Saoussen Krichen

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 25 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #17,421 of 35,345 by trust here, #27,551 by citations
• Computer Networks and Communications — 17 citations into it; #4,174 of 9,886 by trust here, #7,140 by citations
• Signal Processing — 7 citations into it; #3,527 of 7,572 by trust here, #5,570 by citations
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Saoussen Krichen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Nour Moustafa
2. Isabelle M Guyon
3. Lei Yu
4. Longbing Cao
5. Girish Chandrashekar
6. Abdulla Amin Aburomman
7. Siva S. Sivatha Sindhu
8. Mohiuddin Ahmed
9. Chih-Fong Tsai
10. Pedro Garcia-Teodoro

Who trusts Saoussen Krichen overall

1. Sydney Mambwe Kasongo
2. Bayu Adhi Tama
3. Yanxia Sun

Papers from Saoussen Krichen's perspective

Ordered by how strongly the researchers Saoussen Krichen trusts endorse each paper. Same corpus, a different order for every researcher.

1. An Introduction to Variable and Feature Selection
2. An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks (Expert systems with applications)
3. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection (Expert systems with applications)
4. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) (Military Communications and Information Systems Conference)
5. A survey on feature selection methods
6. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (IEEE Communications Surveys and Tutorials)
7. A survey of intrusion detection techniques in Cloud (Journal of Network and Computer Applications)
8. A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems (Expert systems with applications)
9. A GA-based feature selection and parameters optimizationfor support vector machines (Expert systems with applications)
10. Random-Forests-Based Network Intrusion Detection Systems (IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews))

Their papers

• A GA-LR wrapper approach for feature selection in network intrusion detection (2017) — 347 citations