Machine Learning Classification on Behavior-Based Security Alerts : A Comparative Study of Three Algorithms
Inom cybers ̈akerhetsbranschen pl ̊agas s ̈akerhetsanalytiker av ett stort antal falska positiva varningar. Detta tar tid och resurser och g ̈or s ̈akerhetsanalytiker mer ben ̈agna att f ̈orbise verkliga s ̈akerhetshot. I samarbete med Orange Cyberdefense unders ̈oker detta examensar- bete f ̈orm ̊agan hos tre maskininl ̈arningsalgoritmer, Decision Trees, Naive Bayes och Support Vector Machines (SIn the cybersecurity industry, security analysts are plagued by a high number of false positive alerts of various types. This takes up time and resources, and makes security analysts more prone to overlook true security threats. In collaboration with Orange Cyberdefense, this thesis investigates the ability of three machine learning algo- rithms, Decision Trees, Naive Bayes and Support Vector Mach
