State Transition Analysis: A Rule-Based Intrusion Detection Approach
IEEE Transactions on Software Engineering
Classification and detection of computer intrusions
Classification and detection of computer intrusions
Testing and evaluating computer intrusion detection systems
Communications of the ACM
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Computer Networking: A Top-Down Approach Featuring the Internet Package
Computer Networking: A Top-Down Approach Featuring the Internet Package
On a Pattern-Oriented Model for Intrusion Detection
IEEE Transactions on Knowledge and Data Engineering
Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
The Analytic Hierarchy Process--An Exposition
Operations Research
KDD-99 classifier learning contest LLSoft's results overview
ACM SIGKDD Explorations Newsletter
eXpert-BSM: A Host-Based Intrusion Detection Solution for Sun Solaris
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Clustering intrusion detection alarms to support root cause analysis
ACM Transactions on Information and System Security (TISSEC)
Verify Results of Network Intrusion Alerts Using Lightweight Protocol Analysis
ACSAC '05 Proceedings of the 21st Annual Computer Security Applications Conference
An Intelligent Detection and Response Strategy to False Positives and Network Attacks
IWIA '06 Proceedings of the Fourth IEEE International Workshop on Information Assurance
Measuring intrusion detection capability: an information-theoretic approach
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Intrusion Detection Based on Adaptive RBF Neural Network
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
ATLANTIDES: an architecture for alert verification in network intrusion detection systems
LISA'07 Proceedings of the 21st conference on Large Installation System Administration Conference
Network Anomaly Detection Based on Statistical Approach and Time Series Analysis
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Application of artificial neural network in detection of DOS attacks
Proceedings of the 2nd international conference on Security of information and networks
Human interface for cyber security anomaly detection systems
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Neural network based intrusion detection system for critical infrastructures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
| Hi-index | 0.00 |
The main objective of this paper is to design a more complete intrusion detection system solution. The paper presents an efficient approach for reducing the rate of alerts using divided two-part adaptive intrusion detection system (DTPAIDS). The proposed DTPAIDS has a high degree of autonomy in tracking suspicious activity and detecting positive intrusions. The proposed DTPAIDS is designed with the aim of reducing the rate of detected false positive intrusion through two achievements. The first achievement is done by implementing adaptive self-learning neural network in the proposed DTPAIDS to gives it the ability to be automatic adaptively system based on Radial Basis Functions (RBF) neural network. The second achievement is done through dividing the proposed intrusion detection system IDS into two parts. The first part is IDS1, which is installed in the front of firewall and responsible for checking each entry user's packet and deciding if the packet considered is an attack or not. The second is IDS2, which is installed behind the firewall and responsible for detecting only the attacks which passed the firewall. This proposed approach for IDS exhibits a lower false alarm rate when detects novel attacks. The simulation tests are conducted using DARPA 1998 dataset. The experimental results show that the proposed DTPAIDS [1] reduce false positive rate, [2] detects intrusion occurrence sensitively and precisely, [3] accurately self---adapts diagnoser model, thus improving its detection accuracy.