An Automatic Correlated Recursive Wrapper-Based Feature Selector (ACRWFS) for Efficient Classification of Network Intrusion Features

2021 ◽  
pp. 647-660
Author(s):  
P. Ramachandran ◽  
R. Balasubramian
Keyword(s):  
Network Intrusion ◽  
Feature Selector

Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model

2014 ◽  
Vol 643 ◽  
pp. 99-104
Author(s):  
Jin Yang ◽  
Yun Jie Li ◽  
Qin Li
Keyword(s):  
Situation Awareness ◽  
Artificial Immune Systems ◽  
Epidemic Spreading ◽  
State Spaces ◽  
Real Time Processing ◽  
Network Intrusion ◽  
Markov Chain Theory ◽  
Proposed Model ◽  
Network Risk

In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

Ensemble-Based Feature Selection With Long Short-Term Memory for Classification of Network Intrusion

2021 ◽  
pp. 228-245
Author(s):  
Preethi D. ◽  
Neelu Khare
Keyword(s):  
Feature Selection ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Short Term ◽  
Chi Square ◽  
Term Memory ◽  
Network Intrusion ◽  
Proposed Model ◽  
Long Short Term Memory

This chapter presents an ensemble-based feature selection with long short-term memory (LSTM) model. A deep recurrent learning model is proposed for classifying network intrusion. This model uses ensemble-based feature selection (EFS) for selecting the appropriate features from the dataset and long short-term memory for the classification of network intrusions. The EFS combines five feature selection techniques, namely information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the standard benchmark NSL-KDD dataset and implemented using tensor flow and python. The proposed model is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection technique and classified using LSTM. The performance study showed that the proposed model performs better, with 99.8% accuracy, with a higher detection and lower false alarm rates.

scholarly journals Classification of Big Data: Machine Learning Problems and Challenges in Network Intrusion Prediction

2018 ◽  
Vol 7 (4.36) ◽  
pp. 1189
Author(s):  
Yasser Mohammad Al-Sharo ◽  
Ghazi Shakah ◽  
Mutasem Sh.Alkhaswneh ◽  
Bajes Zeyad Aljunaeidi ◽  
Malik Bader Alazzam
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Representation Learning ◽  
Learning Problems ◽  
Traffic Information ◽  
Classification Problems ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Geometric Techniques

Centre of attraction of paper is on the main complication on classification of Big Data on network encroachment on traffic. It also explains the disputes this system faces that is bestowed by the Big Data difficulties that are correlate with the network interruption forecast. Forecasting of an attainable interruption in a network entails a prolonged accumulation of traffic information or data and being able to get the concept on their features on motion. The constant accumulation in the network of traffic data thereafter ends with Big Data difficulties that as a result of the large amount, change and possessions of Big Data. In order to learn the features of a network, one needs to have the skills in the machine techniques that are always able to capture world skills and knowledge of the traffic to be in order. The properties of Big Data will always end to an important system disputes to be able to apply machine learning foundation. The paper also discusses the disputes and problems in the way of taking care of Big Data categorization representing geometric techniques of learning along with the existing technologies of Big networking. The study particularly explains challenges that have a relationship with the combined directed by the techniques one learns, machine long learning techniques, and representation-learning techniques and technologies that are related to Big Data for example Hive, Hadoop and Cloud that are basics that enhances problem-solving that gives relevant solutions to classification problems in traffic networking.  

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.

Sign in / Sign up

Export Citation Format

Share Document