Modeling Intrusion Detection System Using Hybrid Intelligent Systems
The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as Intrusion Detection System (IDS). This paper presents two hybrid approaches for modeling IDS. Decision Trees (DT) and Support Vector Machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT-SVM) and an ensemble approach combining the base classifiers. The hybrid intrusion detection model combines the individual base classifiers and other hybrid machine learning paradigms to maximize detection accuracy and minimize computational complexity. Empirical results illustrate that the proposed hybrid systems provide more accurate intrusion detection systems.
Traditional protection techniques such as user authentication, data encryption, avoiding programming errors and firewalls are used as the first line of defense for computer security. If a password is weak and is compromised, user authentication cannot prevent unauthorized use, firewalls are vulnerable to errors in configuration and suspect to ambiguous or undefined security policies. They are generally unable to protect against malicious mobile code, insider attacks and unsecured modems. Programming errors cannot be avoided as the complexity of the system and application software is evolving rapidly leaving behind some exploitable weaknesses. Consequently, computer systems are likely to remain unsecured for the foreseeable future. Therefore intrusion detection is required as an additional wall for protecting systems despite the prevention techniques. Intrusion detection is useful not only in detecting successful intrusions, but also in monitoring attempts to break security, which provides important information for timely
Click here for free
download this paper