scholarly journals Decision-Forest Voting Scheme for Classification of Rare Classes in Network Intrusion Detection

2018 ◽  
Author(s):  
Jan Brabec ◽  
Lukas Machlica
Keyword(s):  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Voting Scheme ◽  
Rare Classes ◽  
Decision Forest

scholarly journals LSTM deep learning method for network intrusion detection system

2020 ◽  
Vol 10 (3) ◽  
pp. 3315
Author(s):  
Alaeddine Boukhalfa ◽  
Abderrahim Abdellaoui ◽  
Nabil Hmina ◽  
Habiba Chaoui
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Network Intrusion Detection ◽  
Primary Concern ◽  
Great Ability ◽  
Network Intrusion ◽  
Network Intrusion Detection System

The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services or steal information, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-TermMemory (LSTM) to recognize menaces and to obtain a long-term memory on them, inorder to stop the new attacks that are like the existing ones, and at the sametime, to have a single mean to block intrusions. According to the results of the experiments of detections that we have carried out, the Accuracy reaches upto 99.98 % and 99.93 % for respectively the classification of two classes and several classes, Also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is very effective, it has a great ability to memorize and differentiate between normal traffic and attack traffic and its identification is more accurate than other Machine Learning classifiers.

scholarly journals Performance analysis of binary and multiclass models using azure machine learning

2020 ◽  
Vol 10 (1) ◽  
pp. 978
Author(s):  
Smitha Rajagopal ◽  
Katiganere Siddaramappa Hareesha ◽  
Poornima Panduranga Kundapur
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
False Alarms ◽  
Forest Model ◽  
Network Intrusion ◽  
Decision Forest ◽  
Modern Solution

Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.

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