Reading Network Packets as a Natural Language for Intrusion Detection

Information Security and Cryptology – ICISC 2017 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-78556-1_19 ◽

2018 ◽

pp. 339-350 ◽

Cited By ~ 3

Author(s):

Mamoru Mimura ◽

Hidema Tanaka

Keyword(s):

Intrusion Detection ◽

Natural Language

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Network Intrusion Detection using Natural Language Processing and Ensemble Machine Learning

2020 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci47803.2020.9308268 ◽

2020 ◽

Author(s):

Saikat Das ◽

Mohammad Ashrafuzzaman ◽

Frederick T. Sheldon ◽

Sajjan Shiva

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Intrusion Detection ◽

Natural Language ◽

Language Processing ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Ensemble Machine Learning

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Profiling and Identifying Individual Users by Their Command Line Usage and Writing Style

Knowledge Engineering and Data Science ◽

10.17977/um018v1i22018p55-63 ◽

2018 ◽

Vol 1 (2) ◽

pp. 55

Author(s):

Darusalam Darusalam ◽

Helen Ashman

Keyword(s):

Intrusion Detection ◽

Natural Language ◽

Formal Language ◽

User Profile ◽

Specific Information ◽

User Profiles ◽

User Characteristics ◽

User Identification ◽

System User ◽

N Gram

Profiling and identifying individual users is an approach for intrusion detection in a computer system. User profiles are important in many applications since they record highly user-specific information - profiles are basically built to record information about users or for users to share experiences with each other. This research extends previous research on re-authenticating users with their user profiles. This research focuses on the potential to add psychometric user characteristics into the user model so as to be able to detect unauthorized users who may be masquerading as a genuine user. There are five participants involved in the investigation for formal language user identification. Additionally, we analyze the natural language of two famous writers, Jane Austen & William Shakespeare, in their written works to determine if the same principles can be applied to natural language use. This research used the n-gram analysis method for characterizing user’s style, and can potentially provide accurate user identification. As a result, n-gram analysis of a user's typed inputs offers another method for intrusion detection as it may be able to both positively and negatively identify users. The contribution of this research is to assess the use of a user’s writing styles in both formal language and natural language as a user profile characteristic that could enable intrusion detection where intruders masquerade as real users.

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