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

Recently, advanced cyber attacks, which consist of a sequence of steps thatinvolve many vulnerabilities and hosts, compromise the security of manywell-protected businesses. This has led to the solutions that ubiquitouslymonitor system activities in each host (big data) as a series of events, andsearch for anomalies (abnormal behaviors) for triaging risky events. Sincefighting against these attacks is a time-critical mission to prevent furtherdamage, these solutions face challenges in incorporating expert knowledge toperform timely anomaly detection over the large-scale provenance data.  To address these challenges, we propose a novel stream-based query systemthat takes as input, a real-time event feed aggregated from multiple hosts inan enterprise, and provides an anomaly query engine that queries the event feedto identify abnormal behaviors based on the specified anomalies. To facilitatethe task of expressing anomalies based on expert knowledge, our system providesa domain-specific query language, SAQL, which allows analysts to express modelsfor (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-basedanomalies, and (4) outlier-based anomalies. We deployed our system in NEC LabsAmerica comprising 150 hosts and evaluated it using 1.1TB of real systemmonitoring data (containing 3.3 billion events). Our evaluations on a broad setof attack behaviors and micro-benchmarks show that our system has a lowdetection latency (<2s) and a high system throughput (110,000 events/s;supporting ~4000 hosts), and is more efficient in memory utilization than theexisting stream-based complex event processing systems.

SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection