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BotSward: Centrality Measures for graph-based Bot Detection Using Machine Learning

Computers, Materials & Continua 2023年第1期74卷 693-714页

作者： Khlood Shinan Khalid Alsubhi M.Usman Ashraf Department of Computer Science College Computer Science in Al-LeithUmm Al-Qura UniversityMecca 21421Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science GC Women University SialkotPakistan

The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the *** detection using machine learning(ML)with flow-based features has been extensively studied in the *** flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious ***,graph-based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network *** purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and *** propose BotSward,a graph-based bot detection system that is based on *** apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the *** efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,*** is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art *** proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.

关键词： Network security botnet detection graph-based features machine learning measure centrality

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A Unique Discrete Wavelet&Deterministic Walk-based Glaucoma Classification Approach Using Image-Specific Enhanced Retinal Images

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Computer Systems Science & Engineering 2023年第10期47卷 699-720页

作者： Krishna Santosh Naidana Soubhagya Sankar Barpanda School of Computer Science and Engineering VIT-AP UniversityInavoluBeside AP SecretariatAmaravati-522237APIndia

Glaucoma is a group of ocular atrophy diseases that cause progressive vision loss by affecting the optic *** of its asymptomatic nature,glaucoma has become the leading cause of human blindness *** this paper,a novel computer-aided diagnosis(CAD)approach for glaucomatous retinal image classification has been *** extracts graph-based texture features from structurally improved fundus images using discrete wavelet-transformation(DWT)and deterministic tree-walk(DTW)*** images are considered from both public repositories and eye *** are enhanced with image-specific luminance and gradient transitions for both contrast and texture *** enhanced images are mapped into undirected graphs using DTW trajectories formed by the image’s wavelet ***-based features are extracted fromthese graphs to capture image texture *** learning(ML)classifiers use these features to label retinal *** approach has attained an accuracy range of 93.5%to 100%,82.1%to 99.3%,95.4%to 100%,83.3%to 96.6%,77.7%to 88.8%,and 91.4%to 100%on the ACRIMA,ORIGA,RIM-ONE,Drishti,HRF,and HOSPITAL datasets,*** major strength of this approach is texture pattern identification using various topological *** has achieved optimal performance with SVM and RF classifiers using biorthogonal DWT combinations on both public and patients’fundus *** classification performance of the DWT-DTW approach is on par with the contemporary state-of-the-art methods,which can be helpful for ophthalmologists in glaucoma screening.

关键词： Wavelet-transformation glaucoma classification deterministic tree walk graph-based features

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