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Pattern Analysis and Regressive Linear Measure for botnet Detection

Computer Systems Science & Engineering 2022年第10期43卷 119-139页

作者： b.Padmavathi b.muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the bag of Features(boF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected boF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards botNets *** simulation is carried out in MATLAb environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： botNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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An efficient botnet detection approach based on feature learning and classification

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Journal of Control and Decision 2023年第1期10卷 40-53页

作者： b.Padmavathi b.muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Computer Science and Engineering Easwari Engineering CollegeChennaiIndia DMI College of Engineering ChennaiIndia

bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.

关键词： botnet machine learning Adaboost grey wolf optimisation exploration exploitation

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In-vitro study:Immunomodulatory and cytotoxicity effects of ethanolic leave extracts of Aegle marmelos and Ficus benghalensis

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Journal of Coastal Life Medicine 2016年第3期4卷 217-224页

作者： Winnyfred Crossia Hans-Uwe Dahms Krishnan muthukumar Thanamegam Kaviarasan Thiyagarajan Thirunalasundari Rathinam Arthur James Department of Marine Science Bharathidasan UniversityTiruchirappalli 620024Tamil NaduIndia Department of biomedical Science and Environmental biology Kaohsiung Medical UniversityNo.100Shin-Chuan 1st RoadKaohsiung 80708Taiwan Department of Marine biotechnology and Resources National Sun Yat-sen UniversityNo.70Lienhai RoadKaohsiung 80424Taiwan Department of Industrial biotechnology Bharathidasan UniversityTiruchirappalli 620024Tamil NaduIndia

Objective:To assess the immunomodulatory and antibacterial properties of the coastal trees Aegle marmelos(***)and Ficus benghalensis(***)by in-vitro ***:*** and *** leaves were extracted with the solvents di-ethyl ether,ethanol and *** extracts were investigated for antibacterial activity against human pathogenic bacteria and immunomodulatory activity against human peripheral blood mononuclear cells(PbMC),***:*** ethanol cold extract gave maximum inhibition zones against Escherichia coli NCIM 2931(b2)at 18 mm and enhanced the growth of human *** was non-toxic to human PbMC shown by the Tryphan blue dye exclusion method,sulforhodamine b and MTT *** phyto-chemical analysis demonstrated the presence of the functional groups:NH,CH_(2),CH_(3),CO,OH,-C-C=O,and *** confirmed the presence of quantifiable amounts of tannins and alkaloids with traces of phenolic compounds by thin layer chromatography ***:Leaves of *** and *** provide both antibacterial and immunomodulatory properties.

关键词： Antibacterial Immunomodulators Cytotoxicity Aegle marmelos Ficus benghalensis TLC

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