EMD-SCS: A Dynamic Behavioral Approach for Early Malware Detection with Sonification of System Call Sequences

The privacy and security of users are increasingly threatened due to the rising frequency of malware assaults. Both Host-based Intrusion Detection Systems (HIDSes) and Antivirus software rely on signature-based or anomaly-based techniques for malware detection. However, the escalating diversity and sophistication of malware pose significant obstacles. In this research, we introduce EMD-SCS, an early malware detection methodology, employing sonification and system call sequence analysis. In our methodology, we interpret an executing program/process as a sequence of system calls, leveraging a Long Short-Term Memory network (LSTM) to hold a record of preceding system calls within this chain, consequently facilitating the prediction of future calls. After this prediction phase, the BLEU and hamming distance scores are utilized to classify the system call sequence. Importantly, these results are attained by analyzing just a small segment of the data for early prediction, which is crucial for a sonification-based approach as it enables us to notify administrators in advance of potential threats. This early warning system would allow admins to protect the host before a potential compromise. EMD-SCS uses sonification to convey the prediction outcomes using natural and animal sounds, offering a broader monitoring scope than visual observation. Evaluation results from the ADFA-LD dataset suggest that EMD-SCS surpasses prior techniques in early malware detection with an accuracy of 91.2%, a detection rate of 87.7%, and a false-positive rate of 15.3%, achieved by only processing 40% of the input system call sequences before they infiltrate the host.