J. K. Kalita

Parthajit Borah, Sakshi Singh, D. K. Bhattacharyya et al.

As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware. These patterns of sophisticated malware can be obtained with the help of Function Call Graphs (FCGs). However, to effectively cover numerous groups of families of malware, it is required to have a sufficiently large dataset for the system to operate on. In order to ensure accuracy and robustness of the system, the dataset should comprise samples of different malwares and a benign application for secure execution of the detection process. This paper introduces AMD-FCG, an enhanced Function Call Graph dataset integrated with topological features of malwares. The framework enhances the detection procedure, streamlining the workflow for cybersecurity professionals and also eliminating the need for dynamic analysis and extensive processing. Therefore, it can be used to develop and deploy more efficient and innovative malware detection systems.

Parthajit Borah, Upasana Sarmah, D. K. Bhattacharyya et al.

Malware and malware-based attacks are becoming more prevalent and complex. Attackers regularly come up with new techniques that have the ability to evade conventional and signature-based malware defense. In order to address such threats, there is an increasing demand for advanced and better defense solutions. Machine learning-based techniques are efficiently capable of defending against malware and malware-based attacks. Nevertheless, creating and efficiently testing such techniques demand high-quality datasets having samples of various malware families as well as goodware. The lack of such datasets continues to be a major bottleneck in malware research. In this paper, we introduce TUANDROMD-X, a multiclass malware dataset with visual and entropy-based features of each sample, distinctly identifying malware from goodware. The dataset is created based on static analysis, lowering the overhead that comes with high feature engineering and dynamic analysis. As a result, TUANDROMD-X facilitates researchers and cyber-security experts to design faster and better malware detection systems.