sensor nodes in wireless sensor networks, the EE-IDS was developed. Ad hoc on-Demand Distance Vector (AODV), Shortcut Tree Routing (STR), and Opportunistic Shortcut Tree Routing (OSTR) are three distinct routing protocols that are used to evaluate the effectiveness of the EE-IDS. Assess and compare the Energy Efficient Trust System for Wormhole detection (EE-TSW) and Energy Efficient Trust System (EE-TS) for detecting DDOS assault using in-depth simulations with NS2. The simulation findings show that the suggested IDS, EE-IDS-AODV, EE-IDS-STR, and EE-IDS-OSTR, perform better for wormhole attack detection than the existing EE-TSW, while the suggested system, EE-IDSEP, performs better for DDOS attack detection than the existing EE-TS with the performance measures of Packet Delivery Ratio (PDR), Average End-to-End Delay, energy use, and detection. Clustering is a common Hierarchical network management strategy in Wireless Sensor Networks (WSN). Although separate clusters are often desired, several applications of inter-cluster routing, time synchronization, and node location make use of overlapping clusters. In overlapping clusters, replica node discovery is a iv difficult problem to solve. The first process identifies to reproduce by locating the position using Triangulation and RSSI (Received Signal Strength) methodology, while the secondary process uses RFID (Radio Frequency Identification) for distinctively identifying the item. The effectiveness of Line Chosen Multicast, Randomized Multicast, K-coverage WSN, and Fault Tolerant Virtual Backbone Tree (FTVBT) is compared with non-clustered and Multicast techniques. Due to its deterministic approach, the hybrid bat algorithm with differential equation (BA-DE) exhibits reduced communication overhead, a higher rate of detection, as well as lower storage costs, energy consumption, packet loss, and latency under a variety of scenarios. The purpose of data collection, sensor nodes should form clusters and congregate. In multi-hop sensor networks, the Base Station (sink) might not be considered. Due to this, the network may experience a hotspot issue. This paper examines the sleep & wakeup approach, which aims to increase packet delivery ratio (PDR) and energy conservation to extend network lifespan and avoid the hot spot (WLAN) issue. In this method, the C-H (cluster-head) is chosen depending on base station distance and energy level. Using this method, to increase energy conservation and improve the PDR compared to Fuzzy Clustering Algorithm (FCA). To identify the source of danger signals, the Fuzzy Misuse Detector Module (FMDM) and the danger detector module collaborate. The Fuzzy Q-learning Vaccination Modules (FQVM), which improve system capabilities, receive the infected sources and transmit them. To create the best defence methods, the Cooperative Decision Making Modules (Co-DMM) integrate the threat detector module and the Fuzzy Q-learning vaccination module.