A survey of network anomaly detection techniques

By Mohiuddin Ahmed, Abdun Naser Mahmood, Jiankun Hu. Journal of Network and Computer Applications, 2016

1,637 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/2eb2320ff1e1a109d9c8bf0d53b3edc370d4cea6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ansam Khraisat
2. Aunn Raza
3. Nada Sahlab
4. Ala Al-Fuqaha
5. Hunain Arif
6. Amir Hussain
7. Mohamed Mosbah
8. Benjamin Maschler

In Computer Networks and Communications

1. Ansam Khraisat
2. Ala Al-Fuqaha
3. Mohamed Mosbah
4. Joarder Kamruzzaman
5. V. Varadharajan
6. U. Tupakula
7. Junaid Qadir
8. Saoussen Krichen

In Signal Processing

1. Ansam Khraisat
2. Ala Al-Fuqaha
3. Amir Hussain
4. Mohamed Mosbah
5. Joarder Kamruzzaman
6. V. Varadharajan
7. U. Tupakula
8. Junaid Qadir

Citations in the corpus

• Survey of intrusion detection systems: techniques, datasets and challenges (2019, background): “The updated survey of the taxonomy of intrusion-detection discipline is presented in this paper further enhances tax-onomies given in (Liao et al., 2013a; Ahmed et al., 2016).”
• A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “…technique such as Fussy Logic (FL), Nave Bayse (NB) networks, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), Logistic Regression (LR), etc. (Ahmed et al., 2016; Garcia-Teodoro et al., 2009; Ghorbani et al., 2009; Modi et al., 2013; Tsai et al., 2009).”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Another paper [213] reviewing anomaly detection has presented the application of unsupervised SVM and clustering based applications in network intrusion detection systems.”
• A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “Anomaly detection based IDSes are based on the hypothesis that attacker’s behavior differs from normal user’s behavior [22].”
• Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “Some of the important ones are [73], [77], [86]: – The probability distribution of the testing and training sets are different.”
• A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, background): “Existing high-profiled state-of-the-art surveys on anomaly detection techniques, such as [3] and [4], only marginally consider neural-network-based approaches.”
• Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, neutral): “credit cards, military applications, andmanymedical applications [1].”
• Monitoring Misuse for Accountable 'Artificial Intelligence as a Service' (2020, background): “This section discusses some relevant legal and technical considerations, highlighting some interesting practical challenges and opportunities for future work.”