The document proposes a layering based network intrusion detection system to improve detection of network attacks. It selects a small set of important features for each attack type layer, rather than using all features, to build more efficient intrusion detection models. The system is tested on the NSL-KDD intrusion detection dataset using machine learning classifiers like Naive Bayes and Random Forest. The results show the optimal feature selection approach enhances accuracy while reducing computational requirements compared to using all features.
Hybrid Approach for Intrusion Detection Model Using Combination of K-Means Cl...theijes
Any violation of information security policy with malicious intent is regarded as an intrusion. The fast evolving new kind of intrusions poses a very serious threat to system security, although there has been the rapid development of several security tools to counter the growing threats, intrusive activities are still growing. Many Intrusion Detection models have been implemented since the concept of Intrusion Detection emerged, but the majority of the existing Intrusion detection models have many drawbacks which include but not limited to low accuracy in detection, high false alarm rates, adaptability weakness, inability to detect new intrusions etc. The main aim of this study is proposing a model that combined simple K-Means clustering Algorithms and Random Forest classification technique that will have minimum false alarms rate and high accuracy detection rate. The experiment was carried out in WEKA 3.8 using the NSL-KDD dataset to process the dataset and obtained the results. At the end of training and testing of the proposed study, the results indicated that the proposed approach achieved improved accuracy and reduced false alarm rates by 99.98% and 0.14% respectively.
Classifiers are algorithms that map input data to categories in order to build models for predicting unknown data. There are several types of classifiers that can be used including logistic regression, decision trees, random forests, support vector machines, Naive Bayes, and neural networks. Each uses different techniques such as splitting data, averaging predictions, or maximizing margins to classify data. The best classifier depends on the problem and achieving high accuracy, sensitivity, and specificity.
Improving Classifier Accuracy using Unlabeled Data..docbutest
This document describes a method for improving classifier accuracy using unlabeled data in addition to a small set of labeled data. The algorithm builds an initial classifier using just the labeled data, then uses that classifier to label a larger set of unlabeled data. A new classifier is then built using both the original labeled data and the now labeled unlabeled data. Experimental results using three common learning algorithms (neural networks, Naive Bayes, C4.5) on 10 datasets show average accuracy improvements of 5%, 3%, and 8% respectively when incorporating unlabeled data. The results indicate that leveraging unlabeled data can significantly boost classifier performance when labeled data is limited.
This document compares four clustering algorithms (K-means, hierarchical, EM, and density-based) using the WEKA tool. It applies the algorithms to a dataset of software classes and evaluates them based on number of clusters, time to build models, squared errors, and log likelihood. The results show that K-means performs best in terms of time to build models, while density-based clustering performs best in terms of log likelihood. Overall, the document concludes that K-means is the best algorithm for this dataset because it balances low runtime and good clustering accuracy.
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation ...IJECEIAES
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
Machine Learning Algorithms for Image Classification of Hand Digits and Face ...IRJET Journal
This document discusses machine learning algorithms for image classification using five different classification schemes. It summarizes the mathematical models behind each classification algorithm, including Nearest Class Centroid classifier, Nearest Sub-Class Centroid classifier, k-Nearest Neighbor classifier, Perceptron trained using Backpropagation, and Perceptron trained using Mean Squared Error. It also describes two datasets used in the experiments - the MNIST dataset of handwritten digits and the ORL face recognition dataset. The performance of the five classification schemes are compared on these datasets.
Intrusion Detection System for Classification of Attacks with Cross Validationinventionjournals
Now days, due to rapidly uses of internet, the patterns of network attacks are increasing. There are various organizations and institutes are using internet and access or share the sensitive information in network. To protect information from unauthorized or intruders is one of the important issues. In this paper, we have used decision tree techniques like C4.5 and CART as classifier for classification of attacks. We have proposed an ensemble model that is combination of C4.5 and Classification and Regression Tree (CART) as robust classifier for classification of attacks. We have used NSL-KDD data set with binary and multiclass problem with 10-fold cross validation. The proposed ensemble model gives satisfactory accuracy as 99.67% and 99.53% in case of binary class and multiclass NSL-KDD data set respectively.
This document compares the k-means and grid density clustering algorithms. K-means partitions data into k clusters based on minimizing distances between points and cluster centroids. It works well with numerical data but can be affected by outliers. Grid density determines dense grids based on neighbor densities and can handle different shaped and multi-density clusters without knowing the number of clusters beforehand. It has advantages over k-means in that it can handle categorical data, noise and arbitrary shaped clusters.
Hybrid Approach for Intrusion Detection Model Using Combination of K-Means Cl...theijes
Any violation of information security policy with malicious intent is regarded as an intrusion. The fast evolving new kind of intrusions poses a very serious threat to system security, although there has been the rapid development of several security tools to counter the growing threats, intrusive activities are still growing. Many Intrusion Detection models have been implemented since the concept of Intrusion Detection emerged, but the majority of the existing Intrusion detection models have many drawbacks which include but not limited to low accuracy in detection, high false alarm rates, adaptability weakness, inability to detect new intrusions etc. The main aim of this study is proposing a model that combined simple K-Means clustering Algorithms and Random Forest classification technique that will have minimum false alarms rate and high accuracy detection rate. The experiment was carried out in WEKA 3.8 using the NSL-KDD dataset to process the dataset and obtained the results. At the end of training and testing of the proposed study, the results indicated that the proposed approach achieved improved accuracy and reduced false alarm rates by 99.98% and 0.14% respectively.
Classifiers are algorithms that map input data to categories in order to build models for predicting unknown data. There are several types of classifiers that can be used including logistic regression, decision trees, random forests, support vector machines, Naive Bayes, and neural networks. Each uses different techniques such as splitting data, averaging predictions, or maximizing margins to classify data. The best classifier depends on the problem and achieving high accuracy, sensitivity, and specificity.
Improving Classifier Accuracy using Unlabeled Data..docbutest
This document describes a method for improving classifier accuracy using unlabeled data in addition to a small set of labeled data. The algorithm builds an initial classifier using just the labeled data, then uses that classifier to label a larger set of unlabeled data. A new classifier is then built using both the original labeled data and the now labeled unlabeled data. Experimental results using three common learning algorithms (neural networks, Naive Bayes, C4.5) on 10 datasets show average accuracy improvements of 5%, 3%, and 8% respectively when incorporating unlabeled data. The results indicate that leveraging unlabeled data can significantly boost classifier performance when labeled data is limited.
This document compares four clustering algorithms (K-means, hierarchical, EM, and density-based) using the WEKA tool. It applies the algorithms to a dataset of software classes and evaluates them based on number of clusters, time to build models, squared errors, and log likelihood. The results show that K-means performs best in terms of time to build models, while density-based clustering performs best in terms of log likelihood. Overall, the document concludes that K-means is the best algorithm for this dataset because it balances low runtime and good clustering accuracy.
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation ...IJECEIAES
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
Machine Learning Algorithms for Image Classification of Hand Digits and Face ...IRJET Journal
This document discusses machine learning algorithms for image classification using five different classification schemes. It summarizes the mathematical models behind each classification algorithm, including Nearest Class Centroid classifier, Nearest Sub-Class Centroid classifier, k-Nearest Neighbor classifier, Perceptron trained using Backpropagation, and Perceptron trained using Mean Squared Error. It also describes two datasets used in the experiments - the MNIST dataset of handwritten digits and the ORL face recognition dataset. The performance of the five classification schemes are compared on these datasets.
Intrusion Detection System for Classification of Attacks with Cross Validationinventionjournals
Now days, due to rapidly uses of internet, the patterns of network attacks are increasing. There are various organizations and institutes are using internet and access or share the sensitive information in network. To protect information from unauthorized or intruders is one of the important issues. In this paper, we have used decision tree techniques like C4.5 and CART as classifier for classification of attacks. We have proposed an ensemble model that is combination of C4.5 and Classification and Regression Tree (CART) as robust classifier for classification of attacks. We have used NSL-KDD data set with binary and multiclass problem with 10-fold cross validation. The proposed ensemble model gives satisfactory accuracy as 99.67% and 99.53% in case of binary class and multiclass NSL-KDD data set respectively.
This document compares the k-means and grid density clustering algorithms. K-means partitions data into k clusters based on minimizing distances between points and cluster centroids. It works well with numerical data but can be affected by outliers. Grid density determines dense grids based on neighbor densities and can handle different shaped and multi-density clusters without knowing the number of clusters beforehand. It has advantages over k-means in that it can handle categorical data, noise and arbitrary shaped clusters.
This document proposes an approach for intrusion detection that uses clustering and classification. In the first phase, the K-means clustering algorithm is used to generate clusters of similar attack types from network/system activity data streams. The centroids generated by K-means are then used in the second classification phase, where the K-nearest neighbors and decision tree algorithms detect the different types of attacks present in the data. The overall goal is to take advantage of clustering to group similar attacks first before classifying them, which can provide better detection performance than using a single classifier alone.
Performance Evaluation of Classifiers used for Identification of Encryption A...IDES Editor
Evaluating classifier performance is a critical
problem in pattern recognition and machine learning. In this
paper pattern recognition techniques were applied to identify
encryption algorithms. Four different block cipher algorithms
were considered, DES, IDEA, AES, and RC2 operating in
(Electronic Codebook) ECB mode. Eight different classification
techniques were used for this purpose, these are: Naïve
Bayesian (NB), Support Vector Machine (SVM), neural
network (MLP), Instance based learning (IBL), Bagging (Ba),
AdaBoostM1 (MdaBM1), Rotation Forest (RoFo), and Decision
Tree (C4. 5). The result shows that using pattern recognition
is a useful technique to identify the encryption algorithm,
and according to our simulation using one encryption of key
provide better classification than using different keys.
Furthermore, increase the number of the input files will
improve the accuracy.
K-Means clustering uses an iterative procedure which is very much sensitive and dependent upon the initial centroids. The initial centroids in the k-means clustering are chosen randomly, and hence the clustering also changes with respect to the initial centroids. This paper tries to overcome this problem of random selection of centroids and hence change of clusters with a premeditated selection of initial centroids. We have used the iris, abalone and wine data sets to demonstrate that the proposed method of finding the initial centroids and using the centroids in k-means algorithm improves the clustering performance. The clustering also remains the same in every run as the initial centroids are not randomly selected but through premeditated method.
Efficient Intrusion Detection using Weighted K-means Clustering and Naïve Bay...yousef emami
Intrusion detection system (IDS) is becoming a vital component to secure the network. A successful intrusion detection system requires high accuracy and detection rate. In this paper a hybrid approach for intrusion detection system based on data mining techniques is proposed. The principal ingredients of the approach are weighted k-means clustering and naive bayes classification. The C5.0 algorithm is used for ranking attributes, so the attributes receive a weight which is used in K-means clustering therefore accuracy of clustering is increased.
This document discusses using unsupervised support vector analysis to increase the efficiency of simulation-based functional verification. It describes applying an unsupervised machine learning technique called support vector analysis to filter redundant tests from a set of verification tests. By clustering similar tests into regions of a similarity metric space, it aims to select the most important tests to verify a design while removing redundant tests, improving verification efficiency. The approach trains an unsupervised support vector model on an initial set of simulated tests and uses it to filter future tests by comparing them to support vectors that define regions in the similarity space.
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
An Intrusion Detection System (IDS) is a defense measure that supervises activities of the computer network and reports the malicious activities to the network administrator. Intruders do many attempts to gain access to the network and try to harm the organization’s data. Thus the security is the most important aspect for any type of organization. Due to these reasons, intrusion detection has been an important research issue. An IDS can be broadly classified as Signature based IDS and Anomaly based IDS. In our proposed work, the decision tree
algorithm is developed based on C4.5 decision tree approach. Feature selection and split value are important issues for constructing a decision tree. In this paper, the algorithm is designed to address these two issues. The most relevant features are selected using information gain and the split value is selected in such a way that makes the classifier unbiased towards most frequent values. Experimentation is performed on NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) dataset based on number of features. The time taken by the classifier to construct the model and the accuracy achieved is analyzed. It is concluded that the proposed Decision Tree Split (DTS) algorithm can be used for signature based intrusion detection.
This document compares the performance of various machine learning and classification algorithms, including neural networks, support vector machines, Naive Bayes, decision trees, and decision stumps. It analyzes these algorithms using a dataset of annual and monthly temperature data from India over 1901-2012. The analysis is conducted in RapidMiner and finds that neural networks and support vector machines can effectively model complex nonlinear relationships to predict temperature. Neural networks achieved reasonably accurate predictions of annual temperature compared to the original data values. The document concludes by comparing the performance of the different algorithms.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
Feature Subset Selection for High Dimensional Data using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes a FAST algorithm that has three steps: (1) removing irrelevant features, (2) dividing features into clusters, (3) selecting the most representative feature from each cluster. The FAST algorithm uses DBSCAN, a density-based clustering algorithm, to cluster the features. DBSCAN can identify clusters of arbitrary shape and detect noise, making it suitable for high dimensional data. The goal of feature subset selection is to find a small number of discriminative features that best represent the data.
This document describes a new distance-based clustering algorithm (DBCA) that aims to improve upon K-means clustering. DBCA selects initial cluster centroids based on the total distance of each data point to all other points, rather than random selection. It calculates distances between all points, identifies points with maximum total distances, and sets initial centroids as the averages of groups of these maximally distant points. The algorithm is compared to K-means, hierarchical clustering, and hierarchical partitioning clustering on synthetic and real data. Experimental results show DBCA produces better quality clusters than these other algorithms.
Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
Novel algorithms for Knowledge discovery from neural networks in Classificat...Dr.(Mrs).Gethsiyal Augasta
The document describes a new discretization algorithm called DRDS (Discretization based on Range Coefficient of Dispersion and Skewness) for neural networks classifiers. DRDS is a supervised, incremental and bottom-up discretization method that automates the discretization process by introducing the number of intervals and stopping criterion. It has two phases: Phase I generates an Initial Discretization Scheme (IDS) by searching globally, and Phase II refines the intervals by merging them up to a stopping criterion without affecting quality. The algorithm uses range coefficient of dispersion and data skewness to select the best interval length and number of intervals for discretization. Experimental results show DRDS effectively discretizes data for neural network classification.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
This document outlines a 2-hour professional development training for K-12 teachers to learn about hardware, networks, and their impact on classroom instruction. The training will include a lecture on basic computer parts and operating systems, networks and their types, and advantages of school networks. Teachers will then discuss these topics in groups and complete an assessment test. Special needs of participants will be addressed to help all teachers understand.
IEEE PROJECTS ABSTRACT-A zigbee based wireless sensor network for monitoring ...ASHOKKUMAR RAMAR
This project describes the development of a smart wireless sensor network for monitoring agricultural environments. Sensors are deployed to detect water levels, humidity, temperature, and soil pH. Data from the sensors is sent wirelessly via Zigbee nodes to a central server for analysis. This allows irrigation to be targeted to specific areas based on sensor readings, conserving water and minimizing problems like waterlogging or over-fertilization. Monitoring various environmental factors can provide valuable information to farmers for optimizing crop growth.
OpenPGP is a standard for encrypting and digitally signing emails. It allows users to encrypt emails to ensure only the intended recipient can read the message. Digital signatures authenticate who sent the email and confirm the message contents were not altered. OpenPGP provides security and privacy for email communication.
The document provides information about computer networks and routing & switching certification (CCNA). It discusses TCIL-IT, a company that provides computer networking education and training. It then covers topics such as network design, types of networks, network topologies, networking devices, cables, IP addresses, and basic router configuration commands. The document is intended to provide an overview of concepts relevant to the CCNA certification program for computer networking.
This document proposes an approach for intrusion detection that uses clustering and classification. In the first phase, the K-means clustering algorithm is used to generate clusters of similar attack types from network/system activity data streams. The centroids generated by K-means are then used in the second classification phase, where the K-nearest neighbors and decision tree algorithms detect the different types of attacks present in the data. The overall goal is to take advantage of clustering to group similar attacks first before classifying them, which can provide better detection performance than using a single classifier alone.
Performance Evaluation of Classifiers used for Identification of Encryption A...IDES Editor
Evaluating classifier performance is a critical
problem in pattern recognition and machine learning. In this
paper pattern recognition techniques were applied to identify
encryption algorithms. Four different block cipher algorithms
were considered, DES, IDEA, AES, and RC2 operating in
(Electronic Codebook) ECB mode. Eight different classification
techniques were used for this purpose, these are: Naïve
Bayesian (NB), Support Vector Machine (SVM), neural
network (MLP), Instance based learning (IBL), Bagging (Ba),
AdaBoostM1 (MdaBM1), Rotation Forest (RoFo), and Decision
Tree (C4. 5). The result shows that using pattern recognition
is a useful technique to identify the encryption algorithm,
and according to our simulation using one encryption of key
provide better classification than using different keys.
Furthermore, increase the number of the input files will
improve the accuracy.
K-Means clustering uses an iterative procedure which is very much sensitive and dependent upon the initial centroids. The initial centroids in the k-means clustering are chosen randomly, and hence the clustering also changes with respect to the initial centroids. This paper tries to overcome this problem of random selection of centroids and hence change of clusters with a premeditated selection of initial centroids. We have used the iris, abalone and wine data sets to demonstrate that the proposed method of finding the initial centroids and using the centroids in k-means algorithm improves the clustering performance. The clustering also remains the same in every run as the initial centroids are not randomly selected but through premeditated method.
Efficient Intrusion Detection using Weighted K-means Clustering and Naïve Bay...yousef emami
Intrusion detection system (IDS) is becoming a vital component to secure the network. A successful intrusion detection system requires high accuracy and detection rate. In this paper a hybrid approach for intrusion detection system based on data mining techniques is proposed. The principal ingredients of the approach are weighted k-means clustering and naive bayes classification. The C5.0 algorithm is used for ranking attributes, so the attributes receive a weight which is used in K-means clustering therefore accuracy of clustering is increased.
This document discusses using unsupervised support vector analysis to increase the efficiency of simulation-based functional verification. It describes applying an unsupervised machine learning technique called support vector analysis to filter redundant tests from a set of verification tests. By clustering similar tests into regions of a similarity metric space, it aims to select the most important tests to verify a design while removing redundant tests, improving verification efficiency. The approach trains an unsupervised support vector model on an initial set of simulated tests and uses it to filter future tests by comparing them to support vectors that define regions in the similarity space.
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
An Intrusion Detection System (IDS) is a defense measure that supervises activities of the computer network and reports the malicious activities to the network administrator. Intruders do many attempts to gain access to the network and try to harm the organization’s data. Thus the security is the most important aspect for any type of organization. Due to these reasons, intrusion detection has been an important research issue. An IDS can be broadly classified as Signature based IDS and Anomaly based IDS. In our proposed work, the decision tree
algorithm is developed based on C4.5 decision tree approach. Feature selection and split value are important issues for constructing a decision tree. In this paper, the algorithm is designed to address these two issues. The most relevant features are selected using information gain and the split value is selected in such a way that makes the classifier unbiased towards most frequent values. Experimentation is performed on NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) dataset based on number of features. The time taken by the classifier to construct the model and the accuracy achieved is analyzed. It is concluded that the proposed Decision Tree Split (DTS) algorithm can be used for signature based intrusion detection.
This document compares the performance of various machine learning and classification algorithms, including neural networks, support vector machines, Naive Bayes, decision trees, and decision stumps. It analyzes these algorithms using a dataset of annual and monthly temperature data from India over 1901-2012. The analysis is conducted in RapidMiner and finds that neural networks and support vector machines can effectively model complex nonlinear relationships to predict temperature. Neural networks achieved reasonably accurate predictions of annual temperature compared to the original data values. The document concludes by comparing the performance of the different algorithms.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
Feature Subset Selection for High Dimensional Data using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes a FAST algorithm that has three steps: (1) removing irrelevant features, (2) dividing features into clusters, (3) selecting the most representative feature from each cluster. The FAST algorithm uses DBSCAN, a density-based clustering algorithm, to cluster the features. DBSCAN can identify clusters of arbitrary shape and detect noise, making it suitable for high dimensional data. The goal of feature subset selection is to find a small number of discriminative features that best represent the data.
This document describes a new distance-based clustering algorithm (DBCA) that aims to improve upon K-means clustering. DBCA selects initial cluster centroids based on the total distance of each data point to all other points, rather than random selection. It calculates distances between all points, identifies points with maximum total distances, and sets initial centroids as the averages of groups of these maximally distant points. The algorithm is compared to K-means, hierarchical clustering, and hierarchical partitioning clustering on synthetic and real data. Experimental results show DBCA produces better quality clusters than these other algorithms.
Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
Novel algorithms for Knowledge discovery from neural networks in Classificat...Dr.(Mrs).Gethsiyal Augasta
The document describes a new discretization algorithm called DRDS (Discretization based on Range Coefficient of Dispersion and Skewness) for neural networks classifiers. DRDS is a supervised, incremental and bottom-up discretization method that automates the discretization process by introducing the number of intervals and stopping criterion. It has two phases: Phase I generates an Initial Discretization Scheme (IDS) by searching globally, and Phase II refines the intervals by merging them up to a stopping criterion without affecting quality. The algorithm uses range coefficient of dispersion and data skewness to select the best interval length and number of intervals for discretization. Experimental results show DRDS effectively discretizes data for neural network classification.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
This document outlines a 2-hour professional development training for K-12 teachers to learn about hardware, networks, and their impact on classroom instruction. The training will include a lecture on basic computer parts and operating systems, networks and their types, and advantages of school networks. Teachers will then discuss these topics in groups and complete an assessment test. Special needs of participants will be addressed to help all teachers understand.
IEEE PROJECTS ABSTRACT-A zigbee based wireless sensor network for monitoring ...ASHOKKUMAR RAMAR
This project describes the development of a smart wireless sensor network for monitoring agricultural environments. Sensors are deployed to detect water levels, humidity, temperature, and soil pH. Data from the sensors is sent wirelessly via Zigbee nodes to a central server for analysis. This allows irrigation to be targeted to specific areas based on sensor readings, conserving water and minimizing problems like waterlogging or over-fertilization. Monitoring various environmental factors can provide valuable information to farmers for optimizing crop growth.
OpenPGP is a standard for encrypting and digitally signing emails. It allows users to encrypt emails to ensure only the intended recipient can read the message. Digital signatures authenticate who sent the email and confirm the message contents were not altered. OpenPGP provides security and privacy for email communication.
The document provides information about computer networks and routing & switching certification (CCNA). It discusses TCIL-IT, a company that provides computer networking education and training. It then covers topics such as network design, types of networks, network topologies, networking devices, cables, IP addresses, and basic router configuration commands. The document is intended to provide an overview of concepts relevant to the CCNA certification program for computer networking.
1) The document provides instructions for setting up networks using Packet Tracer, including how to connect devices, configure IP addresses, and set up routing.
2) It gives step-by-step explanations for creating different network scenarios with one, two, and three networks connected by switches and routers.
3) For networks with multiple routers, it emphasizes that a routing protocol like RIP must be configured so the routers know how to direct traffic between networks.
This document provides an overview of Cisco Packet Tracer, a networking simulation software. It describes Packet Tracer as a comprehensive teaching and learning tool that offers realistic network simulation, visualization, assessment authoring and multi-user collaboration capabilities. The document outlines key features of Packet Tracer including its simulation environment, activity wizard for creating assessments, and multi-user functionality. It also summarizes new features introduced in version 5.3 and the benefits of Packet Tracer for both instructors and students in teaching and learning networking concepts.
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Network Design on cisco packet tracer 6.0Saurav Pandey
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Lab practice 1 configuring basic routing and switching (with answer) Arz Sy
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The document provides an overview of computer networking fundamentals including:
- The seven layers of the OSI reference model and their functions from physical transmission to application interfaces.
- Reasons for using a layered networking model including modularity, interoperability, and error checking.
- Key networking concepts such as MAC addresses, connection-oriented vs. connectionless transmission, and data encapsulation.
This document provides an overview of how to use the Packet Tracer network simulation software. It describes the Packet Tracer interface and how to build and simulate basic networks. Key aspects covered include the logical and physical workspaces, realtime and simulation modes, and how to add devices, configure IP addresses, send packets between devices, and observe packet flows and processing at each OSI layer. The document demonstrates these concepts through a sample network simulation example.
Step by Step guide to set up a simple network in Packet TracerSorath Asnani
This document shows the detailed Steps to set up a simple network inside Packet Tracer. You will get familiarity with the software after following the Steps.
CCNA 1 Routing and Switching v5.0 Chapter 1Nil Menon
This document summarizes key points from Chapter 1 of a Cisco networking textbook. It introduces networking concepts like LANs, WANs and the Internet. It discusses how networks are used in daily life for communication, work and entertainment. It also outlines trends that will impact networks, such as BYOD, online collaboration, video and cloud computing. The chapter objectives are to explain network topologies, devices and characteristics used in small to medium businesses.
AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...IJNSA Journal
With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard NSL-KDD intrusion dataset using some neural network based techniques for predicting possible intrusions. Four most effective classification methods, namely, Radial Basis Function Network, SelfOrganizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been applied. In order to enhance the performance of the classifiers, three entropy based feature selection methods have been applied as preprocessing of data. Performances of different combinations of classifiers and attribute reduction methods have also been compared.
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
A Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
Abstract—Classical machine learning techniques have been employed severally in intrusion detection. But due to the rising cases and sophistication of attacks, more advanced machine learning techniques including ensemble-based methods, neural networks and deep learning techniques have been applied. However, there is still need for improved machine learning approach to detect attacks more effectively and efficiently. Stacked generalization approach has been shown to be capable of learning from features and meta-features but has been limited by the deficiencies of base classifiers and lack of optimization in the choice of meta-feature combination. This paper therefore proposes a stacked generalization ensemble approach based on two-tier meta-learner, in which the outputs of classical stacked ensemble are passed to multi-feature-based stacked ensemble, which is optimized. A Grid-search approach is used for the optimization. Nine data features and four meta-features derived from Logistic Regression, Support Vector Machine, Naïve Bayes, and Multilayer Perceptron neural network are used for the machine learning classification task. By applying neural networks as the meta-learner for the classification of NSL-KDD data, improved performances in terms of accuracy, precision, recall and F-measure of 0.97, 0.98, 0.98 and 0.98, respectively are achieved.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
The Use of K-NN and Bees Algorithm for Big Data Intrusion Detection SystemIOSRjournaljce
Big data problem in intrusion detection system is mainly due to the large volume of the data. The dimension of the original data is 41. Some of the feature of original data are unnecessary. In this process, the volume of data has expanded into hundreds and thousands of gigabytes(GB) of information. The dimension span of data and volume can be reduced and the system is enhanced by using K-NN and BA. The reduction ratio of KDD datasets and processing speed is very slow so the data has been reduced for extracting features by Bees Algorithm (AB) and use K-nearest neighbors as classification (KNN). So, the KDD99 datasets applied in the experiments with significant features. The results have gave higher detection and accuracy rate as well as reduced false positive rate. Keywords: Big Data; Intru
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An approach for ids by combining svm and ant colony algorithmeSAT Journals
This summarizes a research paper that proposes a new approach called CSVAC (Combined Support Vector with Ant Colony) for intrusion detection. The approach combines two algorithms, Support Vector Machine (SVM) and Ant Colony Optimization (ACO), to classify network data as normal or abnormal. SVM is used to generate a separating hyperplane and find support vectors, while ACO performs clustering around the support vectors. The clusters are added to the SVM training set and it is retrained in an iterative process until the detection rate exceeds a threshold. The paper evaluates this approach on the standard KDD99 dataset and finds it achieves superior results to other algorithms in terms of accuracy and efficiency.
Intrusion Detection System Based on K-Star Classifier and Feature Set ReductionIOSR Journals
Abstract: Network security and Intrusion Detection Systems (IDS’s) is an important security related research
area. This paper applies K-star algorithm with filtering analysis in order to build a network intrusion detection
system. For our experimental analysis and as a case study, we have used the new NSL-KDD dataset, which is a
modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 66.0% for the
training set and the remainder for the testing set a 2 class classifications has been implemented. WEKA which is
a java based open source software consists of a collection of machine learning algorithms for Data mining tasks
has been used in the testing process. The experimental results show that the proposed approach is very accurate
with low false positive rate and high true positive rate and it takes less learning time in comparison with other
existing approaches used for efficient network intrusion detection.
Keywords: Information Gain, Intrusion Detection System, Instance-based classifier, K-Star, Weka.
Intrusion detection with Parameterized Methods for Wireless Sensor Networksrahulmonikasharma
Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two Adaboost based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
This document discusses a novel method for intrusion awareness using Distributed Situational Awareness (D-SA). It proposes using D-SA and support vector machines (SVM) for network intrusion detection and classification. The method is evaluated using the KDD Cup 1999 intrusion detection dataset. Experimental results show the proposed D-SA method achieves higher detection rates compared to rule-based classification techniques.
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
This document presents a study evaluating the performance of machine learning algorithms for network intrusion detection systems (NIDS) using benchmark datasets. Specifically, it applies an AdaBoost-based machine learning algorithm to NIDS and tests its detection accuracy on the KDD Cup 99 and NSL-KDD intrusion detection datasets. The experimental results show that the AdaBoost-based NIDS performs better on the NSL-KDD dataset compared to the KDD Cup 99 dataset, achieving a higher detection rate and lower false alarm rate.
CLASSIFIER SELECTION MODELS FOR INTRUSION DETECTION SYSTEM (IDS)ieijjournal1
This document discusses different classifier selection models for intrusion detection systems. It begins by introducing intrusion detection systems and their importance for network security. It then describes reducing the features of the KDD Cup 99 dataset to improve computational efficiency. Fifteen different classifier algorithms are described, including K-Means, Naive Bayes, Decision Trees, Support Vector Machines, and ensemble methods. Two models are proposed for combining classifier results. Simulation results on the KDD Cup 99 dataset show the true positive rates, false positive rates, correctly classified instances, and training times for each attack category and classifier. The best performing classifiers are identified for different intrusion types.
This document compares the k-means data mining and outlier detection approaches for network-based intrusion detection. It analyzes four datasets capturing network traffic using both approaches. The k-means approach clusters traffic into normal and abnormal flows, while outlier detection calculates an outlier score for each flow. The document finds that k-means was more accurate and precise, with a better classification rate than outlier detection. It requires less computer resources than outlier detection. This comparison of the approaches can help network administrators choose the best intrusion detection method.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
Intrusion Detection and Forensics based on decision tree and Association rule...IJMER
This paper present an approach based on the combination of, two techniques using
decision tree and Association rule mining for Probe attack detection. This approach proves to be
better than the traditional approach of generating rules for fuzzy expert system by clustering methods.
Association rule mining for selecting the best attributes together and decision tree for identifying the
best parameters together to create the rules for fuzzy expert system. After that rules for fuzzy expert
system are generated using association rule mining and decision trees. Decision trees is generated for
dataset and to find the basic parameters for creating the membership functions of fuzzy inference
system. Membership functions are generated for the probe attack. Based on these rules we have
created the fuzzy inference system that is used as an input to neuro-fuzzy system. Fuzzy inference
system is loaded to neuro-fuzzy toolbox as an input and the final ANFIS structure is generated for
outcome of neuro-fuzzy approach. The experiments and evaluations of the proposed method were
done with NSL-KDD intrusion detection dataset. As the experimental results, the proposed approach
based on the combination of, two techniques using decision tree and Association rule mining
efficiently detected probe attacks. Experimental results shows better results for detecting intrusions as
compared to others existing methods
Intrusion Detection Model using Self Organizing Maps.Tushar Shinde
Proposed Model:
[I] Pre-processing of server logs:
Our web-site server log file analyser performs the following steps when provided with a log file:
1) It scans the entries in the log files to help identify unique visitor’s sessions.
2) For each identified sessions, the analyser has to examine its key matching features to generate the session’s dimensional feature-vector representation.
[II] Session identification:
In this process of dividing a web-site server access log enters into sessions. Session identification is performed by:
1) Grouping all HTTP requests on web-sites that originate from the same IP address that matches the visitor and also are described by the same user-agent strings.
2) By applying a timeout approach to divide into unique sessions to avoid any mishaps.
[III] Dataset labelling:
labels each feature-vector as belonging to one of the following four categories:
1. Human visitor’s normal Known.
2. well-behaved web-site attackers.
3. malicious attackers.
4. unknown visitors unidentified.
Thus, allow a better understanding of the cluster’s nature and significance results can be generated.
Techniques:
SOM Algorithm, NNtool, MATLAB, WEKA toolkit, KDD Data-set.
Adapting New Data In Intrusion Detection SystemsCSCJournals
Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.
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This document describes the design and implementation of a carrier-based sinusoidal pulse width modulation (SPWM) bipolar inverter. It begins with an introduction to inverters that convert DC power to AC power. It then discusses SPWM techniques in detail, including bipolar and unipolar switching methods. The document presents simulation results for a single-phase inverter using SPWM strategies. It aims to simulate and analyze the output waveforms of a SPWM inverter model in MATLAB, and examine how the modulation index affects the simulated and implemented designs.
This document analyzes the polarization and transmission effects of antireflection coatings for silicon-on-insulator (SOI) material systems using simulation software. Without a coating, transmission of transverse magnetic (TM) polarized light is slightly higher than transverse electric (TE) polarized light. A single-layer antireflection coating is designed and optimized to increase average transmission by 19%, reducing the polarization effect. However, multilayer coatings did not further increase transmission over the optimized single layer. In conclusion, antireflection coatings can effectively reduce polarization dependence for SOI materials while improving overall light transmission.
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
The document discusses technical issues related to radio links, security, and quality of service (QoS) in mobile ad hoc networks (MANETs). MANETs are self-organized, decentralized networks formed by wireless mobile nodes without a fixed infrastructure. Key challenges in MANETs include limited wireless transmission range, dynamic changes in network topology, interference, and energy constraints of battery-powered devices. The document outlines various characteristics of MANETs, including that they are self-configuring, infrastructureless networks that enable communication in situations where fixed networks are unavailable or inadequate.
This document summarizes a study that investigated applying a stainless steel spray coating called SS-II to an LM13 aluminum alloy used in engine applications. The study aimed to reduce surface roughness and friction of reciprocating engine parts. Key findings include:
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Layering Based Network Intrusion Detection System to Enhance Network Attacks Detection
1. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
Layering Based Network Intrusion Detection
System to Enhance Network Attacks Detection
Yi, Mon Aye1
, Phyu, Thandar2
1
University of Computer Studies, Mandalay, Myanmar
2
University of Computer Studies, Yangon, Myanmar
Abstract: Due to continuous growth of the Internet technology, it needs to establish security mechanism. Intrusion Detection System
(IDS) is increasingly becoming a crucial component for computer and network security systems. Most of the existing intrusion detection
techniques emphasize on building intrusion detection model based on all features provided. Some of these features are irrelevant or
redundant. This paper is proposed to identify important input features in building IDS that is computationally efficient and effective. In
this paper, we identify important attributes for each attack type by analyzing the detection rate. We input the specific attributes for each
attack types to classify using Naïve Bayes, and Random Forest. We perform our experiments on NSL-KDD intrusion detection dataset,
which consists of selected records of the complete KDD Cup 1999 intrusion detection dataset.
Keywords: security mechanism, Intrusion Detection System, Naïve Bayes, Random Forest.
1. Introduction
The field of computer security has become a very important
issue for computer systems with the rapid growth of
computer network and other transaction systems over the
Internet. An Intrusion Detection System (IDS) is a system
for detecting intrusions that attempting to misuse the data or
computing resources of a computer system. IDS play a vital
role in network security. IDS is security tools that collect
information from a variety of network sources, and analyze
the information for signs of network intrusions.
Depending on the information source considered IDS may
be either host or network based. A host based IDS analyzes
events such as process identifiers and system calls, mainly
related to OS information. On the other hand, a network
based IDS analyzes network related events: traffic volume,
IP addresses, service ports, protocol usage, etc. This paper
focuses on the latter type of IDS.
Depending on the type of analysis carried out intrusion
detection systems are classified as either signature-based or
anomaly-based. Signature-based schemes (also denoted as
misuse-based) seek defined patterns, or signatures, within
the analyzed data. For this purpose, a signature database
corresponding to known attacks is specified a priori. On the
other hand, anomaly-based detectors attempt to estimate the
‘‘normal’’ behavior of the system to be protected, and
generate an anomaly alarm whenever the deviation between
a given observation at an instant and the normal behavior
exceeds a predefined threshold. Another possibility is to
model the ‘‘abnormal’’ behavior of the system and to raise
an alarm when the difference between the observed behavior
and the expected one falls below a given limit.
Signature-based schemes provide very good detection results
for specified, well-known attacks. However, they are not
capable of detecting new, unfamiliar intrusions, even if they
are built as minimum variants of already known attacks. On
the contrary, the main benefit of anomaly-based detection
techniques is their potential to detect previously unseen
intrusion events. However, the rate of false positives in
anomaly-based systems is usually higher than in signature-
based ones [6].
There have been many techniques used for machine learning
applications to tackle the problem of feature selection for
intrusion detection. In [23], author used Backward
Sequential Elimination (BSE) to reduce features set. In [10],
author used gain ration for attribute selection.
We construct system which not only reducing time
consuming for features set and but also improving overall
accuracy. In this paper we use original NSL-KDD [18]
training and the test dataset, which have totally different
distributions due to novel intrusions, introduces in the test
data. The training dataset is made up of 22 different attacks
out of 39 present in the test data. The attacks that have any
occurrences in the training sets should be considered as
known attacks and others those are absent in the training set
and present in the test set, considered as novel attacks.
The rest of this paper is organized as follows. In section 2,
we discuss the related work. In section 3, we describe Naïve
Bayes and Random Forest. Section 4 presents the Feature
Selection. The proposed system is described in section 5.
The experiments are presented in section 6. Finally, we
conclude the paper in section 7.
2. Related Work
Intrusion Detection System (IDS) has increasingly become a
crucial issue for computer and network systems. Recently
machine learning-based IDSs have been subjected to
extensive researches because they can detect both misuse
and anomaly. Multi-layer intrusion detection model was
proposed in [10]. They used gain ratio for selecting the best
features for each layer and classified the system by using
machine learning algorithms such as C5.0, Multi-Layer
Perceptron (MLP) Neural Networks and Naïve Bayes. In
[23] authors proposed a three-layer approach to enhance the
perception of intrusion detection on reduced feature set to
Paper ID: 10091302 104
2. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
detect both known and novel attacks. They used NSL-KDD
dataset for their experiments. They employed domain
knowledge and the Backward Sequential Elimination (BSE)
to identify the important set of features and Naïve Bayes
classifier for classification. In [13], authors used Naïve
Bayesian for classification. Their experiment is implemented
on NSL-KDD dataset. The NIDS based on AdaBoost
algorithm had designed in [24] using Java Technology and
tested it on the NSL-KDD intrusion detection dataset. They
had taken 20% of the training dataset of NSL-KDD as input
to the system. In [19], author applied Rough set theory for
extracting relevant features and Adaboost algorithm for
detecting intrusions. Data mining methods, such as decision
tree (DT) [15] [7], naïve Bayesian classifier (NB)[13], neural
network (NN), Rough Set[5], support vector machine
(SVM)[16], k-nearest neighbors (KNN)[5], fuzzy logic
model, and genetic algorithm have been widely used to
analyze network logs to gain intrusion related knowledge to
improve the performance of IDS in last decades.
3. Theory Background
3.1 Naïve Bayes
Naïve Bayesian classification is called naïve because it
assumes class conditional independence. That is, the effect
of an attribute value on a given class is independent of the
values of the other attributes. This assumption is made to
reduce computational costs, and hence is considered naïve.
The major idea behind naïve Bayesian classification is to try
and classify data by maximizing P(Xj|Ci)|P(Ci) (where i is an
index of the class) using the Bayes theorem of posterior
probability[17]. In general,
We are given a set of unknown data tuples, where each
tuple is represented by an n-dimensional vector, X = (x1,
x2,…,xn) depicting n measurements made on the tuple
from n attributes, respectively, A1,A2,…,An. We are also
given a set of m classes C1, C2… Cm.
Using Bayes theorem, the naïve Bayesian classifier
calculates the posterior probability of each class
conditioned on X, X is assigned the class label of the class
with the maximum posterior probability conditioned on X.
Therefore, we try to maximize P (Xij|X) = P (Xj|Ci) P (Ci)
= P(X). However, since P(X) is constant for all classes,
only P (Xj|Ci) P (Ci) need be maximized. If the class prior
probabilities are not known, then it is commonly assumed
that the classes are equally likely, i,e,
P(C1)=P(C2)=…=P(Cm), and we would therefore maximize
P(Xj|Ci). Otherwise, we maximize P (Xj|Ci) P (Ci). The
class prior probabilities may be estimated by P(Ci)=Si/S,
where Si is the number of training tuples of class Ci, and s
is the total number of training tuples.
In order to reduce computation in evaluating P P(Xj|Ci),
the naïve assumption of class conditional independence is
made. This presumes that the values of the attributes are
conditionally independent of one another, given the class
label of the tuple, i.e., that there are no dependence
relationships among the attributes.
If Ak is a categorical attribute then P(Ak=xkj|Ci) is equal to
the number of training tuples in Ci that have xk as the value
for that attribute, divided by the total number of training
tuples in Ci.
If Ak is a continuous attribute then P(Ak=xkj|Ci) can be
calculated using a Gaussian density function with a mean
µ and standard deviation σ defined by
g(x, μ, σ) = exp -
So that p ( |Ci) = ɡ ( k,,μ ,σ )
We need to compute μ and σ , which are the mean and
standard deviation of values of attribute Ak for training
samples of class Ci.
3.2 Random Forest
The random forests [14] are an ensemble of unpruned
classification or regression trees. In general, random forest
generates many classification trees and a tree classification
algorithm is used to construct a tree with different bootstrap
sample from original data using a tree classification
algorithm. After the forest is formed, a new object that needs
to be classified is put down each of the tree in the forest for
classification. Each tree gives a vote about the class of the
object. The forest chooses the class with the most votes. RF
algorithm is given below [4]:
1.Build bootstrapped sample Bi from the original dataset D,
where |Bi| = |D| and examples are chosen at random with
replacement from D.
2.Construct a tree i, using Bi as the training dataset using
the standard decision tree algorithm with the following
modifications:
3.At each node in the tree, restrict the set of candidate
attributes to a randomly selected subset (x1, x2, x3, … ,
xk), where k = no. of features.
4.Do not prune the tree.
5.Repeat steps (1) and (2) for i = 1, … , no. of trees, creating
a forest of trees τi , derived from different bootstrap
samples.
6.When classifying an example x, aggregate the decisions
(votes) over all trees τi in the forest. If τi (x) is the class of
x as determined by tree τi, then the predicted class of x is
the class that occurs most often in the ensemble, i.e. the
class with the majority votes.
Random Forest has been applied in various domains such as
modeling [11] [20], prediction [6] and intrusion detection
system [12] [22]. Zhang and Zulkernine [12] implemented
RF in their hybrid IDS to detect known intrusion. They used
the outlier detection provided by RF to detect unknown
intrusion. Its ability to produce low classification error and
to provide feature ranking has attracted Lee et al. [22] to use
the technique to develop lightweight IDS, which focused on
single attack.
4. Feature Selection
The 41 features for network connection records fall into
three categories [23].
• Intrinsic features. Intrinsic features describe the basic
information of connections, such as the duration, service,
source and destination host, port, and flag.
Paper ID: 10091302 105
3. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
• Traffic features. These features are based on statistics,
such as number of connections to the same host as the
current connection within a time window.
• Content features. These features are constructed from the
payload of traffic packets instead of packet headers, such
as number of failed logins, whether logged in as root, and
number of accesses to control files.
Feature selection is an effective and an essential step in
successful high dimensionality data mining applications. It is
often an essential data processing step prior to applying a
learning algorithm. Reduction of the irrelevant features leads
to a better understandable model, enhances the accuracy of
detection while speeding up the computation and simplifies
the usage of different visualization technique. Thus reducing
attribute space improves the overall performance of IDS.
5. Proposed System
The proposed system applies data mining techniques to build
patterns for network intrusion detection. We implement the
layering based approach by selecting small set of features for
every layer rather than using all the 41 features. According
to serious level we arranged layer 1 is DoS layer, layer 2 is
R2L layer, layer 3 U2R layer and layer 4 is Probe layer.
5.1 Knowledge Base Attributes Selection
Feature selection is the most critical step in building
intrusion detection models [1], [2], [3]. In order to make IDS
more efficient, reducing the dimensions and data complexity
have been used as simplifying features. Feature selection can
reduce both the data and the computational complexity. It
can also get more efficient and find out the useful feature
subsets [8]. It is the process of choosing a subset of original
features so that the feature space is optimally reduced to
evaluation criterion. We analyze features by using different
machine learning attribute selection algorithm such as
ChiSquare Attribute Selection, Cfs Subset Evaluation,
GainRation Feature Selection to get optimal features. We
compare these features with relevant and meaningful
definition of each attack types. The algorithm that provides
the reduction of features has been proposed in the following
steps:
Step 1: Input NSL-KDD Intrusion Detection Dataset.
Step 2: Eliminate the dispensable attributes.
Step 3: Choose common features from feature selection
algorithms.
Step 4: Select relevant and meaningful features, and merge
possible features by comparing common features of
feature selection algorithm..
Step 5: Compute the detection Rate (DR) of the selected
features by choosing different combination of
features.
Step 6: Analyze and estimate DR of selected features based
on each attack type by using Naïve Bayes and
Random Forest.
Step 7: If (DR) is less than Threshold percentage value, go to
step 3 and continues.
Step 8: If (DR) is greater than Threshold value, these feature
is selected.
Step 9: Assesses the optimal selected features by evaluating
the performance value resulted from Naïve Bayes
and Random Forest Classifiers.
Step 10: Prove that the optimal selected features with the
other classifier such as J48 decision tree, Support
Vector Machine, Random Tree.
We select features for each layer based on the type of attacks
and shows in Table 1. The selected feature set of proposed
model for all layers are:
Table 1: Depicts Proposed Feature Set for each Layer
Layer
Number
Attack Type Feature Number
layer 1 DoS 1,5,23,28,34,39,41
layer 2 R2L 3,11,12,21,22,23,32,33
layer 3 U2R 3,6,10,13,23,24,35,36
layer 4 Probe 1,3,6,12,30,32,35,36
We use NSL-KDD dataset for our experiment. This dataset
has many advantages over original KDD 1999 dataset. To
computer performance of the classification algorithm for
each of these feature sets, we used Weka (3.7) machine
learning tool [9].
6. Experiments
6.1 Networking Attack in Data Set
The simulated attacks were classified, according to the
actions and goals of the attacker. We use the NSL-KDD
[18], which consists of selected records of the complete
KDD data set and has advantages over the original KDD
data set. The dataset was divided into training and test
dataset. Training is used to train the work presented here,
while test dataset is used to test it. Test dataset contains
additional attacks not described in training dataset. The
attacks include the four most common categories of attack.
Each attack type falls into one of the following four main
categories [21].
Denial of service (DoS) attacks; here, the attacker makes
some computing or memory resource which makes the
system too busy to handle legitimate requests. These
attacks may be initiated by flooding a system with
communications, abusing legitimate resources, targeting
implementation bugs, or exploiting the system’s
configuration.
User to root (U2R) attacks; here, the attacker starts with
accessing normal user account and exploits vulnerabilities
to gain unauthorized access to the root. The most common
U2R attacks cause buffer overflows.
Remote to user (R2L) attacks; here, the attacker sends a
packet to a machine, then exploits the machine’s
vulnerabilities to gain local access as a user. This
unauthorized access from a remote machine may include
password guessing.
Probing (PROBE); here, the attacker scans a network to
gather information or find known vulnerabilities through
actions such as port scanning.
In NSL-KDD dataset, these four attack classes are divided
into 22 different attack classes that showed in Table 2.
Paper ID: 10091302 106
4. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 2 Issue 9, September 2013
www.ijsr.net
Table 2: Attack type in Dataset
4 Main Attack
Classes
22 Attack Classes
DoS back, land, neptune, pod, smurt, teardrop
R2L ftp_write, guess_passwd, imap, multihop, phf,
spy, warezclient, warezmaster
U2R buffer_overflow, perl, loadmodule, rootkit
Probing ipsweep, nmap, portsweep, satan
We utilize the NSL-Knowledge Discovery and Data Mining
data set to test the system. We have taken 20% of the
training dataset of NSL-KDD as input to the system. Due to
the redundant records in the KDDCup’99 dataset the
performance of the learning algorithms biased. The results of
the accuracy and performance of learning algorithms on the
KDDCup’99 data set are hence unreliable. NSL-KDD
testing set provide more accurate information about the
capability of the classifiers.
6.2 Performance Measures
The most widely used performance evaluations are Detection
Rate (DR) and False Positive Rate (FPR). Good IDS must
have high DR and a low or zero FPR. The performance of
intrusion detection systems (IDS) are estimated by detection
rates (DR). DR is defined as the number of intrusion
instances detected by the system divided by the total number
of intrusion instances present in the dataset.
DR = * 100 (1)
FPR= * 100 (2)
6.3 Experiment and Analysis on Proposed Features
We perform two sets of experiments. From the first
experiment, we examine the TPR and FPR of Naïve Bayes
and Random Forest with all 41features. In this experiment
for Dos Layer we selected features numbers 1,5,41, for R2L
layer feature numbers 3,11,22,23,32,33 are selected, for U2R
layer feature numbers 3,6,13,23,24,35,36 are selected, and
probe layer feature numbers 3,6,12,30,32,35,36 are selected.
We choose 20% of the training dataset and 10-fold cross
validation for the testing process. In 10-fold cross-validation,
the available data is randomly divided into 10 disjoint
subsets of approximately equal size. One of the subsets is
then used as the test set and the remaining 9 sets are used for
building the classifier. This is done repeatedly 10 times so
that each subset is used as a test subset once. Table 3 shows
performance of Naïve byes and Random Forest classifier
with all features. From the results of this experiment we
observe that Random Forest achieves higher attack TPR on
all attack type. However the time taken to build model is
very higher than Naïve Bayes.
Table 3: Performance of Naïve Bayes and Random Forest
with 41 features
Attack
Classes
Naïve Bayes Random Forest
DR (%) FPR (%) DR (%) FPR (%)
DoS 95 0.02 100 0
R2L 46.4 0.014 100 0
U2R 100 0.06 100 0
Probe 87.7 0.054 100 0
For second experiment we examine the input data with
selected attributes. The results are shown in Table 4. The
performance of Naïve Bayes is obviously higher than first
experiment.
Table 4: Performance of Naïve Bayes and Random Forest
with selected features
Attack
Classes
Naïve Bayes Random Forest
DR (%) FPR (%) DR (%) FPR (%)
DoS 100 0.02 100 0.02
R2L 98 0.00 97 0.00
U2R 100 0.00 100 0
Probe 97 0.00 100 0.00
7. Conclusion and Future Work
In this paper we implement the system by analyzing the
features of intrusion detection system. We presented an
optimal intrusion detection system based on Naïve Bayes
and Random Forest classifiers with feature selection. The
system performed comparative analysis of two classifiers
without features selection and with features selection. The
main purpose of the system is to improve the performance of
the system and classifiers using feature selection. The future
works focus on applying the domain knowledge of security
to improve the detection rates for current attacks in real time
computer network, and ensemble with other mining
algorithm for improving the detection rates in intrusion
detection.
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5. International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
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Author Profile
Aye Mon Yi received the B.C.Sc. (Hons:) degree from
Computer University (Myeik) in 2004 and M.C.Sc.
degree from University of Computer Studies, Yangon
(UCSY) in 2006. Now she is a PhD candidate at
University of Computer Studies, Mandalay. Her
interested fields are Data Mining, Machine Learning and Network
Security.
Paper ID: 10091302 108