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Designing a Framework for Network Security Protection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jiang, Eric P. |
| Copyright Year | 2012 |
| Abstract | As the Internet continues to grow at a rapid pace a s the primary medium for communications and commerce and as telecommunication networks and systems continue to xpand their global reach, digital information has become the mo st popular and important information resource and our dependence u pon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and c riminal enterprises. In this paper, we propose a new machi ne learning based network intrusion detection framework for cyber sec urity. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is app lied to a collected set of network audit data to generate a p rofile of normal network behavior and in the intrusion detection sta ge, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anoma lies should these events are sufficiently far from the expected norma l behavior. The proposed framework is particularly applicable to th e situations where there is only a small amount of labeled network tra ining data available, which is very typical in real world netw ork environments. Keywords—classification, data analysis and mining, network intrusion detection, semi-supervised learning. |
| Starting Page | 821 |
| Ending Page | 825 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| Volume Number | 6 |
| Alternate Webpage(s) | https://waset.org/publications/2002/designing-a-framework-for-network-security-protection |
| Alternate Webpage(s) | http://waset.org/publications/2002/designing-a-framework-for-network-security-protection |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
