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 A determinant fuzzy apparoach for intrusion detection system

2018 ◽  
Vol 7 (1.9) ◽  
pp. 245
Author(s):  
S. Vimala ◽  
V. Khanna ◽  
C. Nalini
Keyword(s):  
Data Collection ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Great Degree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Information Mining ◽  
The Past ◽  
Recognition Systems

In MANETs, versatile hubs can impart transparently to each other without the need of predefined framework. Interruption location framework is a fundamental bit of security for MANETs. It is uncommonly convincing for identifying the Intrusions and for the most part used to supplement for other security segment. That is the reason Intrusion discovery framework (IDS) is known as the second mass of assurance for any survivable framework security. The proposed fluffy based IDSs for recognition of Intrusions in MANETs are not prepared to adjust up all sort of assaults. We have examined that all proposed fluffy based IDSs are seen as to a great degree obliged segments or qualities for data collection which is specific for a particular assault. So that these IDSs are simply recognize the particular assault in MANETs. The fluffy motor may perceive blockage from channel mistake conditions, and along these lines helps the TCP blunder discovery. Examination has been made on the issues for upgrading the steady quality and precision of the decisions in MANET. This approach offers a strategy for joining remote units' estimation comes to fruition with alliance information open or priori decided at conglomerating hubs. In our investigation work, the best need was to reduce the measure of information required for getting ready and the false alarm rate. We are chiefly endeavoring to improve the execution of a present framework rather than endeavoring to supplant current Intrusion recognition systems with an information mining approach. While current mark based Intrusion identification procedures have imperatives as communicated in the past region, they do even now give basic organizations and this normal us to choose how information mining could be used as a piece of a correlative way to deal with existing measures and improves it.

scholarly journals The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2559 ◽  
Author(s):  
Celestine Iwendi ◽  
Suleman Khan ◽  
Joseph Henry Anajemba ◽  
Mohit Mittal ◽  
Mamdouh Alenezi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Ensemble Models ◽  
Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

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.

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 Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms

2021 ◽  
Vol 19 (2) ◽  
pp. 2030-2042
Author(s):  
Yue Li ◽  
◽  
Wusheng Xu ◽  
Wei Li ◽  
Ang Li ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Method ◽  
Detection System ◽  
Performance Comparison ◽  
Tree Model ◽  
Network Intrusion ◽  
Id3 Algorithm

<abstract> <p>Intrusion detection system plays an important role in network security. Early detection of the potential attacks can prevent the further network intrusion from adversaries. To improve the effectiveness of the intrusion detection rate, this paper proposes a hybrid intrusion detection method that utilizes ADASYN (Adaptive Synthetic) and the decision tree based on ID3 algorithm. At first, the intrusion detection dataset is transformed by coding technology and normalized. Subsequently, the ADASYN algorithm is applied to implement oversampling on the training set, and the ID3 algorithm is employed to build a decision tree model. In addition, the model proposed by the research is evaluated by accuracy, precision, recall, and false alarm rate. Besides, a performance comparison is conducted with other models. Consequently, it is found that the combined model based on ADASYN and ID3 decision tree proposed in this research possesses higher accuracy as well as lower false alarm rate, which is more suitable for intrusion detection tasks.</p> </abstract>

scholarly journals Reducing the False Alarm Rate in Intrusion Detection System by Providing Authentication and Improving the Efficiency of Intrusion Detection System by using Filtered Clusterer Algorithm using Weka Tool

2021 ◽  
Vol 10 (4) ◽  
pp. 134-142
Author(s):  
Pratik Jain ◽  
Ravikant Kholwal ◽  
Tavneet Singh Khurana,
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
False Positive ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positives ◽  
Cyber Safety ◽  
Authentication System ◽  
Similar Issue

An IDS supervises network traffic by searching for skeptical activities and previously determined threats and sends alerts when detected. In the current times, the splendors of Intrusion detection still prevail censorial in cyber safety, but maybe not as a lasting resolution. To study a plant, one must start with roots, so Cambridge dictionary defines an intrusion as "an occasion when someone goes into an area or situation where they're not wanted or expected to be". For understanding the article, we will characterize interruption as any network movement or unapproved framework identified with one or more PCs or networks. This is an interpretation of permissible use of a system attempting to strengthen his advantages to acquire more noteworthy access to the framework that he is at present endowed, or a similar client attempting to associate with an unapproved far-off port of a server. These are the interruptions which will cause from the surface world, a bothered ex-representative who was terminated recently, or from your reliable staff. In this proviso, the fair information is found as an attack when the case is a false positive. Here they are zeroing in on this issue with a representation and offering one answer for a similar issue. The KDD CUP 1999 informational index is utilized. Here we dropped the number of counts and considered the OTP authentication system. In the result of this test, it may be very well seen that on the off chance that a class has a higher number of checks, at that point this class is believed to be an anomaly class. In any case, it will be considered an oddity if the genuine individual is passing the edge esteem is considered an intruder. One arrangement is proposed to distinguish the genuine individual and to eliminate false positives.

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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