Into the Unknown Unsupervised Machine Learning Algorithms for AnomalyBased Intrusion Detection









Abstract

Anomaly detection aims at identifying patterns in data that do not conform to the expected behavior, relying on machine-learning algorithms that are suited for binary classification. It has been arising as one of the most promising techniques to suspect intrusions, zero-day attacks and, under certain conditions, failures. This tutorial aims to instruct the attendees to the principles, application and evaluation of anomaly-based techniques for intrusion detection, with a focus on unsupervised algorithms, which are able to classify normal and anomalous behaviors without relying on input data with labeled attacks.


Modules


Algorithms


Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL