Rajan Gupta (Avtor), Sunil K. Muttoo (Avtor), Saibal K. Pal (Avtor)

Povzetek

The problem of attacks on various networks and information systems is increasing. And with systems working in public domain like those involved under E-Governance are facing more problems than others. So there is a need to work on either designing an altogether different intrusion detection system or improvement of the existing schemes with better optimization techniques and easy experimental setup. The current study discusses the design of an Intrusion Detection Scheme based on traditional clustering schemes like K-Means and Fuzzy C-Means along with Meta-heuristic scheme like Particle Swarm Optimization. The experimental setup includes comparative analysis of these schemes based on a different metric called Classification Ratio and traditional metric like Detection Rate. The experiment is conducted on a regular Kyoto Data Set used by many researchers in past, however the features extracted from this data are selected based on their relevance to the E-Governance system. The results shows a better and higher classification ratio for the Fuzzy based clustering in conjunction with meta-heuristic schemes. The development and simulations are carried out using MATLAB.

Ključne besede

particle swarm optimization;intrusion detection;anomaly detection;intrusion detection system;network intrusion detection;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija: UNG - Univerza v Novi Gorici
UDK: 004
COBISS: 58232579 Povezava se bo odprla v novem oknu
Št. ogledov: 1350
Št. prenosov: 6
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni naslov: Design and analysis of clustering based intrusion detection schemes for e-governance
URN: URN:SI:UNG
Strani: Str. 461-471
DOI: 10.1007/978-3-319-47952-1_36
ID: 12721858