High-Order Markov Kernels for Network Intrusion Detection

2006 ◽  
pp. 184-191 ◽  
Author(s):  
Shengfeng Tian ◽  
Chuanhuan Yin ◽  
Shaomin Mu
Keyword(s):  
Intrusion Detection ◽  
High Order ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Markov Kernels

scholarly journals LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shengwei Lei ◽  
Chunhe Xia ◽  
Tianbo Wang
Keyword(s):  
Intrusion Detection ◽  
High Order ◽  
Network Intrusion Detection ◽  
Feature Interaction ◽  
Detection Accuracy ◽  
High Order Interaction ◽  
Order Interaction ◽  
Feature Interactions ◽  
Network Intrusion ◽  
Low Order

Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low- or high-order feature interaction. Moreover, they always extract all possible low-order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low-order correlation and high-order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low-order correlation between the same-type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high-order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low- and high-order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower-order feature correlation and the nonlinearity of DNN in modeling higher-order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL-KDD, UNSW-NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.

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.

An Experimental Exploration of the Impact of Network-Level Packet Loss on Network Intrusion Detection

2015 ◽  
Author(s):  
Sidney C. Smith ◽  
Kin W. Wong ◽  
II Hammell ◽  
Mateo Robert J. ◽  
Carlos J.
Keyword(s):  
Intrusion Detection ◽  
Packet Loss ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
The Impact
Sign in / Sign up

Export Citation Format

Share Document