Intrusion Detection System Using Machine Learning

• Published in: International Conference on Computer Communication and Informatics (Online) pp. 1 - 4
• Main Authors: Kiran, Ajmeera, Prakash, S. Wilson, Kumar, B Anand, Likhitha, Sameeratmaja, Tammana, Charan, Ungarala Satya Surya Ram
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.01.2023
• Subjects: Computers; Hardware; Host; Host Intrusion Detection System; Intrusion detection; Intrusion Detection System; Intrusion Prevention System; Machine learning; Network; Network intrusion detection; Network Intrusion Detection System; Software; Support vector machine; Support vector machines
• ISSN: 2473-7577
• DOI: 10.1109/ICCCI56745.2023.10128363

The use of computers and the internet has spread rapidly over the course of the past few decades. Every day, more and more people are coming to rely heavily on the internet. When it comes to the field of information security, the subject of security is one that is becoming an increasingly important focus. It is vital to design a powerful intrusion detection system in order to prevent computer hackers and other intruders from effectively getting into computer networks or systems. This can be accomplished by: (IDS). The danger and attack detection capabilities of the computer system are built into the intrusion detection system. Abuse has occurred and can be used to identify invasions when there is a deviation between a preset pattern of intrusion and an observed pattern of intrusion. An intrusion detection system (IDS) is a piece of hardware (or software) that is used to generate reports for a Management Station as well as monitor network and/or system activities for unethical behaviour or policy violations. In the current study, an approach known as machine learning is suggested as a possible paradigm for the development of a network intrusion detection system. The results of the experiment show that the strategy that was suggested improves the capability of intrusion detection.