scholarly journals An Intelligent Agent Based Intrusion Detection System Using Fuzzy Rough Set Based Outlier Detection

2011 ◽  
pp. 91-94
Author(s):  
N. Jaisankar ◽  
M Ganapathy ◽  
A Kannan ◽  
K Anand
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Outlier Detection ◽  
Rough Set ◽  
Intelligent Agent ◽  
Experimental Result ◽  
Fuzzy Rough Set ◽  
Data Set ◽  
Agent Based

Since existing Intrusion Detection Systems (IDS) including misuse detection and anomoly detection are generally incapable of detecting new type of attacks. However, all these systems are capable of detecting intruders with high false alarm rate. It is an urgent need to develop IDS with very high Detection rate and with low False alarm rate. To satisfy this need we propose a new intelligent agent based IDS using Fuzzy Rough Set based outlier detection and Fuzzy Rough set based SVM. In this proposed model we intorduced two different inteligent agents namely feature selection agent to select the required feature set using fuzzy rough sets and decision making agent manager for making final decision. Moreover, we have introduced fuzzy rough set based outlier detection algorithm to detect outliers. We have also adopted Fuzzy Rough based SVM in our system to classify and detect anomalies efficiently. Finally, we have used KDD Cup 99 data set for our experiment, the experimental result show that the proposed intelligent agent based model improves the overall accuracy and reduces the false alarm rate.

scholarly journals Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Ganapathy ◽  
P. Yogesh ◽  
A. Kannan
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Outlier Detection ◽  
Intelligent Agent ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Set ◽  
Agent Based ◽  
Multiclass Svm

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

scholarly journals An Effective System of Intrusion Detection on Deep Neural Network by Hybrid Optimization in Cyber Security

2019 ◽  
Vol 9 (1) ◽  
pp. 1320-1327
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Search Algorithm ◽  
Detection System ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
Experimental Result ◽  
Gray Wolf ◽  
High Detection

In present trends organizations are very much interested to protect data and prevent malware attack by using well flourished and excellent tools. Many algorithms are used for the intrusion detection system (IDS) and it has pros and cons. Here we proposed a novel method of intrusion detection using hybrid optimization techniques such as Gravity search algorithm with gray wolf optimization (GSGW). In this method the gray wolf technique has a leader for the continuous monitoring of the attacker and has a low false alarm rate and a high detection rate. The performance evaluation is done by the feature selection in NSL-KDD dataset. In the proposed method the experimental result reveals less false alarm rate, better accuracy and high Detection when compared to previous analysis.

scholarly journals Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988313 ◽  
Author(s):  
Parminder Singh ◽  
Sujatha Krishnamoorthy ◽  
Anand Nayyar ◽  
Ashish Kr Luhach ◽  
Avinash Kaur
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Soft Computing ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Real Life ◽  
Hierarchical Data ◽  
Data Set

A false alarm rate of online anomaly-based intrusion detection system is a crucial concern. It is challenging to implement in the real-world scenarios when these anomalies occur sporadically. The existing intrusion detection system has been developed to limit or decrease the false alarm rate. However, the state-of-the-art approaches are attack or algorithm specific, which is not generic. In this article, a soft-computing-based approach has been designed to reduce the false-positive rate for hierarchical data of anomaly-based intrusion detection system. The recurrent neural network model is applied to classify the data set of intrusion detection system and normal instances for various subclasses. The designed approach is more practical, reason being, it does not require any assumption or knowledge of the data set structure. Experimental evaluation is conducted on various attacks on KDDCup’99 and NSL-KDD data sets. The proposed method enhances the intrusion detection systems that can work with data with dependent and independent features. Furthermore, this approach is also beneficial for real-life scenarios with a low occurrence of attacks.

A new hierarchical intrusion detection system based on a binary tree of classifiers

2015 ◽  
Vol 23 (1) ◽  
pp. 31-57 ◽  
Author(s):  
Ahmed Ahmim ◽  
Nacira Ghoualmi Zine
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Binary Tree ◽  
Detection Rate ◽  
Detection System ◽  
Data Set ◽  
Content Type ◽  
Network Connections ◽  
Different Types

Purpose – The purpose of this paper is to build a new hierarchical intrusion detection system (IDS) based on a binary tree of different types of classifiers. The proposed IDS model must possess the following characteristics: combine a high detection rate and a low false alarm rate, and classify any connection in a specific category of network connection. Design/methodology/approach – To build the binary tree, the authors cluster the different categories of network connections hierarchically based on the proportion of false-positives and false-negatives generated between each of the two categories. The built model is a binary tree with multi-levels. At first, the authors use the best classifier in the classification of the network connections in category A and category G2 that clusters the rest of the categories. Then, in the second level, they use the best classifier in the classification of G2 network connections in category B and category G3 that represents the different categories clustered in G2 without category B. This process is repeated until the last two categories of network connections. Note that one of these categories represents the normal connection, and the rest represent the different types of abnormal connections. Findings – The experimentation on the labeled data set for flow-based intrusion detection, NSL-KDD and KDD’99 shows the high performance of the authors' model compared to the results obtained by some well-known classifiers and recent IDS models. The experiments’ results show that the authors' model gives a low false alarm rate and the highest detection rate. Moreover, the model is more accurate than some well-known classifiers like SVM, C4.5 decision tree, MLP neural network and naïve Bayes with accuracy equal to 83.26 per cent on NSL-KDD and equal to 99.92 per cent on the labeled data set for flow-based intrusion detection. As well, it is more accurate than the best of related works and recent IDS models with accuracy equal to 95.72 per cent on KDD’99. Originality/value – This paper proposes a novel hierarchical IDS based on a binary tree of classifiers, where different types of classifiers are used to create a high-performance model. Therefore, it confirms the capacity of the hierarchical model to combine a high detection rate and a low false alarm rate.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

scholarly journals LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Binghao Yan ◽  
Guodong Han
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Network Traffic ◽  
Detection Performance ◽  
Imbalanced Learning ◽  
Traffic Distribution ◽  
Proposed Model ◽  
Gated Recurrent Unit

The intrusion detection models (IDMs) based on machine learning play a vital role in the security protection of the network environment, and, by learning the characteristics of the network traffic, these IDMs can divide the network traffic into normal behavior or attack behavior automatically. However, existing IDMs cannot solve the imbalance of traffic distribution, while ignoring the temporal relationship within traffic, which result in the reduction of the detection performance of the IDM and increase the false alarm rate, especially for low-frequency attacks. So, in this paper, we propose a new combined IDM called LA-GRU based on a novel imbalanced learning method and gated recurrent unit (GRU) neural network. In the proposed model, a modified local adaptive synthetic minority oversampling technique (LA-SMOTE) algorithm is provided to handle imbalanced traffic, and then the GRU neural network based on deep learning theory is used to implement the anomaly detection of traffic. The experimental results evaluated on the NSL-KDD dataset confirm that, compared with the existing state-of-the-art IDMs, the proposed model not only obtains excellent overall detection performance with a low false alarm rate but also more effectively solves the learning problem of imbalanced traffic distribution.

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