{"id":5505,"date":"2023-08-25T18:35:47","date_gmt":"2023-08-25T18:35:47","guid":{"rendered":"https:\/\/getmyprojects.in\/projects\/?post_type=product&p=5505"},"modified":"2023-10-26T10:56:26","modified_gmt":"2023-10-26T05:26:26","slug":"behavioral-malware-detection-in-delay-tolerant-networks","status":"publish","type":"product","link":"https:\/\/getmyprojects.in\/projects\/product\/behavioral-malware-detection-in-delay-tolerant-networks\/","title":{"rendered":"Behavioral Malware Detection in Delay Tolerant Networks"},"content":{"rendered":"

The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware.<\/span><\/p>\n

In this project, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs (\u201cinsufficient evidence versus evidence collection risk\u201d and \u201cfiltering false evidence sequentially and distributedly\u201d), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of \u201cmalicious nodes sharing false evidence.\u201d Real mobile network traces are used to verify the effectiveness of the proposed methods.<\/span><\/p>\n