With various malware, botnets are the legitimate risk increasing against cybersecurity providing criminal operations like malware dispersal, distributed denial of service attacks, fraud clicking, phishing, and identification of theft.Existing techniques used for detection of botnet, which are suitable only for specific command of botnet and protocol for controlling and do not support botnet detection at earlier stages.In several computer security defense systems, honeypots are deployed successfully purple and black wigs by security defenders.As honeypots can attract botnet compromises and expose spies in botnet membership and behaviors of the attacker, they are broadly employed in botnet defense.
Thus, attackers whose role is to construct and maintain botnets have to determine honeypot trap avoiding methods.To handle the issues related to botnet attacks, machine learning techniques are used to support detection and prevent bot attacks.An Ensemble Classifier Algorithm with Stacking Process (ECASP) is proposed in this paper to select optimal features fed as input to the machine learning classifiers to estimate the botnet detection performance.As a result, the method achieves proposed achieves 94.
08% gtech brush bar accuracy, 86.5% sensitivity, 85.68% specificity, and 78.24% F-measure.