English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is needed to ensure the integrity of systems that process sensitiveinformation and control many aspects of everyday life. We examine the use ofmachine learning algorithms to detect malware using the system calls generatedby executables-alleviating attempts at obfuscation as the behavior is monitoredrather than the bytes of an executable. We examine several machine learningtechniques for detecting malware including random forests, deep learningtechniques, and liquid state machines. The experiments examine the effects ofconcept drift on each algorithm to understand how well the algorithmsgeneralize to novel malware samples by testing them on data that was collectedafter the training data. The results suggest that each of the examined machinelearning algorithms is a viable solution to detect malware-achieving between90% and 95% class-averaged accuracy (CAA). In real-world scenarios, theperformance evaluation on an operational network may not match the performanceachieved in training. Namely, the CAA may be about the same, but the values forprecision and recall over the malware can change significantly. We structureexperiments to highlight these caveats and offer insights into expectedperformance in operational environments. In addition, we use the induced modelsto gain a better understanding about what differentiates the malware samplesfrom the goodware, which can further be used as a forensics tool to understandwhat the malware (or goodware) was doing to provide directions forinvestigation and remediation.

Dynamic Analysis of Executables to Detect and Characterize Malware