scholarly journals A Labeled Data Set for Flow-Based Intrusion Detection

2009 ◽  
pp. 39-50 ◽  
Author(s):  
Anna Sperotto ◽  
Ramin Sadre ◽  
Frank van Vliet ◽  
Aiko Pras
Keyword(s):  
Intrusion Detection ◽  
Data Set

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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.

The Application of Fuzzy Clustering Number Algorithm in Network Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 2220-2223
Author(s):  
Lang Guo
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Cluster Number ◽  
Data Set ◽  
Network Intrusion ◽  
Correlation Degree ◽  
Indicator Data ◽  
Detection Effect

In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.

Enhance Network Intrusion Detection System by Exploiting BR Algorithm as an Optimal Feature Selection

2015 ◽  
pp. 17-32 ◽  
Author(s):  
Soukaena Hassan Hashem
Keyword(s):  
Intrusion Detection ◽  
Wireless Network ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Wireless Data ◽  
Data Set ◽  
Network Intrusion ◽  
Network Intrusion Detection System

This chapter aims to build a proposed Wire/Wireless Network Intrusion Detection System (WWNIDS) to detect intrusions and consider many of modern attacks which are not taken in account previously. The proposal WWNIDS treat intrusion detection with just intrinsic features but not all of them. The dataset of WWNIDS will consist of two parts; first part will be wire network dataset which has been constructed from KDD'99 that has 41 features with some modifications to produce the proposed dataset that called modern KDD and to be reliable in detecting intrusion by suggesting three additional features. The second part will be building wireless network dataset by collecting thousands of sessions (normal and intrusion); this proposed dataset is called Constructed Wireless Data Set (CWDS). The preprocessing process will be done on the two datasets (KDD & CWDS) to eliminate some problems that affect the detection of intrusion such as noise, missing values and duplication.

Computer Immunity Using an Intrusion Detection System (IDS)

2013 ◽  
Vol 824 ◽  
pp. 200-205 ◽  
Author(s):  
Susan Konyeha ◽  
Emmanuel A. Onibere
Keyword(s):  
Immune System ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Security Policy ◽  
Detection System ◽  
Human Immune System ◽  
Data Set ◽  
Computer Immune ◽  
Human Immune ◽  
Computer Immune System

Computers are involved in every aspect of modern society and have become an essential part of our lives, but their vulnerability is of increasing concern to us. Security flaws are inherent in the operation of computers Most flaws are caused by errors in the process of software engineering or unforeseen mishaps and it is difficult to solve these problems by conventional methods. A radical way of constantly monitoring the system for newly disclosed vulnerabilities is required. In order to devise such a system, this work draws an analogy between computer immune systems and the human immune system. The computer immune system is the equivalent of the human immune system. The primary objective of this paper is to use an intrusion detection system in the design and implementation of a computer immune system that would be built on the framework of the human immune system. This objective is successfully realized and in addition a prevention mechanism using the windows IP Firewall feature has been incorporated. Hence the system is able to perform intrusion detection and prevention. Data was collected about events occurring in a computer network that violate predefined security policy, such as attempts to affect the confidentiality, integrity or its availability using Snort rules for known attacks and adaptive detection for the unknown attacks. The system was tested using real-time data and Intrusion Detection evaluation (IDEVAL) Department of Defense Advanced Research Projects Agency (DARPA) data set. The results were quite encouraging as few false positive were recorded.

scholarly journals An Intrusion Detection Model based on Hybrid Classification algorithm

2018 ◽  
Vol 246 ◽  
pp. 03027
Author(s):  
Manfu Ma ◽  
Wei Deng ◽  
Hongtong Liu ◽  
Xinmiao Yun
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Performance Requirements ◽  
Hybrid Classification ◽  
Bayes Algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.

scholarly journals RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

Computers ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 59 ◽  
Author(s):  
Ayyaz-Ul-Haq Qureshi ◽  
Hadi Larijani ◽  
Nhamoinesu Mtetwa ◽  
Abbas Javed ◽  
Jawad Ahmad
Keyword(s):  
Intrusion Detection ◽  
Mean Squared Error ◽  
Detection System ◽  
Data Set ◽  
Bee Colony ◽  
Squared Error ◽  
Random Neural Network ◽  
Prime Concern ◽  
Gradient Descent Algorithm ◽  
Memory Resources

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods.

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