Biblio

New technologies from day to day are submitted with many vulnerabilities that can make data exploitation. Nowadays, IoT is a target for Cybercrime attacks as it is one of the popular platforms in the century. This research address the IoT security problem by carried a medium-interaction honeypot. Honeypot is one of the solutions that can be done because it is a system feed for the introduction of attacks and fraudulent devices. This research has created a medium interaction honeypot using Cowrie, which is used to maintain the Internet of Things device from malware attacks or even attack patterns and collect information about the attacker's machine. From the result analysis, the honeypot can record all trials and attack activities, with CPU loads averagely below 6,3%.

Malware is a kind of software that, if installed on a malware victim's device, might carry malicious actions. The malicious actions might be data theft, system failure, or denial of service. Malware analysis is a process to identify whether a piece of software is a malware or not. However, with the advancement of malware technologies, there are several evasion techniques that could be implemented by malware developers to prevent analysis, such as polymorphic and oligomorphic. Therefore, this research proposes an automatic malware detection system. In the system, the malware characteristics data were obtained through both static and dynamic analysis processes. Data from the analysis process were classified using Naive Bayes algorithm to identify whether the software is a malware or not. The process of identifying malware and benign files using the Naive Bayes machine learning method has an accuracy value of 93 percent for the detection process using static characteristics and 85 percent for detection through dynamic characteristics.