Survey on Anomaly Detection using Data Mining Techniques

By Shikha Agrawal, Jitendra Agrawal. International Conference on Knowledge-Based Intelligent Information & Engineering Systems, 2015

598 citations recorded by the source corpus.

Abstract In the present world huge amounts of data are stored and transferred from one location to another. The data when transferred or stored is primed exposed to attack. Although various techniques or applications are available to protect data, loopholes exist. Thus to analyze data and to determine various kind of attack data mining techniques have emerged to make it less vulnerable. Anomaly detection uses these data mining techniques to detect the surprising behaviour hidden within data increasing the chances of being intruded or attacked. Various hybrid approaches have also been made in order to detect known and unknown attacks more accurately. This paper reviews various data mining techniques for anomaly detection to provide better understanding among the existing techniques that may help interested researchers to work future in this direction.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4ecc1703dfa8cf20767321746a10524ab733493b

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Citations in the corpus

Machine Learning for Anomaly Detection: A Systematic Review (2021, background): “On the other hand, Agrawal and Agrawal [8] proposed a survey on anomaly detection using data mining techniques.”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, unknown): “[110,111] X - - X - - X X - - - - - KDD99 Anomaly Detection”
A review of unsupervised feature selection methods (2019, background): “…(Yasmin et al. 2014; Swets and Weng 1995), social media (Zafarani et al. 2014; Tang and Liu 2014) and intrusion detection (Ahmed et al. 2016; Lee et al. 2000; Agrawal and Agrawal 2015; Ambusaidi et al. 2015), to name a few; UFS methods have gained significant interest in the scientific community.”
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey (2019, background): “Several previous surveys [3–5] have classiﬁed research efforts by their applied machine learning algorithms.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “Despite idealized simplifying assumptions, naive Bayes classifiers have enjoyed popularity in numerous complex realworld situations, such as outlier detection [230], spam filtering [231], etc.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “NB have been used for network intrusion detection [143], [144] and anomaly detection [145], [146].”
Anomalous Instance Detection in Deep Learning: A Survey (2020, methodology): “In [5], a survey of the data mining techniques used for anomaly detection are discussed.”
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges (2021, background): “Existing review articles (e.g., such as (Chaabouni et al., 2019; da Costa et al., 2019; Buczak & Guven, 2016; Lunt, 1988; Agrawal & Agrawal, 2015)) focus on intrusion detection techniques or dataset issue or type of computer attack and IDS evasion.”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “Surveys about IDS based on learning techniques (not specially for IoT) such as [13], [15], and [24]–[27] have performed comprehensive evaluation for traditional systems.”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “Agrawal and Agrawal [39] have provided a survey on anomaly detection using data mining techniques for intrusion detection.”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “NB is usually used in IoT to detect intrusion detection in the network layer [93], [94] and anomaly detection [95], [96].”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, background): “Existing review articles (e.g., such as (Buczak & Guven, 2016; Axelsson, 2000; Ahmed et al., 2016; Lunt, 1988; Agrawal & Agrawal, 2015)) focus on intrusion detection techniques or dataset issue or type of computer attack and IDS evasion.”