An approach for efficient intrusion detection for KDD dataset: A survey

Sharma, Namita; Gaur, Bhupesh

doi:10.19101/ijatee.2016.318006

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…The major challenge field of network intrusion detection is to shape the area of intrusion and to reason on the model. Knowledge engineering approach and data mining approach [6] are the basic flavors of designing a domain model in IDS. All the people who are experts in that domain in along with one or more knowledge engineers and recognize the relations connecting domain variables by general manual approach.…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

Murthy¹,

Satyanarayana²

2018

IJET

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Today, there is a far reaching of Internet benefits everywhere throughout the world, numerous sorts and vast number of security dangers are expanding. Since it isn't in fact possible to assemble a framework without any vulnerability, Intrusion Detection System (IDS), which can successfully distinguish Intrusion, gets to have pulled in consideration. Intrusion detection can be characterized as the way toward distinguishing irregular, unauthorized or unapproved action that objective is to target a system and its assets. IDS plays a very important role for analyzing the network passage, also it assumes a key part to analyze the system activity log and each log is portrayed by huge arrangement of highlights and it requires tremendous computational preparing force and time for the characterization procedure. This work proposes filter based feature selection methods to predict intrusion with Feature based Mutual Information Feature Selection Support Vector Machine (FMIFSSVM), Feature based Liner Correlation Feature Selection Support Vector Machine (FLCFSSVM), misuses SVM, anomaly SVM and Bayesian methods. The performances of these methods are considered by using the intrusion detection calculation data set called Knowledge Discovery in Databases (KDD) cup 99. Detection Rate (DR), False Alarm Rate (FAR) and Percentage of Successful Prediction (PSP) are the major performance measures studied in this work.

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Section: Introductionmentioning

confidence: 99%

Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

Murthy¹,

Satyanarayana²

2018

IJET

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“…In this paper we have mainly focused on data mining and evolutionary algorithm based detection. It is the technique of pernicious assaults from the system and framework when it is as of now correspondence or expelling data in the steady condition [2,3]. Since its creation, interference recognizable proof has been one of the key parts in achieving information security.…”

Section: Introductionmentioning

confidence: 99%

A review on intrusion detection system based on data mining and evolutionary algorithms

Gupta¹,

Singh²

2018

IJATEE

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Intrusion detection is the procedure for determining intrusions in the network. This paper explores the methodology in the direction of intrusion detection system. It explores the possibility of enhancement and propounding the advantages.This study helps in exploring the method analytically, methodically and experimentally. This paper lists the gaps and the advantages, so that future framework can be design to enhance the efficiency. It also provides the detail discussion based on the attributes and parameters variations. Finally future suggestions have been listed.

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“…Their IDS used a learning algorithm based on the SVM and a detection technique based on the attack signatures and their combined model of IDS achieved a higher rate of intrusion detection almost 98% with a number very reduces false alarms near 2%. N. Sharma et al [4] have observed several research works and they have compared the resulting discussions by their techniques. This paper provided a direction in the face of Intrusion detection improvement and suggested just like "The detection approach can be better at detecting R2L and U2R attacks more efficiently as well as anomaly detection approach, which is better at detecting attacks at the absence of match signatures as provided in the misuse rule files", "Hybridization of Association and Optimization can provide better detection.…”

mentioning

confidence: 99%

An Intrusion Detection System for Identifying Attacks using Classification Technique

Tiwari¹

2017

IJRASET

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Information security is one of the important role to protect the information from unauthorized person. Intrusion detection is a classifier to classify the data as normal and various types of attacks. Data mining based decision tree algorithm play very important role to develop the robust IDS to classify the attacks which is harmful for our system. In this research work, used decision tree techniques as classifier to classify the attacks. We have also develop the robust ensemble model which is combination of C4.5, Simple CART and decision tree that gives better accuracy. Our proposed ensemble model gives 99.70% with 80-20% training -testing partition. We have also applied the feature selection technique to computationally increase the performance of model. Our proposed model gives 99.80% in 11 features with info gain feature selection technique while 98.80% in 16 features with gain ratio feature selection technique. Keywords: Intrusion Detection System, Classifier, Attack. I.INTRODUCTION Now a day's data is increasing day by day in every organization. To secure such information is one of the most important issue for every organization. Information security is one of the important issues to protect the information from unauthorized access. An intrusion detection system is used to detect several types of malicious behaviors that can compromise the security and trust of computer system. Classification plays very important role to classify the unwanted data. Actually IDS is a classifier that classifies the different types of attacks and normal data. In this research work, we have used decision tree algorithm to develop the classifier which classify the normal and different types of attacks. Various researcher have worked to develop the IDS. J. Jabez et al. [2] have presented the details of new approach called Outlier Detection approach to detect the intrusion in the computer network. Their training model was consist of big datasets with distributed environment that improves the performance of Intrusion detection system. There proposed approach was also been tested with the KDD data sets that are received from real world. Experimental results shows that proposed IDS system took less execution time and storage to predict and also the performance of proposed IDS is better than that of other existing machine learning approaches and can significantly detect almost all anomaly data in the computer network. Y. Maleh et al. [3] have evaluated the performance of their intrusion detection model using KDDcup'99 database and they studied the variations in detection rate and false positive, when the number of IDS increases in the network. They proposed a hybrid intrusion detection model for WSN. Their IDS used a learning algorithm based on the SVM and a detection technique based on the attack signatures and their combined model of IDS achieved a higher rate of intrusion detection almost 98% with a number very reduces false alarms near 2%. N. Sharma et al. [4] have observed several research works and they have compared t...

show abstract

An approach for efficient intrusion detection for KDD dataset: A survey

Cited by 4 publications

References 20 publications

Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

A review on intrusion detection system based on data mining and evolutionary algorithms

An Intrusion Detection System for Identifying Attacks using Classification Technique

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