International Journal of Computer Engineering and Applications, Volume XI, Issue XII, Dec. 17, ISSN
|
|
- Sybil French
- 5 years ago
- Views:
Transcription
1 RULE BASED CLASSIFICATION FOR NETWORK INTRUSION DETECTION SYSTEM USING USNW-NB 15 DATASET Dr C Manju Assistant Professor, Department of Computer Science Kanchi Mamunivar center for Post Graduate Studies, Lawspet, Puducherry ABSTRACT: Communication plays a vital role in information technology. It involves transfer of data from one place to another. An intrusion detection system is used to detect and manage internal and external attacks and other threats such as botnets, phishing spoofing etc. Here in this paper, evaluation of Network Intrusion Detection Systems is dealt with using USNWNB 15 dataset and rule based classifiers. Direct and Indirect method of analysis is done using Ripper, One-R, RIDOR, Decision Table and PART procedures. After evaluation and Analysis, it is found that PART classifier which is an indirect method of rule based classifiers is best in accuracy and error reduction compared to other classifiers. Keywords: Intrusion Detection System, USNW-NB15 dataset, Rule Based Classifiers, Direct Method, Indirect Method [1] INTRODUCTION Security in information technology is very important when transmission of data is involved. IDS deals in detecting and managing various attacks that happen during the process of communication. IDS can be classified as Host based and Network based. Host based concerned with local attacks where as Network based IDS on overall network activities [1][2]. Dr C Manju 130
2 RULE BASED CLASSIFICATION FOR NETWORK INTRUSION DETECTION SYSTEM USING USNW-NB 15 DATASET IDS can be modelled using analysis approach which monitors against predetermined attack list or signatures. It is based on matching signature system hence can be focused only on known attacks. Next is anomaly based approach which makes use of state of network traffic and report whether it has normal traffic or anomaly in it. Main aim of IDS is to generate and integrate Network and Host based approaches for better detection. Many IDS schemes can be developed for detecting novel attacks more than individual incantations. Evaluation of network data is done using various available data sets. [2] DATA SET DESCRIPTION Evaluation of network intrusion data system was done by using KDD98, KDDCUP99, NSDLKDD benchmark data set. These data sets are very old and cannot take care of current topology and traffic in the network. KDDCUP [3] dataset contains a large number of redundant records and also multiple missing data. NSDL is another data set which is a modification of KDDCUP but they cannot be used as perfect data set in modern network and traffic environment [4]. The Australian center for cyber security research group created data set called UNSW-NB15 [4] data set to evaluate NIDS. The IXIA perfect system tool is utilized in cyber range lab ACCS to create a modern and abnormal dataset. The dataset contains 49 fields and nine different types of attacks namely Fuzzers, Backdoors, Analysis, DOS, Exploits, Generic, Reconnaissance,shell code and Worms,[4].The dataset data can be categorized into details of flow features(which contains source,destination address, port address, Transmission protocol),basic features(data transfer details, load, services),time features, connection features and labelled features. [3] RULE BASED CLASSIFICATION Mining is the process of extracting knowledge from available datasets. Analysis of data can be used for extracting models, specifying classes or to predict what will happen. Classification can be used to analyse categorized labels and used to predict what will happen. Different classification models are available such as Statistical models, Fuzzy models, Rule based models, Ensemble method and Probabilistic method [5]. The rule based model generates a set of rules for prediction of output. A rule is actually a condition of the form (Condition) - y where condition is conjunction of attribute tests and y is a class label. A rule r covers an instance x if the attribute of instance satisfies the condition of rule. Main advantages of rule based classifiers are it is highly expressive as decision trees and easy to interpret and generate. New instances can be easily classified by rule based system. The rules can be mutually exclusive in which classifier contains rule that is independent of each other and exhaustive which accounts for every combination of attribute values [6]. There are two ways of building rules. They are direct method and indirect method. Direct method extracts rules directly from data and indirect method from other classification models. In this paper an analysis of direct method and indirect method is done by using classification algorithms and evaluation is done on the result. [3.1] Direct Method It starts with empty set of rules and the rules are generated directly from data. The rules are then pruned and simplified. Quality of classification rule can be evaluated by coverage and accuracy. The coverage is fraction of records that satisfy the antecedent of a rule and accuracy is fraction of records covered by rule that belongs to class on RHS. Various methods of classifiers are available under method. Here, RIPPER, RIDOR, One-R classifiers are studied and analyzed. Dr C Manju 131
3 A. One-R This method is used for finding relations between various variables in datasets. The method creates rule for each predictor and makes the rule assign value of each target class, It also calculates total error of rule of each predictor. Rules generated are as below < > Exploits < > Generic < > Fuzzers >= > Generic (55580/81694 instances correct) B. RIDOR Ridor is ripple down rule leaner, which generates a default rule first and use incremental reduced error pruning is it used to find exceptions with smallest error rate [7]. Except (id > ) and (id <= ) => attack_cat = Generic (3.0/0.0) [2.0/1.0] Except (id > ) and (id <= ) and (id > ) => attack_cat = DoS (33.0/0.0) [13.0/1.0] Except (id <= ) and (id > ) => attack- cat = Generic (25.0/0.0) [9.0/0.0].The values specify accuracy and coverage. Total number of rules (incl. the default rule) is and time taken to build model: seconds. C. DECISION TABLE It specifies only logic rules and is used to find quality of decision. It contains classifier rules which are created by a simple decision table majority classifier. It returns the majority of the training sets if the decision table matching the new instance is empty [8]. The testing resulted in forward searching with 47 evaluated subsets. The number of rules generated is 4326 and time for generating them is s. D. RIPPER RIPPER is repeated Incremental Pruning to Produce Error Reduction. It divides training set into growing and pruning sets [7]. It is easy to interpret the results and applicable for certain kind of problems. The sample rule generated is as follows. (label = Attacked) and (dmean >= 45) and (dmean >= 107) and (sttl >= 254) => attack_cat=exploits (230.0/48.0) (label = Attacked) and (sinpkt >= 0.024) and (sinpkt <= ) and (dloss >= 2) and (dmean <= 55) => attack_cat=exploits (88.0/17.0) The value (230.0/48.0) specifies the coverage. It means, out of 230 instances 48 instances in data set is covered by the rule and others are not covered by the rule. The number of rules generated is 39 and execution time is 8049 s. Dr C Manju 132
4 RULE BASED CLASSIFICATION FOR NETWORK INTRUSION DETECTION SYSTEM USING USNW-NB 15 DATASET [3.2] INDIRECT METHOD The rules in this method are extracted from other classification models. The rules generated here are mutually exclusive and exhaustive. A. PART This is a new method for rule induction in which extract rules from an unpruned decision tree in the attempt to avoid problems. Unlike both C4.5 and RIPPER, it does not need to perform global optimization to produce accurate rule sets and the added simplicity is its main advantage. It will create partial decision tree on the current state of instances and rules are created from decision tree. It is a separate divide and conquer rule proposed by EIBE [8][9]. It generates decision list which are ordered set of rules and are as below id <= AND inpkt <= AND id <= : Fuzzers (5.0/2.0) id <= AND sinpkt <= AND d > : DoS (3.0/1.0) [4] EXPERIMENTAL ANALYSIS The analysis of the above classifiers is done using WEKA tool [8]. The evaluation is done using mining techniques which include pre-processing and filtration techniques. The pre-processing is done using CfsSubsetEval which Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them and corresponding search is done using Best First which searches the space of attribute subsets by greedy hill climbing augmented with a backtracking facility. After the pre-processing and filtration, the number of attributes were reduced from 49 to 10. After that, data is passed through 10-fold cross validation. This method splits the dataset into 10 folds and for each 10 folds it builds a model on 9 sets of datasets. It records the error on each prediction and repeat the process until each of the 10 folds has served as test set. The dataset is analyzed through various classifiers in the category of direct and indirect method. They are evaluated for accuracy and error parameters. [4.1] ACCURACY PARAMETERS Accuracy parameters include Precision, True Positive, F-measure, ROC and Kappa statistic. Precision measure is the accuracy of the dataset and is evaluated based on attack so that intrusion detection data can be evaluated and find how accurate data is. It also specifies the attack on the data [8]. Accuracy refers to the ability of model to correctly predict the attacks of new or previously unseen data. Also, it is the percentage of correctly classified by the classifier testing set. The Precision is defined by TP/(TP+FP). Recall r is the number of correctly classified positive data divided by actual positive data in dataset. R = TP/(TP+FN). Receive Operating Characteristics Curve is the plot of True Positive Rate against False Positive Rate which also provides accuracy of classifier on the data [9]. It shows the tradeoff between sensitivity and specificity. The area under ROC is measurement of accuracy. Dr C Manju 133
5 Fig.1 ROC curve for PART classification The rule based system are evaluated and the accuracy parameters are as specified in the table CLASSIFIERS TP FP PRECISION F MEASURE ROC KAPPA DT JRIP One-R PART RIDOR Table:1 showing accuracy parameters of various classifiers The graph representing the above data is as follows Fig 2. Graph representing accuracy parameters From the graph and table, it is found that in evaluation based on accuracy parameters, the PART classification algorithm has increased accuracy rate precision, Kappa statistic, True positive and False Negative. The area Under ROC that means accuracy is high with PART classifier. From this, Dr C Manju 134
6 RULE BASED CLASSIFICATION FOR NETWORK INTRUSION DETECTION SYSTEM USING USNW-NB 15 DATASET we can conclude that PART classifier which is an indirect method of the classification is most suitable method of evaluating the USNWNB15 dataset. [4.2] Error rate evaluation Parameters The error evaluation parameters include Root Mean Squared error which shows the error in the predicated actual classes which the instance dataset belongs to [10][11]. RMSE values should be lower for more accurate classification rules. Mean absolute error measures the average magnitude of errors. The classifiers are evaluated for relative absolute error (RAE) and root relative squared error (RRSE) also. Classifiers MAE RMSE RAE RRSE DT RIDOR JRIP One-R PART Table 2: Error parameters of various classifiers Fig 3: Graph representing error rate In the figure, PART algorithm have reduced error rate and have higher performance. RIDOR classification found to have next less error rate. So we can conclude that these two provide higher performance than other classifiers under study. [5] CONCLUSION The Intrusion Detection Systems plays a vital role in the secure communication of data. The system is evaluated through USNW-NB 15 dataset using various rule based classification algorithms. In this paper performance of rule classifiers namely RIDOR, RIPPER, Decision Table, PART, One-R is analyzed using the cross- fold validation. The performance is evaluated for accuracy and error parameters. From the result it is evident that PART classification which is an indirect method of rule based classification is the better method in accuracy and reduced error rate than when compared with other system under study. Dr C Manju 135
7 REFERNCES [1] Krishna Kant Tiwari, Susheel Tiwari, Sriram Yadav Intrusion Detection Using Data Mining Techniques International Journal of Advanced Computer Technology (IJACT). [2] Trupti Phutane, Apashabi Pathan A Survey of Intrusion Detection System Using Different Data Mining Techniques International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 11, November [3] UNSW-NB15: A Comprehensive Data set for Network Intrusion Detection systems (UNSW-NB15 Network Data Set) Nour Moustafa, University of New South Wales at the Australian Defence Force Academy Canberra,Australia.Conference Paper November2015DOI: /MilCIS [4] Safaa O. Al-mamory, Firas S. Jassim Evaluation of Different Data Mining Algorithms with KDD CUP 99 Data Set Journal of Babylon University/Pure and Applied Sciences/ No.(8)/ Vol.(21): 2013 [5] Dr C Manju, Performance Evaluation of Intrusion Detection System Using Classification Algorithms, International Journal of Innovative Research in Science, Engineering and Technology Vol. 6, Issue 7, July [6] S Vijayarani, S, M. Muthulakshmi. Evaluating The Efficiency Of Rule Techniques for File Classification. International Journal of Research in Engineering and Technology eissn: ISSN: [7] Gaines, B.R., Paul Compton, J Induction of Ripple-Down Rules Applied to Modeling Large Databases. [8] Petra Kralj Novak,,Intell. Inf. Syst. 5(3): ,2009 : Classification in WEKA. [9] Ali, Shawkat, and Kate A. Smith. "On learning algorithm selection for classification." Applied Soft Computing 6.2 (2006): [10] Pankaj Singh, Sudhakar Singh, Comparative Study of Data Mining Algorithms through Weka, International Journal of Emerging Research in Management &Technology, ISSN: (Volume-4, Issue-9). [11] Qin, Biao, et al. "A rule-based classification algorithm for uncertain data." Data Engineering, ICDE'09. IEEE 25th International Conference on. IEEE, Dr C Manju 136
Ripple Down Rule learner (RIDOR) Classifier for IRIS Dataset
Ripple Down Rule learner (RIDOR) Classifier for IRIS Dataset V.Veeralakshmi Department of Computer Science Bharathiar University, Coimbatore, Tamilnadu veeralakshmi13@gmail.com Dr.D.Ramyachitra Department
More informationA Comparative Study of Selected Classification Algorithms of Data Mining
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 6, June 2015, pg.220
More informationPerformance Evaluation of Rule Based Classification Algorithms
Performance Evaluation of Rule Based Classification Algorithms Aditi Mahajan 1, Anita Ganpati 2 1 Research Scholar, Department of Computer Science, Himachal Pradesh University Shimla, India 2 Associate
More informationEVALUATING THE EFFICIENCY OF RULE TECHNIQUES FOR FILE CLASSIFICATION
EVALUATING THE EFFICIENCY OF RULE TECHNIQUES FOR FILE CLASSIFICATION S. Vijayarani 1, M. Muthulakshmi 2 1 Assistant Professor, 2 M. Phil Research Scholar, Department of Computer Science, School of Computer
More informationComparative Study on Classification Meta Algorithms
Comparative Study on Classification Meta Algorithms Dr. S. Vijayarani 1 Mrs. M. Muthulakshmi 2 Assistant Professor, Department of Computer Science, School of Computer Science and Engineering, Bharathiar
More informationData Mining. 3.3 Rule-Based Classification. Fall Instructor: Dr. Masoud Yaghini. Rule-Based Classification
Data Mining 3.3 Fall 2008 Instructor: Dr. Masoud Yaghini Outline Using IF-THEN Rules for Classification Rules With Exceptions Rule Extraction from a Decision Tree 1R Algorithm Sequential Covering Algorithms
More informationData Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.4. Spring 2010 Instructor: Dr. Masoud Yaghini Outline Using IF-THEN Rules for Classification Rule Extraction from a Decision Tree 1R Algorithm Sequential Covering Algorithms
More informationBest First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis
Best First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis CHAPTER 3 BEST FIRST AND GREEDY SEARCH BASED CFS AND NAÏVE BAYES ALGORITHMS FOR HEPATITIS DIAGNOSIS 3.1 Introduction
More informationWeka ( )
Weka ( http://www.cs.waikato.ac.nz/ml/weka/ ) The phases in which classifier s design can be divided are reflected in WEKA s Explorer structure: Data pre-processing (filtering) and representation Supervised
More informationData Mining and Knowledge Discovery Practice notes 2
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
More informationComparative Study of Instance Based Learning and Back Propagation for Classification Problems
Comparative Study of Instance Based Learning and Back Propagation for Classification Problems 1 Nadia Kanwal, 2 Erkan Bostanci 1 Department of Computer Science, Lahore College for Women University, Lahore,
More informationEffect of Principle Component Analysis and Support Vector Machine in Software Fault Prediction
International Journal of Computer Trends and Technology (IJCTT) volume 7 number 3 Jan 2014 Effect of Principle Component Analysis and Support Vector Machine in Software Fault Prediction A. Shanthini 1,
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 8.11.2017 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
More informationA Review on Performance Comparison of Artificial Intelligence Techniques Used for Intrusion Detection
A Review on Performance Comparison of Artificial Intelligence Techniques Used for Intrusion Detection Navaneet Kumar Sinha 1, Gulshan Kumar 2 and Krishan Kumar 3 1 Department of Computer Science & Engineering,
More informationInternational Journal of Scientific Research & Engineering Trends Volume 4, Issue 6, Nov-Dec-2018, ISSN (Online): X
Analysis about Classification Techniques on Categorical Data in Data Mining Assistant Professor P. Meena Department of Computer Science Adhiyaman Arts and Science College for Women Uthangarai, Krishnagiri,
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/11/16 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
More informationINTRUSION DETECTION MODEL IN DATA MINING BASED ON ENSEMBLE APPROACH
INTRUSION DETECTION MODEL IN DATA MINING BASED ON ENSEMBLE APPROACH VIKAS SANNADY 1, POONAM GUPTA 2 1Asst.Professor, Department of Computer Science, GTBCPTE, Bilaspur, chhattisgarh, India 2Asst.Professor,
More informationStudy on Classifiers using Genetic Algorithm and Class based Rules Generation
2012 International Conference on Software and Computer Applications (ICSCA 2012) IPCSIT vol. 41 (2012) (2012) IACSIT Press, Singapore Study on Classifiers using Genetic Algorithm and Class based Rules
More informationDr. Prof. El-Bahlul Emhemed Fgee Supervisor, Computer Department, Libyan Academy, Libya
Volume 5, Issue 1, January 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Performance
More informationCredit card Fraud Detection using Predictive Modeling: a Review
February 207 IJIRT Volume 3 Issue 9 ISSN: 2396002 Credit card Fraud Detection using Predictive Modeling: a Review Varre.Perantalu, K. BhargavKiran 2 PG Scholar, CSE, Vishnu Institute of Technology, Bhimavaram,
More informationHybrid Feature Selection for Modeling Intrusion Detection Systems
Hybrid Feature Selection for Modeling Intrusion Detection Systems Srilatha Chebrolu, Ajith Abraham and Johnson P Thomas Department of Computer Science, Oklahoma State University, USA ajith.abraham@ieee.org,
More informationResearch Article International Journals of Advanced Research in Computer Science and Software Engineering ISSN: X (Volume-7, Issue-6)
International Journals of Advanced Research in Computer Science and Software Engineering Research Article June 17 Artificial Neural Network in Classification A Comparison Dr. J. Jegathesh Amalraj * Assistant
More informationNETWORK INTRUSION DETECTION SYSTEM BASED ON MODIFIED RANDOM FOREST CLASSIFIERS FOR KDD CUP-99 AND NSL-KDD DATASET
NETWORK INTRUSION DETECTION SYSTEM BASED ON MODIFIED RANDOM FOREST CLASSIFIERS FOR KDD CUP-99 AND NSL-KDD DATASET Prakash Chandra 1, Prof. Umesh Kumar Lilhore 2, Prof. Nitin Agrawal 3 M. Tech. Research
More informationERA -An Enhanced Ripper Algorithm to Improve Accuracy in Software Fault Prediction
ERA -An Enhanced Ripper Algorithm to Improve Accuracy in Software Fault Prediction N.Vinothini # 1 (Research Scholar) School Of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli,
More informationCS570: Introduction to Data Mining
CS570: Introduction to Data Mining Classification Advanced Reading: Chapter 8.4 & 8.5 Han, Chapters 4.5 & 4.6 Tan Anca Doloc-Mihu, Ph.D. Slides courtesy of Li Xiong, Ph.D., 2011 Han, Kamber & Pei. Data
More informationIMPLEMENTATION OF CLASSIFICATION ALGORITHMS USING WEKA NAÏVE BAYES CLASSIFIER
IMPLEMENTATION OF CLASSIFICATION ALGORITHMS USING WEKA NAÏVE BAYES CLASSIFIER N. Suresh Kumar, Dr. M. Thangamani 1 Assistant Professor, Sri Ramakrishna Engineering College, Coimbatore, India 2 Assistant
More informationFeature Selection in UNSW-NB15 and KDDCUP 99 datasets
Feature Selection in UNSW-NB15 and KDDCUP 99 datasets JANARTHANAN, Tharmini and ZARGARI, Shahrzad Available from Sheffield Hallam University Research Archive (SHURA) at: http://shura.shu.ac.uk/15662/ This
More informationInternational Journal of Software and Web Sciences (IJSWS)
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International
More informationModeling Intrusion Detection Systems With Machine Learning And Selected Attributes
Modeling Intrusion Detection Systems With Machine Learning And Selected Attributes Thaksen J. Parvat USET G.G.S.Indratrastha University Dwarka, New Delhi 78 pthaksen.sit@sinhgad.edu Abstract Intrusion
More informationCluster Based detection of Attack IDS using Data Mining
Cluster Based detection of Attack IDS using Data Mining 1 Manisha Kansra, 2 Pankaj Dev Chadha 1 Research scholar, 2 Assistant Professor, 1 Department of Computer Science Engineering 1 Geeta Institute of
More informationDeep Learning Approach to Network Intrusion Detection
Deep Learning Approach to Network Intrusion Detection Paper By : Nathan Shone, Tran Nguyen Ngoc, Vu Dinh Phai, Qi Shi Presented by : Romi Bajracharya Overview Introduction Limitation with NIDS Proposed
More informationIntrusion Detection Using Data Mining Technique (Classification)
Intrusion Detection Using Data Mining Technique (Classification) Dr.D.Aruna Kumari Phd 1 N.Tejeswani 2 G.Sravani 3 R.Phani Krishna 4 1 Associative professor, K L University,Guntur(dt), 2 B.Tech(1V/1V),ECM,
More informationWhat is Learning? CS 343: Artificial Intelligence Machine Learning. Raymond J. Mooney. Problem Solving / Planning / Control.
What is Learning? CS 343: Artificial Intelligence Machine Learning Herbert Simon: Learning is any process by which a system improves performance from experience. What is the task? Classification Problem
More informationMachine Learning and Bioinformatics 機器學習與生物資訊學
Molecular Biomedical Informatics 分子生醫資訊實驗室 機器學習與生物資訊學 Machine Learning & Bioinformatics 1 Evaluation The key to success 2 Three datasets of which the answers must be known 3 Note on parameter tuning It
More informationCollaborative Anomaly Detection Framework for handling Big Data of Cloud Computing
Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing School of Engineering and Information Technology University of New South Wales @ Canberra Nour Moustafa, Gideon Creech,
More informationCS4491/CS 7265 BIG DATA ANALYTICS
CS4491/CS 7265 BIG DATA ANALYTICS EVALUATION * Some contents are adapted from Dr. Hung Huang and Dr. Chengkai Li at UT Arlington Dr. Mingon Kang Computer Science, Kennesaw State University Evaluation for
More informationIntrusion Detection System with FGA and MLP Algorithm
Intrusion Detection System with FGA and MLP Algorithm International Journal of Engineering Research & Technology (IJERT) Miss. Madhuri R. Yadav Department Of Computer Engineering Siddhant College Of Engineering,
More informationAn Ensemble Data Mining Approach for Intrusion Detection in a Computer Network
International Journal of Science and Engineering Investigations vol. 6, issue 62, March 2017 ISSN: 2251-8843 An Ensemble Data Mining Approach for Intrusion Detection in a Computer Network Abisola Ayomide
More informationFeature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods
Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods Zahra Karimi Islamic Azad University Tehran North Branch Dept. of Computer Engineering Tehran, Iran Mohammad Mansour
More informationFlow-based Anomaly Intrusion Detection System Using Neural Network
Flow-based Anomaly Intrusion Detection System Using Neural Network tational power to analyze only the basic characteristics of network flow, so as to Intrusion Detection systems (KBIDES) classify the data
More informationA Network Intrusion Detection System Architecture Based on Snort and. Computational Intelligence
2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 206) A Network Intrusion Detection System Architecture Based on Snort and Computational Intelligence Tao Liu, a, Da
More informationISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 7, January 2015
Intrusion Detection System using Bayesian Approach S. Saravanan, Dr. R M. Chandrasekaran Department of Computer Science & Engineering, Annamalai University Annamalainagar 608 00, Tamil Nadu, India. Abstract
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 5 Issue 4, Jul Aug 2017
International Journal of Computer Science Trends and Technology (IJCST) Volume 5 Issue 4, Jul Aug 17 RESEARCH ARTICLE OPEN ACCESS Classifying Brain Dataset Using Classification Based Association Rules
More informationINTRODUCTION TO MACHINE LEARNING. Measuring model performance or error
INTRODUCTION TO MACHINE LEARNING Measuring model performance or error Is our model any good? Context of task Accuracy Computation time Interpretability 3 types of tasks Classification Regression Clustering
More informationData Mining and Knowledge Discovery Practice notes: Numeric Prediction, Association Rules
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
More informationEvaluation of different biological data and computational classification methods for use in protein interaction prediction.
Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Yanjun Qi, Ziv Bar-Joseph, Judith Klein-Seetharaman Protein 2006 Motivation Correctly
More informationAn Intelligent Clustering Algorithm for High Dimensional and Highly Overlapped Photo-Thermal Infrared Imaging Data
An Intelligent Clustering Algorithm for High Dimensional and Highly Overlapped Photo-Thermal Infrared Imaging Data Nian Zhang and Lara Thompson Department of Electrical and Computer Engineering, University
More informationPreprocessing of Stream Data using Attribute Selection based on Survival of the Fittest
Preprocessing of Stream Data using Attribute Selection based on Survival of the Fittest Bhakti V. Gavali 1, Prof. Vivekanand Reddy 2 1 Department of Computer Science and Engineering, Visvesvaraya Technological
More informationAn Information-Theoretic Approach to the Prepruning of Classification Rules
An Information-Theoretic Approach to the Prepruning of Classification Rules Max Bramer University of Portsmouth, Portsmouth, UK Abstract: Keywords: The automatic induction of classification rules from
More informationA Comparison of Decision Tree Algorithms For UCI Repository Classification
A Comparison of Decision Tree Algorithms For UCI Repository Classification Kittipol Wisaeng Mahasakham Business School (MBS), Mahasakham University Kantharawichai, Khamriang, Mahasarakham, 44150, Thailand.
More informationFeature Selection in the Corrected KDD -dataset
Feature Selection in the Corrected KDD -dataset ZARGARI, Shahrzad Available from Sheffield Hallam University Research Archive (SHURA) at: http://shura.shu.ac.uk/17048/ This document is the author deposited
More informationCLASSIFICATION OF ARTIFICIAL INTELLIGENCE IDS FOR SMURF ATTACK
CLASSIFICATION OF ARTIFICIAL INTELLIGENCE IDS FOR SMURF ATTACK N.Ugtakhbayar, D.Battulga and Sh.Sodbileg Department of Communication technology, School of Information Technology, National University of
More informationEfficient Pairwise Classification
Efficient Pairwise Classification Sang-Hyeun Park and Johannes Fürnkranz TU Darmstadt, Knowledge Engineering Group, D-64289 Darmstadt, Germany Abstract. Pairwise classification is a class binarization
More informationIntrusion detection in computer networks through a hybrid approach of data mining and decision trees
WALIA journal 30(S1): 233237, 2014 Available online at www.waliaj.com ISSN 10263861 2014 WALIA Intrusion detection in computer networks through a hybrid approach of data mining and decision trees Tayebeh
More informationA Multi-agent Based Cognitive Approach to Unsupervised Feature Extraction and Classification for Network Intrusion Detection
Int'l Conf. on Advances on Applied Cognitive Computing ACC'17 25 A Multi-agent Based Cognitive Approach to Unsupervised Feature Extraction and Classification for Network Intrusion Detection Kaiser Nahiyan,
More informationDESIGN AND EVALUATION OF MACHINE LEARNING MODELS WITH STATISTICAL FEATURES
EXPERIMENTAL WORK PART I CHAPTER 6 DESIGN AND EVALUATION OF MACHINE LEARNING MODELS WITH STATISTICAL FEATURES The evaluation of models built using statistical in conjunction with various feature subset
More informationEnhancing Forecasting Performance of Naïve-Bayes Classifiers with Discretization Techniques
24 Enhancing Forecasting Performance of Naïve-Bayes Classifiers with Discretization Techniques Enhancing Forecasting Performance of Naïve-Bayes Classifiers with Discretization Techniques Ruxandra PETRE
More informationComparative Analysis of Classification Algorithms on KDD 99 Data Set
I. J. Computer Network and Information Security, 2016, 9, 34-40 Published Online September 2016 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijcnis.2016.09.05 Comparative Analysis of Classification
More informationNetwork Traffic Measurements and Analysis
DEIB - Politecnico di Milano Fall, 2017 Sources Hastie, Tibshirani, Friedman: The Elements of Statistical Learning James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning Andrew Ng:
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/01/12 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
More informationREVIEW OF VARIOUS INTRUSION DETECTION METHODS FOR TRAINING DATA SETS
REVIEW OF VARIOUS INTRUSION DETECTION METHODS FOR TRAINING DATA SETS Nilofer Shoaib Khan 1 and Prof. Umesh Lilhore 2 1 M.Tech Scholar NIIST Bhopal (MP) 2 PG In charge NIIST Bhopal (MP) Abstract-In the
More informationBayesian Learning Networks Approach to Cybercrime Detection
Bayesian Learning Networks Approach to Cybercrime Detection N S ABOUZAKHAR, A GANI and G MANSON The Centre for Mobile Communications Research (C4MCR), University of Sheffield, Sheffield Regent Court, 211
More informationMachine Learning Classifiers for Network Intrusion Detection
Int'l Conf. on Advances on Applied Cognitive Computing ACC'18 55 Machine Learning Classifiers for Network Intrusion Detection Samilat Kaiser and Ken Ferens Department of Electrical and Computer Engineering,
More informationA Survey And Comparative Analysis Of Data
A Survey And Comparative Analysis Of Data Mining Techniques For Network Intrusion Detection Systems In Information Security, intrusion detection is the act of detecting actions that attempt to In 11th
More informationClassification with Decision Tree Induction
Classification with Decision Tree Induction This algorithm makes Classification Decision for a test sample with the help of tree like structure (Similar to Binary Tree OR k-ary tree) Nodes in the tree
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2013/12/09 1 Practice plan 2013/11/11: Predictive data mining 1 Decision trees Evaluating classifiers 1: separate
More informationA Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection
A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection S. Revathi Ph.D. Research Scholar PG and Research, Department of Computer Science Government Arts
More informationIEE 520 Data Mining. Project Report. Shilpa Madhavan Shinde
IEE 520 Data Mining Project Report Shilpa Madhavan Shinde Contents I. Dataset Description... 3 II. Data Classification... 3 III. Class Imbalance... 5 IV. Classification after Sampling... 5 V. Final Model...
More informationList of Exercises: Data Mining 1 December 12th, 2015
List of Exercises: Data Mining 1 December 12th, 2015 1. We trained a model on a two-class balanced dataset using five-fold cross validation. One person calculated the performance of the classifier by measuring
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/01/12 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
More informationApplication of the Generic Feature Selection Measure in Detection of Web Attacks
Application of the Generic Feature Selection Measure in Detection of Web Attacks Hai Thanh Nguyen 1, Carmen Torrano-Gimenez 2, Gonzalo Alvarez 2 Slobodan Petrović 1, and Katrin Franke 1 1 Norwegian Information
More informationEvaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
More informationIJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 06, 2014 ISSN (online):
IJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 06, 2014 ISSN (online): 2321-0613 IDS Using Classification Teachniques in Weka Environment With Feature Reduction Jyoti
More informationData Mining and Knowledge Discovery Practice notes: Numeric Prediction, Association Rules
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 06/0/ Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
More informationNETWORK FAULT DETECTION - A CASE FOR DATA MINING
NETWORK FAULT DETECTION - A CASE FOR DATA MINING Poonam Chaudhary & Vikram Singh Department of Computer Science Ch. Devi Lal University, Sirsa ABSTRACT: Parts of the general network fault management problem,
More informationAGRICULTURAL SOIL LIME STATUS ANALYSIS USING DATA MINING CLASSIFICATION TECHNIQUES
AGRICULTURAL SOIL LIME STATUS ANALYSIS USING DATA MINING CLASSIFICATION TECHNIQUES 1 Dr. K. Arunesh, 2 V. Rajeswari 1 Department of Computer Science, Sri S. Ranmasamy Naidu Memorial College, (India) 2
More informationMachine Learning Techniques for Data Mining
Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART V Credibility: Evaluating what s been learned 10/25/2000 2 Evaluation: the key to success How
More informationEvaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München
Evaluation Measures Sebastian Pölsterl Computer Aided Medical Procedures Technische Universität München April 28, 2015 Outline 1 Classification 1. Confusion Matrix 2. Receiver operating characteristics
More informationK-Nearest-Neighbours with a Novel Similarity Measure for Intrusion Detection
K-Nearest-Neighbours with a Novel Similarity Measure for Intrusion Detection Zhenghui Ma School of Computer Science The University of Birmingham Edgbaston, B15 2TT Birmingham, UK Ata Kaban School of Computer
More informationAn Anomaly-Based Intrusion Detection System for the Smart Grid Based on CART Decision Tree
An Anomaly-Based Intrusion Detection System for the Smart Grid Based on CART Decision Tree P. Radoglou-Grammatikis and P. Sarigiannidis* University of Western Macedonia Department of Informatics & Telecommunications
More informationLecture Notes on Critique of 1998 and 1999 DARPA IDS Evaluations
Lecture Notes on Critique of 1998 and 1999 DARPA IDS Evaluations Prateek Saxena March 3 2008 1 The Problems Today s lecture is on the discussion of the critique on 1998 and 1999 DARPA IDS evaluations conducted
More informationA Novel Approach for Removal of Redundant Test Cases using Hash Set Algorithm along with Data Mining Techniques
A Novel Approach for Removal of Redundant Test Cases using Hash Set Algorithm along with Data Mining Techniques Pandi Jothi Selvakumar Department of Computer Applications, AVC College (Autonomous), Mayiladuthurai,
More informationDecision Tree Learning
Decision Tree Learning 1 Simple example of object classification Instances Size Color Shape C(x) x1 small red circle positive x2 large red circle positive x3 small red triangle negative x4 large blue circle
More informationPramod Bide 1, Rajashree Shedge 2 1,2 Department of Computer Engg, Ramrao Adik Institute of technology/mumbai University, India
Comparative Study and Analysis of Cloud Intrusion Detection System Pramod Bide 1, Rajashree Shedge 2 1,2 Department of Computer Engg, Ramrao Adik Institute of technology/mumbai University, India ABSTRACT
More informationMultiple Classifier Fusion With Cuttlefish Algorithm Based Feature Selection
Multiple Fusion With Cuttlefish Algorithm Based Feature Selection K.Jayakumar Department of Communication and Networking k_jeyakumar1979@yahoo.co.in S.Karpagam Department of Computer Science and Engineering,
More informationA Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters
A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters Slobodan Petrović NISlab, Department of Computer Science and Media Technology, Gjøvik University College,
More informationK- Nearest Neighbors(KNN) And Predictive Accuracy
Contact: mailto: Ammar@cu.edu.eg Drammarcu@gmail.com K- Nearest Neighbors(KNN) And Predictive Accuracy Dr. Ammar Mohammed Associate Professor of Computer Science ISSR, Cairo University PhD of CS ( Uni.
More informationIntrusion Detection System based on Support Vector Machine and BN-KDD Data Set
Intrusion Detection System based on Support Vector Machine and BN-KDD Data Set Razieh Baradaran, Department of information technology, university of Qom, Qom, Iran R.baradaran@stu.qom.ac.ir Mahdieh HajiMohammadHosseini,
More informationAnomaly Detection in Communication Networks
Anomaly Detection in Communication Networks Prof. D. J. Parish High Speed networks Group Department of Electronic and Electrical Engineering D.J.Parish@lboro.ac.uk Loughborough University Overview u u
More informationClassification. Instructor: Wei Ding
Classification Part II Instructor: Wei Ding Tan,Steinbach, Kumar Introduction to Data Mining 4/18/004 1 Practical Issues of Classification Underfitting and Overfitting Missing Values Costs of Classification
More informationA Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis
A Critical Study of Selected Classification s for Liver Disease Diagnosis Shapla Rani Ghosh 1, Sajjad Waheed (PhD) 2 1 MSc student (ICT), 2 Associate Professor (ICT) 1,2 Department of Information and Communication
More informationThe Explorer. chapter Getting started
chapter 10 The Explorer Weka s main graphical user interface, the Explorer, gives access to all its facilities using menu selection and form filling. It is illustrated in Figure 10.1. There are six different
More informationGurmeet Kaur 1, Parikshit 2, Dr. Chander Kant 3 1 M.tech Scholar, Assistant Professor 2, 3
Volume 8 Issue 2 March 2017 - Sept 2017 pp. 72-80 available online at www.csjournals.com A Novel Approach to Improve the Biometric Security using Liveness Detection Gurmeet Kaur 1, Parikshit 2, Dr. Chander
More informationImplementation of Novel Algorithm (SPruning Algorithm)
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 4, Ver. V (Jul Aug. 2014), PP 57-65 Implementation of Novel Algorithm (SPruning Algorithm) Srishti
More informationGlobal Journal of Engineering Science and Research Management
ADVANCED K-MEANS ALGORITHM FOR BRAIN TUMOR DETECTION USING NAIVE BAYES CLASSIFIER Veena Bai K*, Dr. Niharika Kumar * MTech CSE, Department of Computer Science and Engineering, B.N.M. Institute of Technology,
More informationGlobal Journal of Engineering Science and Research Management
A NOVEL HYBRID APPROACH FOR PREDICTION OF MISSING VALUES IN NUMERIC DATASET V.B.Kamble* 1, S.N.Deshmukh 2 * 1 Department of Computer Science and Engineering, P.E.S. College of Engineering, Aurangabad.
More informationClassification. Slide sources:
Classification Slide sources: Gideon Dror, Academic College of TA Yaffo Nathan Ifill, Leicester MA4102 Data Mining and Neural Networks Andrew Moore, CMU : http://www.cs.cmu.edu/~awm/tutorials 1 Outline
More informationMetrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates?
Model Evaluation Metrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates? Methods for Model Comparison How to
More informationA Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression
Journal of Data Analysis and Information Processing, 2016, 4, 55-63 Published Online May 2016 in SciRes. http://www.scirp.org/journal/jdaip http://dx.doi.org/10.4236/jdaip.2016.42005 A Comparative Study
More informationComparative Study of J48, Naive Bayes and One-R Classification Technique for Credit Card Fraud Detection using WEKA
Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 6 (2017) pp. 1731-1743 Research India Publications http://www.ripublication.com Comparative Study of J48, Naive Bayes
More information