Pongsakorn Tatongjai1, Tossapon Boongoen2,*, Natthakan Iam-On2, Nitin Naik3, Longzhi Yang4
CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2479-2498, 2023, DOI:10.32604/cmc.2023.024858
Abstract As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that… More >
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