NDLI: TCP/IP Model and Intrusion Detection Systems

Content Provider	IEEE Xplore Digital Library
Author	Zaman, S. Karray, F.
Copyright Year	2009
Abstract	To accommodate the information security growth and hacker's improved strategies and tools, Intrusion Detection Systems (IDSs) are required to be allocated across the network. Furthermore, previous studies showed that the choice of network features used for the IDS is dependent on the type of the attack. Accordingly, each TCP/IP network layer has specific type of network attacks, which means that each TCP/IP network layer needs a specific type of IDS. This paper proposes a new categorization for IDS depending on the TCP/IP network model: Application layer IDS (AIDS), Transport layer IDS (TIDS), Network layer IDS (NIDS) and Link layer IDS (LIDS). Each of these IDS types is specialized to a specific network device. So, the detection process will be distributed among all TCP/IP network model layers through the network devices. To design each of these different types of IDS, several experiments have been conducted using two different features selection approaches to select the appropriate features set for each IDS type. The experimental results indicate that each IDS type has different features set that can not only improve the overall performance of the IDS, but it also can improve its scalability.
Starting Page	90
Ending Page	96
File Size	226990
Page Count	7
File Format	PDF
ISBN	9781424439997
DOI	10.1109/WAINA.2009.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-26
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	support vector machines Scalability Data processing features selection Support vector machines TCP/IP network model Computer hacking Acquired immune deficiency syndrome Intrusion detection Information security TCPIP Computer networks features ranking IP networks support vector decision function Intrusion detection systems
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Lightweight IDS Based on Features Selection and IDS Classification Scheme

Identifying important features for intrusion detection using support vector machines and neural networks

A Novel Detection of Network Errrors by Study of Raw TCP/IP Packets

Anomaly Detection in P2P Networks Using Markov Modelling

Features Selection for Intrusion Detection Systems Based on Support Vector Machines

Intrusion Detection Based on Fuzzy Support Vector Machines

High Efficient Intrusion Detection Methodology with Twin Support Vector Machines

Correlating TCP/IP Interactive Sessions with Correlation Coefficient to Detect Stepping-Stone Intrusion

A network state based intrusion detection model

TCP/IP Model and Intrusion Detection Systems

Similar Documents

Lightweight IDS Based on Features Selection and IDS Classification Scheme

Identifying important features for intrusion detection using support vector machines and neural networks

A Novel Detection of Network Errrors by Study of Raw TCP/IP Packets

Anomaly Detection in P2P Networks Using Markov Modelling

Features Selection for Intrusion Detection Systems Based on Support Vector Machines

Intrusion Detection Based on Fuzzy Support Vector Machines

High Efficient Intrusion Detection Methodology with Twin Support Vector Machines

Correlating TCP/IP Interactive Sessions with Correlation Coefficient to Detect Stepping-Stone Intrusion

A network state based intrusion detection model

TCP/IP Model and Intrusion Detection Systems