scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

Ant Colony Optimization Algorithm in Intrusion Detection and Positive

2013 ◽  
Vol 443 ◽  
pp. 541-545
Author(s):  
Qian Zou ◽  
Hua Jun Wang ◽  
Wei Huang ◽  
Jin Pan
Keyword(s):  
Combinatorial Optimization ◽  
Intrusion Detection ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Combinatorial Optimization Problems

Ant colony algorithm is an effective algorithm to solve combinatorial optimization problems, it has many good features, and there are also some disadvantages. In this paper, through research on ant colony optimization algorithm, apply it in intrusion detection. Then it gives an improved ant colony optimization algorithm. Tests show that the algorithm improves the efficiency of intrusion detection, reduces false positives of intrusion detection.

scholarly journals Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1223 ◽  
Author(s):  
Jianlei Gao ◽  
Senchun Chai ◽  
Baihai Zhang ◽  
Yuanqing Xia
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Principal Component ◽  
Detection Accuracy ◽  
Network Attack ◽  
Network Intrusion ◽  
Learning Machine ◽  
Malicious Behaviors ◽  
Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

scholarly journals Statistical based Feature Selection and Ensemble Model for Network Intrusion Detection using Data Mining Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 858-864
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Data Mining Technique ◽  
Chi Square ◽  
Network Intrusion ◽  
Secure Network ◽  
World Information ◽  
Using Data

In today’s world, Information society, computer networks and their interconnected applications are the emerging technologies. Intrusion Detection System (IDS) is used to distinguish the attitude of the network. Now a days, due to frequent and heavy attacks an Network devices, the Intrusion Detection System has become growing and censorious component to secure Network devices. A huge amount of data is needed to build the perfect Intrusion Detection System. This proposed system focuses on feature selection and ensemble of tree based classification methods to build Intrusion Detection System. The implementation of feature selection is fulfilled by using the NSL-KDD dataset. Statistical based feature selection methods such as Pearson's Correlation, Chi-square, Gain ratio and Symmetrical uncertainty are used to generate four modified datasets. By using that modified datasets the tree based Intrusion Detection models are built using J48, REP Tree and simple CART algorithms. To acquire better prediction of accuracy the algorithms J48, REP tree and simple CART are combined using ensemble method and built perfect tree based Intrusion Detection System.

Reach on Wireless Sensor Networks Security Issues and IDS Model

2014 ◽  
Vol 530-531 ◽  
pp. 45-49
Author(s):  
Jian Gang Tang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Detection Accuracy ◽  
Security Measures ◽  
Security Technology ◽  
Single Node ◽  
Security Issues ◽  
Network Intrusion ◽  
Wsn Security

Security measures could not absolutely prevent network intrusion. The security technology of intrusion detection system had made up for the lack of preventive measures; it could provide real-time intrusion detection and take appropriate protection for network. The research directions of WSN security were how to improve security strength and prolong the life of nodes, how to enhance the preventive ability of intelligent security system and real-time detection with high detection accuracy. This paper analyzed the typical network intrusion and defensive strategies, and researched WSN intrusion detection model by analyzing the typical algorithm. IDS model was divided into three types the first was based on single-node detection, the other was based on Multi-node peer cooperative, and the third was based on task decomposition level. Finally the paper gave the main research topic and direction for WSN security issues.

scholarly journals An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021 ◽  
Vol 13 (18) ◽  
pp. 10057
Author(s):  
Imran ◽  
Faisal Jamil ◽  
Dohyeun Kim
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Detection Accuracy ◽  
Learning Mechanism ◽  
Detection Systems ◽  
Network Applications ◽  
Network Intrusion

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

scholarly journals A Residual Learning-Based Network Intrusion Detection System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiarui Man ◽  
Guozi Sun
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Class Imbalance Problem ◽  
Data Set ◽  
Residual Learning ◽  
Network Intrusion

Neural networks have been proved to perform well in network intrusion detection. In order to acquire better features of network traffic, more learning layers are necessarily required. However, according to the results of the previous research, adding layers to the neural networks might fail to improve the classification results. In fact, after the number of layers has reached a certain threshold, performance of the model tends to degrade. In this paper, we propose a network intrusion detection model based on residual learning. After transforming the UNSW-NB15 data set into images, deeper convolutional neural networks with residual blocks are built to learn more critical features. Instead of the cross-entropy loss function, the modified focal loss is calculated to address the class imbalance problem in the training set and identify minor attacks in the testing set. Batch normalization and global average pooling are used to avoid overfitting and enhance the model. Experimental results show that the proposed model can improve attack detection accuracy compared with existing models.

The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network

2014 ◽  
Vol 599-601 ◽  
pp. 726-730 ◽  
Author(s):  
Gang Ke ◽  
Ying Han Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Detection System ◽  
Training Sample ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion

The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.

scholarly journals Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zengri Zeng ◽  
Wei Peng ◽  
Baokang Zhao
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Causal Intervention ◽  
Preliminary Classification

In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.

scholarly journals Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Mohammed Abdulhammed Al-Shabi
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Neuronal Networks ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.

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