The widespread use and the immense user base make Windows systems a prime target for attackers seeking to exploit vulnerabilities and maximize impact. Modern obfuscation techniques enable malware to evade detection tools, allowing it to intrude on systems and carry out malicious activities. Thus, to prevent potential harm to the victim's system, it is crucial to detect malware at the early execution stage and initiate adequate action. However, there is often a trade-off between accuracy and earliness in malware detection, as detecting threats at earlier stages may sometimes come at the cost of reduced detection accuracy. We introduce an early malware detection framework that balances this trade-off. Our proposed framework iteratively constructs API call prefix subsequence and applies security-sensitive embedding using API call parameters. The causal sequence encoder transforms these sequences into contextual vectors, which are then classified by a multi-layer perceptron. The proposed framework not only outperforms the state-of-the-art early malware detection method EarlyMalDetect but also demonstrates comparable performance to post-sequence malware detection methods like CTIMD and BD-MDLC, achieving accurate maliciousness prediction on or before analyzing just 3 % of the API sequence.

Packet based Network-on-Chip (NoC) connect tens to hundreds of components in a multi-core system. The routing and arbitration policies employed in traditional NoCs treat all application packets equally. However, some packets are critical as they stall application execution whereas others are not. We differentiate packets based on a metric called slack that captures a packet's criticality. We observe that majority of NoC packets generated by standard application based benchmarks do not have slack and hence are critical. Prioritising these critical packets during routing and arbitration will reduce application stall and improve performance. We study the diversity and interference of packets to propose a policy that prioritises critical packets in NoC. This paper presents a slack-aware re-routing (SAR) technique that prioritises lower slack packets over higher slack packets and explores alternate minimal path when two no-slack packets compete for same output port. Experimental evaluation on a 64-core Tiled Chip Multi-Processor (TCMP) with 8×8 2D mesh NoC using both multiprogrammed and multithreaded workloads show that our proposed policy reduces application stall time by upto 22% over traditional round-robin policy and 18% over state-of-the-art slack-aware policy.