Sökning: "Detektering av cyberattacker"

Hittade 3 uppsatser innehållade orden Detektering av cyberattacker.

1. 1. Cyber Threat Detection using Machine Learning on Graphs : Continuous-Time Temporal Graph Learning on Provenance Graphs

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jakub Reha; [2023]
   Nyckelord :Graph neural networks; Temporal graphs; Benchmark datasets; Anomaly detection; Heterogeneous graphs; Provenance graphs; Grafiska neurala nätverk; temporala grafer; benchmark-datauppsättningar; anomalidetektering; heterogena grafer; härkomstgrafer;

   Sammanfattning : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. LÄS MER

3. 2. Data-driven cyberattack detection for microgrids

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jiaying Mao; [2022]
   Nyckelord :Cyberattack detection; Deep learning network; Microgrid; Smart inverter; Cyber-physical system; Distributed system; Detektering av cyberattacker; Djupinlärning nätverk; Microgrid; Smart växelriktare; Cyberfysiska system; Distribuerade system;

   Sammanfattning : Microgrids are undergoing higher penetrations of renewables and associated power electronics, along with precise and sophisticated control and communication networks. However, such cyber-physical systems might suffer from potential cybersecurity threats and inherent low inertia. LÄS MER

4. 3. Information-Theoretic Framework for Network Anomaly Detection: Enabling online application of statistical learning models to high-speed traffic

   Master-uppsats, KTH/Matematisk statistik

   Författare :Gabriel Damour; [2019]
   Nyckelord :Network Security; Distributed Denial of Service; DDoS; DoS; Anomaly Detection; Intrusion Detection; Attack Source Identification; Information Theory; Statistical Learnin; Nätverkssäkerhet; Distribuerad Överbelastningsattack; DDoS; DoS; Anomalidetektering; Intrångsdetektering; Identifiering av Attackkällor; Informationsteori; Maskininlärning;

   Sammanfattning : With the current proliferation of cyber attacks, safeguarding internet facing assets from network intrusions, is becoming a vital task in our increasingly digitalised economies. Although recent successes of machine learning (ML) models bode the dawn of a new generation of intrusion detection systems (IDS); current solutions struggle to implement these in an efficient manner, leaving many IDSs to rely on rule-based techniques. LÄS MER