| Title | Anomaly-based IDS to Detect Attack Using Various Artificial Intelligence Machine Learning Algorithms: A Review |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Mishra, A., Yadav, P. |
| Conference Name | 2nd International Conference on Data, Engineering and Applications (IDEA) |
| Keywords | accurate intrusion detection, anomaly-based IDS, artificial intelligence, artificial intelligence & machine learning algorithms, attack detection, complex & increasing tasks, composability, computer network security, computer networks, computer safety hazards, current IDS, cyber security, cyber-attacks, detect attack, ID, identifying attacks, IDS, intrusion detection system, learning (artificial intelligence), machine learning algorithms, multiple machine learning algorithms, Network security, NSL-KDD, pubcrawl, resilience, Resiliency, security of data, Signature-based Intrusion Detection Systems & Anomaly-based Intrusion Detection Systems |
| Abstract | Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms. |
| DOI | 10.1109/IDEA49133.2020.9170674 |
| Citation Key | mishra_anomaly-based_2020 |
Anomaly-based IDS to Detect Attack Using Various Artificial Intelligence Machine Learning Algorithms: A Review