Anomalous system call detection
By Darren H. Mutz, Fredrik Valeur, Giovanni Vigna, Christopher Krügel. TSEC, 2006
301 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/72f6ac503cad5d6017348b501d0a81a565b2da4c
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
- Kang G. Shin
- Fabio Martinelli
- Daniele Sgandurra
- Blaine Nelson
- Battista Biggio
- Fabio Roli
- Nicolas Papernot
- Giorgio Fumera
In Computer Networks and Communications
- Kang G. Shin
- Andrea Saracino
- Fabio Martinelli
- Daniele Sgandurra
- Gianluca Dini
- Blaine Nelson
- Battista Biggio
- Fabio Roli
Citations in the corpus
- MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention (2018, background): “The usage of computational intelligence (classifiers) and statistical techniques for intrusion detection is a well-known approach already exploited on other OSes and for network analysis [19], [20].”
- Detecting energy-greedy anomalies and mobile malware variants (2008, methodology): “, enumerating sequences [26, 47], ngram vectors [15], data-mining techniques [28], Bayesian classification [35], neural networks [34], finite-state automata [43], and hidden Markov models [54].”
- Behavior of Machine Learning Algorithms in Adversarial Environments (2010, background): “…[e.g., Newsome et al., 2005, Stolfo et al., 2003, 2004], host-based intrusion detection systems (HIDS) [e.g., Forrest et al., 1996, Hofmeyr et al., 1998, Mutz et al., 2006, Somayaji and Forrest, 2000, Warrender et al., 1999], and other types of fraud detection [cf., Bolton and Hand, 2002].”