NDLI: An artificial intelligence membrane to detect network intrusion

Content Provider	Springer Nature Link
Author	Okamoto, Takeshi
Copyright Year	2011
Abstract	We propose an artificial intelligence membrane to detect network intrusion, which is analogous to a biological membrane that prevents viruses from entering cells. This artificial membrane is designed to monitor incoming packets and to prevent a malicious program code (e.g., a shellcode) from breaking into a stack or heap in a memory. While monitoring incoming TCP packets, the artificial membrane constructs a TCP segment of incoming packets, and derives the byte frequency of the TCP segment (from 0 to 255 bytes) as well as the entropy and size of the segment. These features of the segment can be classified by a data-mining technique such as a decision tree or neural network. If the data-mining method finds a suspicious byte sequence, the sequence is emulated to ensure that it is just a shellcode. If the byte sequence is a shellcode, the sequence is dropped. At the same time, an alert is communicated to the system administrator. Our experiments examined seven data-mining methods for normal and malicious network traffic. The malicious traffic included 114 shellcodes, provided by the Metasploit framework, and including 10 types of metamorphic or polymorphic shellcodes. In addition, real network traffic involving shellcodes was examined. We found that a random forest method outperformed all the other datamining methods and had a very high detection accuracy, including a true-positive rate of 99.6% and a false-positive rate of 0.4%.
Starting Page	44
Ending Page	47
Page Count	4
File Format	PDF
ISSN	14335298
Journal	Artificial Life and Robotics
Volume Number	16
Issue Number	1
e-ISSN	16147456
Language	English
Publisher	Springer Japan
Publisher Date	2011-06-29
Publisher Place	Japan
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Network intrusion detection Malicious software Shellcode Data mining Computation by Abstract Devices Control , Robotics, Mechatronics Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Biochemistry, Genetics and Molecular Biology

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