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Real-Time Network Intrusion Prevention System Using Incremental Feature Generation

Computers, Materials & Continua 2022年第1期70卷 1631-1648页

作者： Yeongje Uhm wooguil pak Research and Business Development Foundation Yeungnam UniversityGyeongsanGyeongbuk38541Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsanGyeongbuk38541Korea

Security measures are urgently required to mitigate the recent rapid increase in network security *** methods employing machine learning have been researched and developed to detect various network attacks effectively,these are passive approaches that cannot protect the network from attacks,but detect them after the end of the *** such passive approaches cannot provide fundamental security solutions,we propose an active approach that can prevent further damage by detecting and blocking attacks in real time before the session *** proposed technology uses a two-level classifier structure:the first-stage classifier supports real-time classification,and the second-stage classifier supports accurate ***,the proposed approach can be used to determine whether an attack has occurred with high accuracy,even under heavy *** extensive evaluation,we confirm that our approach can provide a high detection rate in real ***,because the proposed approach is fast,light,and easy to implement,it can be adopted in most existing network security ***,we hope to mitigate the limitations of existing security systems,and expect to keep networks faster and safer from the increasing number of cyber-attacks.

关键词： Network intrusion detection network intrusion prevention realtime two-level classifier

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An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

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Computers, Materials & Continua 2022年第11期73卷 3921-3949页

作者： Murtaza Ahmed Siddiqi wooguil pak Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The network infrastructure has evolved rapidly due to the everincreasing volume of users and *** massive number of online devices and users has forced the network to transform and facilitate the operational necessities of *** these necessities,network security is of prime *** intrusion detection systems(NIDS)are among the most suitable approaches to detect anomalies and assaults on a ***,keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the *** paper presents an effective and prevalent framework for NIDS by merging image processing with convolution neural networks(CNN).The proposed framework first converts non-image data from network traffic into images and then further enhances those images by using the Gabor *** images are then classified using a CNN *** assess the efficacy of the recommended method,four benchmark datasets i.e.,CSE-CIC-IDS2018,CIC-IDS-2017,ISCX-IDS 2012,and NSL-KDD were *** proposed approach showed higher precision in contrast with the recent work on the mentioned ***,the proposed method is compared with the recent well-known image processing methods for NIDS.

关键词： Anomaly detection convolution neural networks deep learning image processing intrusion detection network intrusion detection

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High Performance Classification of Android Malware Using Ensemble Machine Learning

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Computers, Materials & Continua 2022年第7期72卷 381-398页

作者： Pagnchakneat C.Ouk wooguil pak Department of Computer Engineering Keimyung UniversityDaegu42601Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsanGyeongbuk38541Korea

Although Android becomes a leading operating system in market,Android users suffer from security threats due to *** protect users from the threats,the solutions to detect and identify the malware variant are ***,modern malware evades existing solutions by applying code obfuscation and native *** resolve this problem,we introduce an ensemble-based malware classification algorithm using malware family *** proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total *** also adopts unified feature extraction technique for handling seamless both bytecode and native *** propose a unique feature selection algorithm that improves classification performance and time simultaneously.2-gram based features are generated from the instructions and segments,and then selected by using multiple filters to choose most effective *** extensive simulation with many obfuscated and native code malware applications,we confirm that it can classify malwares with high accuracy and short processing *** existing approaches failed to achieve classification speed and detection time ***,the approach can help Android users to keep themselves safe from various and evolving cyber-attacks very effectively.

关键词： Android malware classification family grouping native code obfuscation unified feature extraction

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Unified Detection of Obfuscated and Native Android Malware

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Computers, Materials & Continua 2022年第2期70卷 3099-3116页

The Android operating system has become a leading smartphone platform for mobile and other smart devices,which in turn has led to a diversity of malware *** amount of research on Android malware detection has increased significantly in recent years and many detection systems have been *** these efforts,however,most systems can be thwarted by sophisticated Androidmalware adopting obfuscation or native code to avoid discovery by anti-virus *** this paper,we propose a new static analysis technique to address the problems of obfuscating and native malware *** proposed system provides a unified technique for extracting features from applications and native libraries using a selection algorithm that can extract a small set of unique and effective features for detecting malware applications rapidly and with a high detection *** using large Android malware detection datasets obtained from various sources confirmed that the proposed approach achieves very promising results in terms of improved accuracy,low false positive rate,and high detection rate.

关键词： Android malware detection native code obfuscation unified feature extraction

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