Biblio

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.

The IoT industry has developed rapidly in recent years, which has attracted the attention of security researchers. However, the researchers are hampered by the wide variety of IoT device operating systems and their hardware architectures. Especially for the lightweight IoT devices, many manufacturers do not provide the device firmware images, embedded firmware source code or even the develop documents. As a result, it hinders traditional static analysis and dynamic analysis techniques. In this paper, we propose a novel dynamic analysis framework, called FIoT, which aims at finding memory corruption vulnerabilities in lightweight IoT device firmware images. The key idea is dynamically run the binary code snippets through symbolic execution with carrying out a fuzzing test. Specifically, we generate code snippets through traversing the control-flow graph (CFG) in a backward manner. We improved the CFG recovery approach and backward slice approach for better performance. To reduce the influence of the binary firmware, FIoT leverages loading address determination analysis and library function identification approach. We have implemented a prototype of FIoT and conducted experiments. Our results show that FIoT can complete the Fuzzing test within 40 seconds in average. Considering 170 seconds for static analysis, FIoT can load and analyze a lightweight IoT firmware within 210 seconds in total. Furthermore, we illustrate the effectiveness of FIoT by applying it over 115 firmware images from 17 manufacturers. We have found 35 images exist memory corruptions, which are all zero-day vulnerabilities.