PeerAmbush: Multi-Layer Perceptron to Detect Peer-to-Peer Botnet

【Author】 Kabla, Arkan Hammoodi Hasan; Thamrin, Achmad Husni; Anbar, Mohammed; Manickam, Selvakumar; Karuppayah, Shankar

【Source】SYMMETRY-BASEL

【影响因子】2.940

【Abstract】Due to emerging internet technologies that mostly depend on the decentralization concept, such as cryptocurrencies, cyber attackers also use the decentralization concept to develop P2P botnets. P2P botnets are considered one of the most serious and challenging threats to internet infrastructure security. Consequently, several open issues still need to be addressed, such as improving botnet intrusion detection systems, because botnet detection is essentially a confrontational problem. This paper presents PeerAmbush, a novel approach for detecting P2P botnets using, for the first time, one of the most effective deep learning techniques, which is the Multi-Layer Perceptron, with certain parameter settings to detect this type of botnet, unlike most current research, which is entirely based on machine learning techniques. The reason for employing machine learning/deep learning techniques, besides data analysis, is because the bots under the same botnet have a symmetrical behavior, and that makes them recognizable compared to benign network traffic. The PeerAmbush also takes the challenge of detecting P2P botnets with fewer selected features compared to the existing related works by proposing a novel feature engineering method based on Best First Union (BFU). The proposed approach showed considerable results, with a very high detection accuracy of 99.9%, with no FPR. The experimental results showed that PeerAmbush is a promising approach, and we look forward to building on it to develop better security defenses.

【Keywords】P2P networks; P2P botnets; intrusion detection systems; feature engineering; Multi-Layer Perceptron; deep learning

【发表时间】2022 DEC

【收录时间】2023-01-14

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-区块链网络