Malware detection is a critical but very challenging task in cybersecurity. The eternal competition between malware authors (cyber attackers) and security analysts (detectors) is a never-ending game in which malware evolves rapidly and becomes more sophisticated as cyber attackers constantly evolve their tactics to evade detection. Such competition raises the demand for new automated malware detection techniques to keep pace with malware evolution and address sophisticated malware. This paper presents an empirical study that analyzes the effectiveness of static and dynamic features using machine learning algorithms. We propose FETA, a systematic approach for F eature E ngineering on anti-s T atic and anti-dyn A mic malware analysis. FETA combines static and dynamic features through feature aggregation and model integration techniques to improve detection accuracy and robustness. Extensive experiments on a real-world dataset show that the aggregation of static and dynamic features outperforms individual feature sets, achieving a detection rate of 98.06%. Additionally, we provide insights into feature selection and conduct a deep analysis of misclassified samples. This research contributes to the development of more effective and efficient malware detection techniques for enhanced cybersecurity.

Procurement is an essential operation of every organization regardless of its size or domain. As such, aggregating the demands could lead to better value-for-money due to: (1) lower bulk prices; (2) larger vendor tendering; (3) lower shipping and handling fees; and (4) reduced legal and administration overheads. This paper describes our experience in developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore with procurement expenditure to the scale of hundreds of millions of dollars annually. We formulate the demand aggregation problem using a bipartite graph model depicting the relationship between procured items and target vendors, and show that identifying maximal edge bicliques within that graph would reveal potential demand aggregation patterns. We propose an unsupervised learning methodology for efficiently mining such bicliques using a novel Monte Carlo subspace clustering approach. Based on this, a proof-of-concept prototype was developed and tested with the end users during 2017, and later trialed and iteratively refined, before being rolled out in 2019. The final performance was 71% of past cases transformed into bulk tenders correctly detected by the engine; for new opportunities pointed out by the engine 81% were deemed useful for potential bulk tender contracts in the future. Additionally, per each valid pattern identified, the engine achieved 100% precision (all aggregated purchase orders were correct), and 79% recall (the engine correctly identified 79% of orders that should have been put into the bulk tenders). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7 million annually.