NDLI: Integer Data Zero-Watermark Assisted System Calls Abstraction and Normalization for Host Based Anomaly Detection Systems

Content Provider	IEEE Xplore Digital Library
Author	Haider, W. Jiankun Hu Xinghuo Yu Yi Xie
Copyright Year	2015
Description	Author affiliation: Sch. of Eng. & Inf. Technol., Univ. of New South Wales at the Australian Defence Force Acad., Canberra, ACT, Australia (Haider, W.) \|\| Dept. of Electr. & Commun. Eng., Sun Yat-Sen Univ., Guangzhou, China (Xinghuo Yu; Yi Xie) \|\| Res. Inst., RMIT, Melbourne, VIC, Australia (Jiankun Hu)
Abstract	The generation of representative computer system behavior profile from system calls in LINUX environments to establish reliable Host Based Anomaly Detection Systems (HADS) against Next Generation of Attacks (NGA) is a challenge due to two major reasons. Firstly, NGA causes a low footprint upon host activities and consequently, attack activities are difficult to detect from normal computer processes in terms of accuracy and processing time. Secondly, there is no effective method to extract the natural difference from the two different types of traces (e.g. normal or abnormal) of system calls. Following these reasons, a semi-supervised model is proposed, which is comprised of two parts. Firstly, to establish an unsupervised computer behavior classification, an integer data zero-watermarking algorithm is developed to extract abstract hidden representation of system calls. This hidden representation constitutes the natural difference between attack and normal computer system behavior in real-time. Secondly, various supervised Machine Learning (ML) algorithms and normalizations are realized with proposed hidden representation of the system calls to evaluate the semi-supervised model in HADS. To evaluate the performance in terms of accuracy and processing time, the publicly available bench mark host based data sets: ADFA-LD and KDD 98 have been utilized. Each data set is the collection of traces of processes and each trace comprises of process's system calls. Experimental results shows that the suggested semi-supervised model outperforms existing methodologies in terms of accuracy and processing time for the detection of low and high foot print attacks.
Starting Page	349
Ending Page	355
File Size	391819
Page Count	7
File Format	PDF
e-ISBN	9781467393003
DOI	10.1109/CSCloud.2015.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Linux Operating systems And Hidden features extraction Semantics Watermarking Zero day attacks Feature extraction Host based intrusion detection systems (HIDS) Computational efficiency Low foot print attacks Stealth Attacks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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