An intrusion detection and prevention system in cloud computing: A systematic review

By Ahmed Patel, Mona Taghavi, Kaveh Bakhtiyari, Joaquim Celestino Jr.. Journal of Network and Computer Applications, 2013

453 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cf003fbb1609c1457701cb7d698a5361be1d16b1

How researchers see this paper

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

In Computer Networks and Communications

1. Ansam Khraisat
2. Mohammad Ali Jabraeil Jamali
3. Joarder Kamruzzaman
4. Peter Vamplew
5. Arash Heidari
6. Iqbal Gondal
7. Ammar Alazab

In Information Systems

1. Ansam Khraisat
2. Joarder Kamruzzaman
3. Peter Vamplew
4. Arash Heidari
5. Iqbal Gondal

In Signal Processing

1. Ansam Khraisat
2. Mohammad Ali Jabraeil Jamali
3. Joarder Kamruzzaman
4. Peter Vamplew
5. Arash Heidari
6. Iqbal Gondal
7. Ammar Alazab

Citations in the corpus

• Evaluation of machine learning classifiers for mobile malware detection (2014, unknown): “In addition, the detection process can run using cloud services such as Patel et al. (2013), whereby network traffic is analyzed remotely.”
• Security issues in cloud environments: a survey (2014, background): “[208] in their state-of-the-art survey on IDS and IPS solutions.”
• Internet of Things intrusion detection systems: a comprehensive review and future directions (2022, background): “There may not be a traditional IDS that can effectively satisfy these requirements [36].”
• Survey of intrusion detection systems: techniques, datasets and challenges (2019, result): “Compared to previous survey publications (Patel et al., 2013; Liao et al., 2013a), this paper presents a discussion on IDS dataset problems which are of main concern to the research community in the area of network intrusion detection systems (NIDS).”