scholarly journals Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

2019 ◽  
Vol 9 (2) ◽  
pp. 228-235
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Fuzzy Inference System ◽  
Intrusion Detection System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Features Selection ◽  
Inference System ◽  
Testing Procedures ◽  
Network Intrusion

Intrusion Detection System (IDS) is the nearly all imperative constituent of computer network security. IDSs are designed to comprehend intrusion attempts in incoming network traffic shrewdly. It deals with big volume of data containing immaterial and outmoded features, which lead to delay in training as well as testing procedures. Therefore, to minimize the false alarm and computation complexity, the features selection technique for intrusion detection has been implemented. In this paper PCA (Principal Component Analysis) and Fuzzy Inference System (FIS) have been used on kdd99 dataset to develop FC-NIDS model. PCA is used to select the attacked features to minimize the computational work, while FIS is used to develop a fuzzy inference system for accuracy in prophecy using MATLAB. The results of the experiment are tested on UCI data sets as a standard bench-mark. It has been found efficient for true prediction of intrusion as well as to reduce the false alarm rate. The proposed fuzzy logic controller IDS (FC-NIDS), is passable to covenant with signature and anomaly based attacks to get enhanced intrusion detection, decreases false alarm and to optimize complexity.

scholarly journals Computation of Risk Severity of the Malicious Node using Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 8 (1) ◽  
pp. 9-14
Author(s):  
R. Dharmarajan ◽  
V. Thiagarasu
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Malicious Nodes ◽  
Inference System ◽  
Detection Systems ◽  
Network Intrusion ◽  
Neuro Fuzzy ◽  
Network Intrusion Detection Systems

The Intrusion Detection System (IDS) can be employed broadly for safety network. Intrusion Detection Systems (IDSs) are commonly positioned alongside with other protecting safety mechanisms, such as authentication and access control, as a subsequent line of defence that guards data structures. In this paper, Adaptive Neuro Fuzzy Inference System has utilized to predict the risk severity of the malicious nodes found the previous classification phase.

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.

PAIRWISE CLUSTERS OPTIMIZATION AND CLUSTER MOST SIGNIFICANT FEATURE METHODS FOR ANOMALY-BASED NETWORK INTRUSION DETECTION SYSTEM (POC2MSF)

2018 ◽  
Vol 3 (2) ◽  
pp. 93
Author(s):  
Gervais Hatungimana
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
False Positive ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Significant Feature ◽  
Features Selection ◽  
Number Of Clusters ◽  
Network Intrusion

 Anomaly-based Intrusion Detection System (IDS) uses known baseline to detect patterns which have deviated from normal behavior. If the baseline is faulty, the IDS performance degrades. Most of researches in IDS which use k-centroids-based clustering methods like K-means, K-medoids, Fuzzy, Hierarchical and agglomerative algorithms to baseline network traffic suffer from high false positive rate compared to signature-based IDS, simply because the nature of these algorithms risk to force some network traffic into wrong profiles depending on K number of clusters needed. In this paper we propose alternate method which instead of defining K number of clusters, defines t distance threshold. The unrecognizable IDS; IDS which is neither HIDS nor NIDS is the consequence of using statistical methods for features selection. The speed, memory and accuracy of IDS are affected by inappropriate features reduction method or ignorance of irrelevant features. In this paper we use two-step features selection and Quality Threshold with Optimization methods to design anomaly-based HIDS and NIDS separately. The performance of our system is 0% ,99.9974%, 1,1 false positive rates, accuracy , precision and recall respectively for NIDS and  0%,99.61%, 0.991,0.978 false positive rates, accuracy, precision and recall respectively for HIDS.

scholarly journals A Mean Convolutional Layer for Intrusion Detection System

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Leila Mohammadpour ◽  
T.C. Ling ◽  
C.S. Liew ◽  
Alihossein Aryanfar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Internet Applications ◽  
Real World Application ◽  
Network Intrusion ◽  
Proposed Model

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies’ content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models.

scholarly journals An Efficient Optimized Fuzzy Inference System based Intrusion Detection in Cloud Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 4285-4295
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Denial Of Service ◽  
Gravitational Search Algorithm ◽  
Detection Accuracy ◽  
Cloud Environment ◽  
Inference System ◽  
Fuzzy C Means

Security incidents namely, Denial of service (DoS), scanning, virus, malware code injection, worm and password cracking are becoming common in a cloud environment that affects the company and may produce an economic loss if not detected in time. These problems are handled by presenting an intrusion detection system (IDS) in the cloud. But, the existing cloud IDSs affect from low detection accuracy, high false detection rate and execution time. To tackle these issues, in this paper, gravitational search algorithm based fuzzy Inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely; Possibilistic Fuzzy C-Means (PFCM) algorithm based clustering, training based on GSA-FIS and testing process. Initially, the incoming data are pre-processed and clustered with the help of PFCM. PFCM is detecting the noise of fuzzy c-means clustering (FCM), conquer the coincident cluster problem of Possibilistic Fuzzy C-Means (PCM) and eradicate the row sum constraints of fuzzy Possibilistic c-means clustering (FPCM). After the clustering process, the clustered data are given to the optimized fuzzy Inference system (OFIS). Here, normal and abnormal data are identified by the Fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely, probe, Remote to Local (R2L), User to Root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different scheme in terms of precision, recall and F-measure

scholarly journals Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection

2021 ◽  
Vol 3 (2) ◽  
pp. 118-127
Author(s):  
Subarna Shakya
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Gray Wolf ◽  
Machine Learning Classification ◽  
Network Intrusion ◽  
Proposed Model

The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.

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