Abstract:

Computer and networking technologies play a significant role in our lives nowadays. Many of us rely on these technologies in our day-to-day activities, which include personal work, office work, organization, community work, education, transportation, and communications. Today, most of them discussions on network security tools or techniques used in protecting and defending networks. The traditional methods like firewall, URL filters, mainly focused on the filtering of data and may not sufficient to find all type of attacks always. Among numerous solutions, Intrusion detection systems (IDS) plays a major role in system security and also optimal system for detecting different kind of attacks. In order to stop hackers from harming computer systems, an ideal intrusion detection system can identify intrusions in real time. Different intrusion detection methods, each having advantages and disadvantages, can be used to construct intrusion detection systems. The IDS is being implemented using latest technologies such as Machine Learning Algorithms to classify the attacks and detecting them whenever an attack happens and also to find which machine learning algorithm is best suitable for identifying the attack. The paper presents an overview of the IDS and IPS, differences between IDS and IPS, classifications, methods and various aspects of traditional IDS and also discussed on Machine Learning based IDS, datasets for developing efficient and effective ML based IDS.

Description:

Cyber Attacks, Network Security, Intrusion Detection System, Intrusion Prevention System, Machine Learning.