scholarly journals Detection and Recognition of Atomic Evasions Against Network Intrusion Detection/Prevention Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 87816-87826 ◽  
Author(s):  
Jia Jingping ◽  
Chen Kehua ◽  
Chen Jia ◽  
Zhou Dengwen ◽  
Ma Wei
Keyword(s):  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Detection And Recognition

scholarly journals FUSION OF RECIRCULATION NEURAL NETWORKS FOR REAL-TIME NETWORK INTRUSION DETECTION AND RECOGNITION

2014 ◽  
pp. 383-390
Author(s):  
Pavel Kachurka ◽  
Vladimir Golovko
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Traffic Data ◽  
Network Intrusion ◽  
Anomaly Detector ◽  
Detection And Recognition

Intrusion detection system is one of the essential security tools of modern information systems. Continuous development of new types of attacks re quires the development of intelligent approaches for intrusion detection capable to detect newest attacks. We present recirculation neural network based approach which lets to detect previously unseen attack types in real-time mode and to further correct recognition of this types. In this paper we use recirculation neural networks as an anomaly detector as well as a misuse detector, ensemble of anomaly and misuse detectors, fusion of several detectors for correct detection and recognition of attack types. The experiments held on both KDD’99 data and real network traffic data show promising results.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

Sign in / Sign up

Export Citation Format

Share Document