Abstract—Mobile Ad hoc Networks (MANETs) are characterized by their dynamic topology, limited bandwidth, and unpredictable link conditions, which make reliable packet delivery a significant challenge. Regression techniques have been previously utilized in Mobile Ad hoc Networks (MANET) with a high success rate. However, a modification of Lasso regression is still required to effectively mitigate attacks while improving service quality. In our research paper, we developed an innovative method that integrates a modified Lasso regression analysis with a regularization term based on absolute deviation. By comparing the modified Lasso regression algorithm with other regression techniques, such as linear regression, ridge regression, Least Absolute Deviation (LAD) regression, Method of Least Square (MLS) regression, and Ridge regression, we demonstrate its superior performance over the others. During the Wormhole attack, we attempted to enhance the packet delivery ratio, reduce delay variance, etc., using our algorithm. It was discovered that the Absolute Deviated Lasso Regression (ADLR) algorithm outperformed others significantly when dealing with large datasets. Through experimental evaluation, we have demonstrated that our modified algorithm significantly reduces errors and maximizes the packet delivery fraction by more than 90% compared to other regression techniques. The highest packet delivery ratio reaches 92%, while the lowest packet loss ratio is 24% when using our ADLR algorithm.
 
Keywords—Adhoc on Demand (AODV), Packet Delivery Ratio (PDR), throughput, wormhole


Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.