Machine Learning For HTTP Botnet Detection Using Classifier Algorithms

Recently,HTTP based Botnet threat has become a serious problem for computer security experts as bots can infect victim’s computer quick and stealthily.By using HTTP protocol,Bots are able to hide their communication flow within normal HTTP communications.In addition,since HTTP protocol is widely used by internet application,it is not easy to block this service as a precautionary approach. Thus,it is needed for expert finding ways to detect the HTTP Botnet in network traffic effectively.In this paper, we propose to implement machine learning classifiers,to detect HTTP Botnets.Network traffic dataset used in this research is extracted based on TCP packet feature.We also able to find the best machine learning classifier in our experiment.The proposed method is able to classify HTTP Botnet in network traffic using the best classifier in the experiment with an average accuracy of 92.93%

Formulating Generalize Malware Attack Pattern Using Features Selection

Malicious software or malware activity is increasingly threatened the network security as the malicious codes can be easily obtained and can be used as a weapon to gain illegal objectives. Hence, network traffic gathered from a control experiment are explored and features selection method is used to identify the features involved in formulating the malware attack pattern. This paper proposes generalize malware attack pattern in two perspectives which is attacker and victim using traditional worm. This research shall facilitate the authorities in detecting the malware intrusion activities in cyber space while protecting the Critical National Information Infrastructure (CNII) in the country. These generalized malware attack pattern can be extended into research areas in alert correlation and computer forensic investigation

Machine Learning for HTTP Botnet Detection Using Classifier Algorithms

Recently, HTTP based Botnet threat has become a serious problem for computer security experts as bots can infect victim’s computer quick and stealthily. By using HTTP protocol, Bots are able to hide their communication flow within normal HTTP communications. In addition, since HTTP protocol is widely used by internet application, it is not easy to block this service as a precautionary approach. Thus, it is needed for expert finding ways to detect the HTTP Botnet in network traffic effectively. In this paper, we propose to implement machine learning classifiers, to detect HTTP Botnets. Network traffic dataset used in this research is extracted based on TCP packet feature. We also able to find the best machine learning classifier in our experiment. The proposed method is able to classify HTTP Botnet in network traffic using the best classifier in the experiment with an average accuracy of 92.93%