Biblio

Throughout the years, honeypots have been very useful in tracking down attackers and preventing different types of cyber attacks on a very large scale. It's been almost 3 decades since the discover of honeypots and still more than 80% of the companies rely on this system because of intrusion detection features and low false positive rate. But with time, the attackers tend to start discovering loopholes in the system. Hence it is very important to be up to date with the technology when it comes to protecting a computing device from the emerging cyber attacks. Timely advancements in the security model provided by the honeypots helps in a more efficient use of the resource and also leads to better innovations in that field. The following paper reviews different methods of honeypot network and also gives an insight about the problems that those techniques can face along with their solution. Further it also gives the detail about the most preferred solution among all of the listed techniques in the paper.

Nowadays, Windows is an operating system that is very popular among people, especially users who have limited knowledge of computers. But unconsciously, the security threat to the windows operating system is very high. Security threats can be in the form of illegal exploitation of the system. The most common attack is using malware. To determine the characteristics of malware using dynamic analysis techniques and static analysis is very dependent on the availability of malware samples. Honeypot is the most effective malware collection technique. But honeypot cannot determine the type of file format contained in malware. File format information is needed for the purpose of handling malware analysis that is focused on windows-based malware. For this reason, we propose a framework that can collect malware information as well as identify malware PE file type formats. In this study, we collected malware samples using a modern honey network. Next, we performed a feature extraction to determine the PE file format. Then, we classify types of malware using VirusTotal scanning. As the results of this study, we managed to get 1.222 malware samples. Out of 1.222 malware samples, we successfully extracted 945 PE malware. This study can help researchers in other research fields, such as machine learning and deep learning, for malware detection.