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

Existing malware detection approaches suffer from a simplistic trade-offbetween false positive rate (FPR) and true positive rate (TPR) due to a singletier classification approach, where the two measures adversely affect oneanother. The practical implication for malware detection is that FPR must bekept at an acceptably low level while TPR remains high. To this end, we proposea two-tiered learning, called ``Echelon", from raw byte data with no need forhand-crafted features. The first tier locks FPR at a specified target level,whereas the second tier improves TPR while maintaining the locked FPR. The coreof Echelon lies at extracting activation information of the hidden layers offirst tier model for constructing a stronger second tier model. Echelon is aframework in that it allows any existing CNN based model to be adapted in bothtiers. We present experimental results of evaluating Echelon by adapting thestate-of-the-art malware detection model ``Malconv" in the first and secondtiers.

Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms