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To enhance the efficiency of incident response triage operations, it is notcost-effective to defend all systems equally in a complex cyber environment.Instead, prioritizing the defense of critical functionality and the mostvulnerable systems is desirable. Threat intelligence is crucial for guidingSecurity Operations Center (SOC) analysts' focus toward specific systemactivity and provides the primary contextual foundation for interpretingsecurity alerts. This paper explores novel approaches for improving incidentresponse triage operations, including dealing with attacks and zero-daymalware. This solution for rapid prioritization of different malware have beenraised to formulate fast response plans to minimize socioeconomic damage fromthe massive growth of malware attacks in recent years, it can also be extendedto other incident response. We propose a malware triage approach that canrapidly classify and prioritize different malware classes to address thisconcern. We utilize a pre-trained ResNet18 network based on Siamese NeuralNetwork (SNN) to reduce the biases in weights and parameters. Furthermore, ourapproach incorporates external task memory to retain the task information ofpreviously encountered examples. This helps to transfer experience to newsamples and reduces computational costs, without requiring backpropagation onexternal memory. Evaluation results indicate that the classification aspect ofour proposed method surpasses other similar classification techniques in termsof performance. This new triage strategy based on task memory withmeta-learning evaluates the level of similarity matching across malware classesto identify any risky and unknown malware (e.g., zero-day attacks) so that adefense of those that support critical functionality can be conducted.

Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework