Add to ORKGAbstract. This paper extends a series of experiments performed by Schonlau et al. [1], Maxion [2] and Bertacchini et al. [3] on the detection of computer masqueraders (i.e. illegitimate users trying to impersonate legitimate ones). A compression-based classification algorithm called Normalized Compression Distance or NCD, developed by Vitányi et al. [4] is applied on truncated and enriched command-line data. It is shown that the use of enriched data significantly improves the NCD-based detection performance compared with using truncated data sets. Future work, possible enhancements and directions of further research on this topic are presented as well.
Abstract. Masqueraders in computer intrusion detection are people who use somebody else’s computer a...
This paper presents one-class Hellinger distance-based and one-class SVM modeling techniques that us...
Masquerade attacks are unfortunately a familiar security problem that is a consequence of identity t...
This paper extends a series of experiments performed by Schonlau et al. [1] on the detection of comp...
Masqueraders are people who impersonate other people on a computer system and they pose threat to th...
Insider threat detection is still a relatively new area of study in Computer Science. Perhaps the mo...
Abstract. This paper focuses on the study of a new method for de-tecting masqueraders in computer sy...
Malware concealment is the predominant strategy for malware propagation. Black hats create variants ...
Masquerade attacks are characterized by an adversary stealing a legitimate user's credentials and us...
Masqueraders are a category of intruders who impersonate other people on a computer system and use t...
This paper presents one-class Hellinger distance-based and one-class SVM modeling techniques that us...
The facial forgery generation, which could be utilised to modify facial attributes, is the critical ...
In applying command sequence matching to detect masquerading attack, the first important step is to ...
We show that masquerade detection, based on sequences of commands executed by the users, can be effe...
Abstract. Masquerade attacks are a common security problem that is a consequence of identity theft. ...
Abstract. Masqueraders in computer intrusion detection are people who use somebody else’s computer a...
This paper presents one-class Hellinger distance-based and one-class SVM modeling techniques that us...
Masquerade attacks are unfortunately a familiar security problem that is a consequence of identity t...
This paper extends a series of experiments performed by Schonlau et al. [1] on the detection of comp...
Masqueraders are people who impersonate other people on a computer system and they pose threat to th...
Insider threat detection is still a relatively new area of study in Computer Science. Perhaps the mo...
Abstract. This paper focuses on the study of a new method for de-tecting masqueraders in computer sy...
Malware concealment is the predominant strategy for malware propagation. Black hats create variants ...
Masquerade attacks are characterized by an adversary stealing a legitimate user's credentials and us...
Masqueraders are a category of intruders who impersonate other people on a computer system and use t...
This paper presents one-class Hellinger distance-based and one-class SVM modeling techniques that us...
The facial forgery generation, which could be utilised to modify facial attributes, is the critical ...
In applying command sequence matching to detect masquerading attack, the first important step is to ...
We show that masquerade detection, based on sequences of commands executed by the users, can be effe...
Abstract. Masquerade attacks are a common security problem that is a consequence of identity theft. ...
Abstract. Masqueraders in computer intrusion detection are people who use somebody else’s computer a...
This paper presents one-class Hellinger distance-based and one-class SVM modeling techniques that us...
Masquerade attacks are unfortunately a familiar security problem that is a consequence of identity t...