Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET
Not on the map. Why?
Abstract (arXiv)
Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Apr 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 6.
6 papers.
- 2023Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detectioncited by 3
- 2023Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methodscited by 0
- 2023Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimizationcited by 0
- 2023Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processingcited by 0
- 2023Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniquescited by 0
- 2023IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Supportcited by 0
- 2023IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support7 Nov 2023
- 2023Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection2 Sep 2023
- 2023Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniques11 May 2023
- 2023Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing8 May 2023
- 2023Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization30 Apr 2023
- 2023Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods29 Apr 2023