Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an Intrusion Detection System is very important. Hence there is high demand to reduce the threat level in networks to ensure the data and services offered by them to be more secure. In this paper we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection system using the layered CRFs. We set up different types of checks at multiple levels in each layer. Our framework examines various attributes at every layer in order to effectively identify any breach of security. Once the attack is detected, it is intimated through mobile phone to the system administrator for safeguarding the server system. We established experimentally that the layered CRFs can thus be more effective in detecting intrusions when compared with the other previously known techniques.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
A Survey: Comparative Analysis of Classifier Algorithms for DOS Attack Detectionijsrd.com
In today's interconnected world, one of pervasive issue is how to protect system from intrusion based security attacks. It is an important issue to detect the intrusion attacks for the security of network communication.Denial of Service (DoS) attacks is evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money.Significance of Intrusion detection system (IDS) in computer network security well proven. Intrusion Detection Systems (IDSs) have become an efficient defense tool against network attacks since they allow network administrator to detect policy violations. Mining approach can play very important role in developing intrusion detection system. Classification is identified as an important technique of data mining. This paper evaluates performance of well known classification algorithms for attack classification. The key ideas are to use data mining techniques efficiently for intrusion attack classification. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an Intrusion Detection System is very important. Hence there is high demand to reduce the threat level in networks to ensure the data and services offered by them to be more secure. In this paper we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection system using the layered CRFs. We set up different types of checks at multiple levels in each layer. Our framework examines various attributes at every layer in order to effectively identify any breach of security. Once the attack is detected, it is intimated through mobile phone to the system administrator for safeguarding the server system. We established experimentally that the layered CRFs can thus be more effective in detecting intrusions when compared with the other previously known techniques.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
A Survey: Comparative Analysis of Classifier Algorithms for DOS Attack Detectionijsrd.com
In today's interconnected world, one of pervasive issue is how to protect system from intrusion based security attacks. It is an important issue to detect the intrusion attacks for the security of network communication.Denial of Service (DoS) attacks is evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money.Significance of Intrusion detection system (IDS) in computer network security well proven. Intrusion Detection Systems (IDSs) have become an efficient defense tool against network attacks since they allow network administrator to detect policy violations. Mining approach can play very important role in developing intrusion detection system. Classification is identified as an important technique of data mining. This paper evaluates performance of well known classification algorithms for attack classification. The key ideas are to use data mining techniques efficiently for intrusion attack classification. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
A Survey on Various Data Mining Technique in Intrusion Detection SystemIOSRjournaljce
The intrusion detection plays an essential role in computer security. Data Mining refers to the process of extracting hidden, previously unknown and useful information from large databases. Thus data mining techniques help to detect patterns in the data set and use these patterns to detect future intrusions. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the performance of the IDS. This paper concerned with the brief review of comparative study on applied data mining based intrusion detection techniques with their merit and demerits. This paper relay more number of applications of the data mining and also focuses extent of the data mining which will useful in the further research.
DETECTING NETWORK ANOMALIES USING CUSUM and FCMEditor IJMTER
The network intrusion detection techniques are important to prevent our systems and
networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls,
user authentication and data encryption have failed to completely protect networks and systems from the
increasing and sophisticated attacks and malwares. Two anomaly detection techniques – CUSUM and
clustering are used to find network anomalies. CUSUM detect changes based on the cumulative effect of
the changes made in the random sequence instead of using a single threshold to check every variable. It
involves calculating cumulative sum and determining whether a packet is normal or not. The FCM
algorithm employs fuzzy partitioning such that a data point can belong to all groups with different
membership grades. Together, CUSUM and FCM become a good technique in detecting network
anomalies with a very less false alarm rate.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
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
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
For a human body to function properly it is essential to have a certain amount of body fat. Fat serves to
manage body temperature, pads and protects the organs. Fat is the fundamental type of the body's vitality
stockpiling. It is important to have a healthy amount of body fat. Overabundance of fat quotient can build
danger of genuine wellbeing issues. Anthropometry is a broadly accessible and basic strategy for the
appraisal of body composition. Anthropometry measures are weight, height, Body Mass Index (BMI),
waist, boundary, biceps, skinfold etc. The human fat percentage is figured by taking anthropometric
variables. We proposed a methodology to determine the body fat percentage using R programming and
regression formula. We analyzed 10 anthropometric variables and 3 demographic variables. Our study
shows that the impact of certain variables has an edge over other in predicting body fat percentage.
Patents play different strategic roles in different industries. Because patent law is independent of industry structure, capital needs, R&D patterns, or the relationship between innovation and commercialization, however, elements that are critical to some industries threaten others. Yet every patent, regardless of quality, is a presumably valid federal license authorizing its bearer to restrict, reduce, restrain and contest all products in its sphere of exclusivity.
Armed with patents, big companies can police their competitors; small companies can secure hefty license fees or investment stakes; and non-practicing entities (NPEs) can file strategic lawsuits. Inattention to this terrain courts disaster. Any company possessing patentable technology must recognize that someone else may patent it first. Any company possessing patents must learn how to extract maximum profitability from intangible assets. And every company must appreciate that success invites litigation and explore preventative and defensive steps.
Learning Objectives:
- Understand the role that patents play in the economy
- Highlight key aspects of the legal terrain
- Revisit the roots of the modern era of patenting
- Recognize how to counsel companies in light of the current patent terrain
- Identify different types of patentees and the strategies they favor
- Recognize key strategic and valuation questions
- Appreciate the importance of a Strategic Patent Counsel
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
A Survey on Various Data Mining Technique in Intrusion Detection SystemIOSRjournaljce
The intrusion detection plays an essential role in computer security. Data Mining refers to the process of extracting hidden, previously unknown and useful information from large databases. Thus data mining techniques help to detect patterns in the data set and use these patterns to detect future intrusions. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the performance of the IDS. This paper concerned with the brief review of comparative study on applied data mining based intrusion detection techniques with their merit and demerits. This paper relay more number of applications of the data mining and also focuses extent of the data mining which will useful in the further research.
DETECTING NETWORK ANOMALIES USING CUSUM and FCMEditor IJMTER
The network intrusion detection techniques are important to prevent our systems and
networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls,
user authentication and data encryption have failed to completely protect networks and systems from the
increasing and sophisticated attacks and malwares. Two anomaly detection techniques – CUSUM and
clustering are used to find network anomalies. CUSUM detect changes based on the cumulative effect of
the changes made in the random sequence instead of using a single threshold to check every variable. It
involves calculating cumulative sum and determining whether a packet is normal or not. The FCM
algorithm employs fuzzy partitioning such that a data point can belong to all groups with different
membership grades. Together, CUSUM and FCM become a good technique in detecting network
anomalies with a very less false alarm rate.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
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
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
For a human body to function properly it is essential to have a certain amount of body fat. Fat serves to
manage body temperature, pads and protects the organs. Fat is the fundamental type of the body's vitality
stockpiling. It is important to have a healthy amount of body fat. Overabundance of fat quotient can build
danger of genuine wellbeing issues. Anthropometry is a broadly accessible and basic strategy for the
appraisal of body composition. Anthropometry measures are weight, height, Body Mass Index (BMI),
waist, boundary, biceps, skinfold etc. The human fat percentage is figured by taking anthropometric
variables. We proposed a methodology to determine the body fat percentage using R programming and
regression formula. We analyzed 10 anthropometric variables and 3 demographic variables. Our study
shows that the impact of certain variables has an edge over other in predicting body fat percentage.
Patents play different strategic roles in different industries. Because patent law is independent of industry structure, capital needs, R&D patterns, or the relationship between innovation and commercialization, however, elements that are critical to some industries threaten others. Yet every patent, regardless of quality, is a presumably valid federal license authorizing its bearer to restrict, reduce, restrain and contest all products in its sphere of exclusivity.
Armed with patents, big companies can police their competitors; small companies can secure hefty license fees or investment stakes; and non-practicing entities (NPEs) can file strategic lawsuits. Inattention to this terrain courts disaster. Any company possessing patentable technology must recognize that someone else may patent it first. Any company possessing patents must learn how to extract maximum profitability from intangible assets. And every company must appreciate that success invites litigation and explore preventative and defensive steps.
Learning Objectives:
- Understand the role that patents play in the economy
- Highlight key aspects of the legal terrain
- Revisit the roots of the modern era of patenting
- Recognize how to counsel companies in light of the current patent terrain
- Identify different types of patentees and the strategies they favor
- Recognize key strategic and valuation questions
- Appreciate the importance of a Strategic Patent Counsel
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
Tijdens de diplomauitreiking gaf Edwin Klijn, projectleider digitalisering bij de KB een presentatie over de Databank Digitale Dagbladen. In het kader van dit project worden in de komende jaren 8 miljoen krantenpagina's gedigitaliseerd
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EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
Articles - International Journal of Network Security & Its Applications (IJNSA)IJNSA Journal
International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal1
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging
endlessly. So it is critical to protect the networks from attackers and the Intrusion detection
technology becomes popular. Therefore, it is necessary that this security concern must be articulate
right from the beginning of the network design and deployment. The intrusion detection technology is the
process of identifying network activity that can lead to a compromise of security policy. Lot of work has
been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a
novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and
manage misuse and anomaly detects
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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. Our observations confirm the conjecture
that both the feature selection and stochastic based genetic operators improves the accuracy and the
effectiveness. The training time is shown to be reduced tremendously by 98.59% and accuracy improved to
98.75%.
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.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Intrusion detection system via fuzzy
1. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.3, May 2015
DOI : 10.5121/ijdkp.2015.5303 29
INTRUSION DETECTION SYSTEM-VIA FUZZY
ARTMAP IN ADDITION WITH ADVANCE SEMI
SUPERVISED FEATURE SELECTION
Swati Sonawale and Roshani Ade
Department of Computer Engineering, Savitribai Phule Pune University, India
ABSTRACT
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
KEYWORDS
Feature Selection, Intrusion Detection, Redundancy, Fuzzy ARTMAP
1. INTRODUCTION
In today’s computing world security is main issue. There are many solutions available to protect
the network structure and communication over the Internet, amongst them we can use firewalls,
encryption, and virtual private networks. Intrusion detection is advance step for these methods.
IDS protect a system from various types of attack, misuse, and compromise. It is also used for
observing activity of networks. Network traffic watching and measurement is gradually
considered as a critical task for understanding, increasing performance & safety of various cyber
organization. Feature base uses plays an important role in reducing development lead periods
improving quality of product [1].
By using dimensionality reduction technique we can achieve efficiency as it will reduce data
Hence, in a number of cases it can be useful or even necessary to first reduce the dimensionality
2. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.3, May 2015
30
of the records to a convenient size, by keeping as it original info as probable, and then give this
reduced dimension data as a input to system [2].
In formativeness coherence that capture relevant properties of the constraint sets coherence
measure is independent of any learning algorithm. Training time for learning fuzzy art map
classifier is less as compare to other classifier [3].
By using this technique we can detect intrusions to find whether there is attack or not in
system as information is valuable asset for any organization it can cause millions of harm for any
organization within a few seconds [4].
1.1 INTRUSION DETECTION
An intrusion detection system (IDS) inspects all inbound and outbound network activity and
identifies suspicious patterns that may indicate a network or system attack from someone
attempting to break into or compromise a system. IDS is a system which is designed for security
purpose. It will protect computers or network of computer. It collects information from numerous
sources to detect security breaches. IDS generates various types of alert. Alert can be From
change in log files to email messages or sms[5].
There are several ways to categorize an IDS:
1.1.1. Misuse detection vs. Anomaly detection
In misuse detection, the IDS analyses the information it gathers and compares it to large
databases of attack signatures. Essentially, the IDS looks for a specific attack that has already
been documented. Like a virus detection system, misuse detection software is only as good as the
database of attack signatures that it uses to compare packets against. In anomaly detection, the
system administrator defines the baseline, or normal, state of the networks traffic load,
breakdown, protocol, and typical packet size. The anomaly detector monitors network segments
to compare their state to the normal baseline and look for anomalies [6].
1.1.2. Network-based vs. Host-based system
In a network-based system, or NIDS, the individual packets flowing through a network are
analysed. The NIDS can detect malicious packets. They work basically on a “eavesdropping
idea,” information is collected from the network traffic stream, as data travels on the network
segment [7].
Drawback of NIDS is that it is unable to scan protocol if data is in other formats means if data is
in encrypted format. There are also problems in case of networks having high bandwidth. It is
hard for implementation.
In a host-based system, the IDS examines at the activity on each individual computer or host
[8].Host-based intrusion detection systems are required for numerous causes because host-based
systems can screen access to information in terms of “who accessed what,” these systems can
trace spiteful or incorrect actions to a particular user ID. It has been observed that due to HIDS
false positive rate has been decreased [9].
3. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.3, May 2015
31
One of the drawback of host base system is portability. This is because it run on a single platform
so it must be compatible with the system on which it is running and it cannot monitor network
traffic because it is not design for this purpose. They uses local resources. HIDS usually includes
an agent fixed on every system, monitoring and informing on local OS and application action
[10].
1.1.3. Passive system vs. Reactive system
In a passive system, the IDS identifies a possible security break, logs the data and signals an
aware. It is designed in such way that it will give only alert. It cannot perform any function on its
own [11].
In a reactive system, the IDS replies to the doubtful activity by logging off a user or by
reprogramming the firewall to block network traffic from the doubted malicious cause [12].
However they both related to network security, an IDS contrasts from a firewall in that a firewall
looks out for intrusions in order to stay them from happening. The firewall bounds the access
amongst networks in order to stop intrusion and does not signal an attack from inside the
network. An IDS estimates a doubted intrusion once it has taken place and signals an alarm. An
IDS also watches for attacks that originate from inside of a system [13].
2. BACKGROUND
2.1 FEATURE SELECTION
Feature selection (FS) methods have commonly been used as a main way to select the relevant
features. Author has proposed a novel unsupervised FS method, i.e., locality and similarity
preserving embedding (LSPE) for feature selection. Specifically, the nearest neighbour graph is
firstly constructed to preserve the locality structure of data points, and then this locality structure
is mapped to the reconstruction coefficients such that the similarity among these data points is
preserved. Moreover, the sparsity derived by the locality is also preserved. Finally, the low
dimensional embedding of the sparse reconstruction is evaluated to best preserve the locality and
similarity [14].
Energy efficiency is a key issue in wireless sensor networks where the energy resources and
battery capability are very inadequate. In this author has introduced a new pattern recognition
based creation for reducing the energy usage in wireless sensor networks. The proposed scheme
encompasses an algorithm to rank and select the sensors from the most important to the least, and
followed by a nave Bayes classification.[15].
An efficient fuzzy classifier is depend on the ability of feature selection based on a fuzzy entropy
technique. It is used to evaluate the information of pattern distribution in pattern space. With this
info, we can divide the pattern space into non overlapping decision regions for pattern
classification. Then the decision sections do not overlap, both the difficulty and computational
burden of the classifier is decreased and thus the training and classification time are very short.
Although the judgment areas are divided into non overlapping subspaces, we can obtain good
classification performance meanwhile the decision areas can be properly identified via proposed
fuzzy entropy method. The feature selection procedure reduces the dimensionality of a problem
as well as rejects noise-corrupted, redundant and insignificant features [16].
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Today it is very vital need of provision of a high level security to guard highly sensitive and
confidential information. In Network Security Intrusion Detection System is an essential
technology. Nowadays scientists have interested on intrusion detection system using Data mining
procedures as an devious skill. IDS is a software or hardware device that deals with attacks by
gathering information from a variation of system and network sources, then analysing
symptoms[17] The main aim of feature selection is to select number of features from the set of
thousands of features. It is applied to reduce the dimensionality & improving learning
performance [18].
Feature selection has been active research area in pattern recognition statistics and data mining
community. The main idea of feature selection is to remove redundant features select features
which are needed. Feature selection can considerably increase the clarity of the resulting
classifier models and repeatedly form a model that simplifies better to unseen points. Further, it is
frequently the event that identifying the particular subset of predictive features is an significant
problem in its own way [19].
In supervised learning Feature selection has been well measured, where the key aim is to discover
a feature subset that forms greater classification accuracy have planned feature selection and
clustering together with a only or joint measure[20].
For feature selection in unsupervised learning, learning algorithms are aimed to find natural
grouping of the instances in the feature space. Thus feature selection in unsupervised learning
aims to find a good subclass of features that is needed to build high value of clusters from a given
number of clusters. Though, the traditional approaches to feature selection with particular
evaluation criterion have displayed limited capability in terms of knowledge detection and
judgment provision. This is as decision makers should take into consideration multiple, conflicted
ideas immediately. In particular no single condition for unsupervised feature selection is best for
each use and simply the decision maker can choose the relative weights of criteria for any
application [21].
Algorithms for feature selection fall into two broad classes namely wrappers that use the learning
algorithm itself to estimate the effectiveness of features and filters that calculate features
according to heuristics based on overall features of the data. In machine learning and statistics,
feature selection, also well-known as variable selection, attribute selection or variable subset
selection, is the method of selecting a subset of related features are used in model creation. The
principal hypothesis while using a feature selection method is that the data contains several
duplicate or unrelated features. Redundant features are those inappropriate features deliver
useless info in any perspective. Various feature selection techniques are consider as a subdivision
of the additional universal field of feature extraction Feature extraction creates new features from
purposes of the unique features, whereas feature selection yields a subclass of the features.
Feature selection techniques are frequently used in areas where there are numerous features and
relatively limited samples [22].
The typical case is the usage of feature selection in examining DNA microarrays, where there are
several thousands of features, and a less number of samples. Feature selection method offers three
key advantages when making analytical models [23].
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Feature selection is used to eliminate irrelevant and redundant features, which improves
prediction accuracy and reduces the computational overhead in classification. This paper presents
comparison of 3 methods namely fast correlation based feature selection (FCBF), Multi thread
based FCBF feature selection and decision dependent -decision independent correlation (DDC-
DIC). These approaches are concerning the relevance of the features and the pair wise features
correlation for redundancy checking in order to improve the prediction accuracy and reduce the
computation time. The experimental results are tested in weka tool for C4.5 decision tree
construction algorithm, which provide better performance for lung cancer, Tic 2000 Insurance
company data and breast cancer data sets [24].
Features are representative characteristics of data sets. Identifying such features in a high
dimensional dataset play an important role in real world applications. Data mining is best used to
determine important features. Selecting important features from a subject of identified features
can help in making expert decisions. However, efficient identification of such feature subset and
selection is a challenging problem. Recently Song et al. proposed a solution that is capable of
selecting subset of features with good quality. They used clustering approach before selecting
representative features for final selection. Similar work is carried out in this paper which
demonstrates the proof of concept. The proposed solution makes use of clustering for achieving
the goal of the system. The empirical results reveal that the application is useful. The results are
compared with many existing algorithms like C4.5, Naïve Bayes, IB1 and RIPPER [25].
2.2 INTRUSION DETECTION SYSTEM
Today it is very vital need of provision of a high level security to guard highly sensitive and
confidential information. In Network Security Intrusion Detection System is an essential
technology. Nowadays scientists have interested on intrusion detection system using Data mining
procedures as an devious skill. IDS is a software or hardware device that deals with attacks by
gathering information from a variation of system and network sources, then analysing symptoms.
An overview of intrusion detection system introduces the reader to some fundamental concepts of
IDS methodology. The primary intrusion detection techniques has been discussed. In this we
focus on data mining algorithms for implementation of IDS such as Support Vector Machine It
contains Kernelized of security problems support vector machine, Extreme Learning Machine
and Kernelized Extreme Learning Machine [26].
The Internet environment has become more complex and untrusted Over the past several years,.
Enterprise networked systems are unsurprisingly showing to the increasing threats posed by
hackers as well as malicious users’ internal to a network. IDS technology is one of the important
tools used now-a-days, to counter such threats. Various IDS techniques has been proposed, which
identifies and alarms for such threats or attacks. Data mining provides a wide range of techniques
to classify these attacks. The author provides a comparative study on the attack detection rate of
these existing classification techniques [27].
Present intrusion detection methods mostly effort on determining irregular system measures in
computer networks and distributed communication schemes. Clustering methods are usually used
to decide a probable attack. Due to the doubt nature of intrusions, fuzzy sets show an significant
part in identifying hazardous actions and decreasing false alarms level. The author suggests a
dynamic method that attempts to determine well-known or unfamiliar intrusion forms. A dynamic
fuzzy boundary is established from labelled data for dissimilar levels of security necessities [28].
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34
We introduces the network intrusion detection system (NIDS), which uses a suite of data
Mining techniques to automatically detect attacks against computer networks and systems. It
consist of two specific contributions:
1. An unsupervised anomaly detection technique that assigns a score to each network connection
that reflects how anomalous the connection is, and
2. An association pattern analysis based module that summarizes those network connections that
are ranked highly anomalous by the anomaly detection module. Experimental results show that
our anomaly detection techniques are successful in automatically detecting several intrusions that
could not be identified using popular signature-based tools .Furthermore, given the very high
volume of connections observed per unit time, association pattern based summarization of novel
attacks is quite useful in enabling a security analyst to understand and characterize emerging
threats[29].
Proposed Intrusion Detection System is an intrusion detection system (IDS) proposed by
analysing the principle of the intrusion detection system based on host and network. Here we
are concentrating and analysing overall performance as well as security of the proposed IDS.
Moreover the proposed IDS approve the effectiveness of the proposed method, and presented
results shows advantages of host based as well as network based security. The proposed model of
hybrid IDSs offers several advantages over alternative systems. First of all it provided higher
security, it supported high availability and scalability, and most important thing it produced good
results in terms of normal and abnormal behaviours of captured packet. The proposed model
includes integration of individual components to produced better results [19].Author has
proposed novel approach for intrusion detection and diagnosis. The proposed approach uses
Sequential Backward Floating Search for feature selection and fuzzy ARTMAP for detection and
diagnosis of attacks. The optimal vigilance parameter for the fuzzy ARTMAP is chosen using a
genetic algorithm. Due to this reduced features computation time is saved by 0.789 s[30].
The reliance on information technology became serious and IT infrastructure, sensitive data,
intangible intellectual property are susceptible to attacks threats. Organizations mount Intrusion
Detection Systems (IDS) to alert suspicious traffic or action. IDS produce a big number of alerts
and most of them are false positive as the activities interpret for partly attack pattern or lack of
background knowledge. Monitoring and identifying risky alerts is a major concern to security
administrator. The present diagnosis stage, respectively work is to design an operational model
for minimization of false positive alarms, including periodic alarms by security administrator.
The construction, design and performance of model in reduction of false positives in IDS are
discovered [31,32].
The Intrusion detection system deals with huge amount of data which contains irrelevant and
redundant features causing slow training and testing process, higher resource consumption as
well as poor detection rate. Feature selection, therefore, is an important issue in intrusion
detection. In this paper we introduce concepts and algorithms of feature selection, survey existing
feature selection algorithms in intrusion detection systems, group and compare different
algorithms in three broad categories: filter, wrapper, and hybrid. We conclude the survey by
identifying trends and challenges of feature selection research and development in intrusion
detection system[33].
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3. ALGORITHIM
3. 1 CONSTRAINT SELCTION ALGORITHIM
Step 1: Select Dataset from System.
D = {KDD99, WAVE,}
Step 2: Create and Initialize Instances.
X =x1, x2,…, xN}
Step 3: Divide instances into Categories i.e. Labeled Instance and
Unlabeled Instances.
XL = {x1, x2,…, xN}
XU = {x1, x2,…, xN}
Step 4: Create and Initialize Constraints.
Ω= {(x1, x2), (x1, x3) … (L*(L-1)/2) }
Step 5: Divide constraints into Categories i.e. Must-Link Constraints and Cannot-Link
Constraint.
Ω’ML = {Must-Link Constraints}
Ω’CL = {Cannot -Link Constraints}
Step 6: Calculate Global coherence of constraint Coh(Ω)
Step 7: for i=1 to Ω
if Coh(Wi) >= Coh(Ω) then
ΩS = ΩS U {Wi}
end if
end for
Step 8: Selected Constraints are
ΩS = Ω’ML U Ω’CL
3.2 FUZZY ARTMAP ALGORITHM
1. Initially, all neuron values should be normalized to guarantee that they are in the range 0-
Encode the vectors of ARTa and ARTb modules.
2. Initialize the weights and parameters of ARTa, ARTb and Inter-ART.
3. Choose the category for ARTa and ARTb. If more than one module is active, take the one that
has the highest ordering index.
4. Test the vigilance of ARTa and ARTb. If the vigilance criterion is met, then the resonance
(Match) takes place.
5. Match tracking between ARTa and ARTb: check if there was matching between the input and
output. If not, search another index that satisfies it.
6. Repeat steps 4 through 7 for every pair of vectors to be trained. Algorithm Of Fuzzy ARTMAP
[20].
3.3 FAST ALGORITHIM
INPUT: D (F1,F2……….Fm,C)
Ɵ=Threshold
OUTPUT:S=Set of selected feature subset
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Step 1:
1. For i=1 to m
2. T-relevance=SU(Fi ,C)
3. If T-relevance > Ɵ then
4. S=S U { Fi }
5. Generate spanning tree using prims algorithm
6. Make partition of tree to select representative features.
Step 2: We get selected feature set.
4. FUZZY ARTMAP NEURAL NETWORKS
ART stands for “Adaptive Resonance Theory” developed by Stephen Grossberg in 1976.ART
signifies family of neural networks. Real world is faced with a situations where data is
continuously changing. In such situation, every learning system faces plasticity-stability
dilemma. This dilemma is about :"A system that must be able to learn to adapt to a changing
environment (i.e. it must be plastic) but the constant change can make the system unstable,
because the system may learn new information only by forgetting everything it has so far
learned[34].
This phenomenon, a contradiction between plasticity and stability, is called plasticity - stability
dilemma. The back-propagation algorithms suffer from such stability problem. The basic ART
system is unsupervised learning model.
It typically consists of
• Comparison field and a recognition field composed of neurons
• Vigilance parameter, and
• Reset module
In comparison field it takes an input vector (a one-dimensional array of values) and transfers it to
its best match in the recognition field. Its best match is the single neuron whose set of weights
most closely matches the input vector.
Neural networks have been used widely for identifying intrusions in computer networks , which
confirms that the paradigm of learning by sampling in training IDSs are becoming more and more
popular these days. There are various methods available for learning classifier such as
incremental learning given in [37,38].
In particular, the fuzzy ARTMAP neural network represents a valuable supervision learning
system that classifies input data into stable categories to respond to random input patterns [35].
Fig. 2 depicts the architecture of the fuzzy ARTMAP neural network. It comprises two modules:
fuzzy ARTa and fuzzy ARTb. Both modules use the same structure of the neural network. These
two modules are interconnected by a third module called inter-ART.
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Figure 1. Architecture of the fuzzy ARTMAP neural network [20].
5. ARCHITECTURE OF SYSTEM
The main component in the architecture diagram Dataset process of feature selection IDS and
final output. Semi supervised feature selection has constraint selection, Feature relevance,
Dimension Reduction Method. Following process occur in case of feature selection process. Then
applying fuzzy ARTMAP algorithm we can detect intrusion has occurred or not.
5.1 KDD99 AS A DATASET
For the purpose of this study we have used kdd99 as a dataset. The main goal is to use this dataset
is that this database is freely available. Since 1999, KDD’99 has been the most wildly used data
set for the estimation of anomaly detection techniques. This dataset is prepared by Stolfo et al.and
is made based on the data captured in DARPA’98 IDS estimation Program.
DARPA’98 is around 4 gigabytes of compacted raw (binary)tcp dump data of 7 weeks of
network traffic, which can be managed into about 5 million connection records, each with
Around 100 bytes. The two weeks of test data have around 2 million link records. KDD training
dataset comprises of nearly 4,900,000 particular connection vectors each of which contains 41
features and is labeled as either normal or an attack, with just one specific attack type. The
simulated attacks comes under one of the following four attacks:
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Figure 2. Architecture of the Proposed system
1) Denial of Service Attack (DoS): is an attack in which the attacker creates some computing or
memory resource too busy or too full to handle valid requests, or denies valid users access to a
machine.
2) User to Root Attack (U2R): is a class of action in which the attacker starts out with entry to a
normal user account on the system (perhaps gained by sniffing passwords, a dictionary attack, or
social engineering and is capable to exploit some susceptibility to gain root access to the system.
3) Remote to Local Attack (R2L): happens when an attacker who has the ability to send packets
to a machine over a network but who does not have an account on that machine exploits specific
weakness to gain local entrée as a user of that machine.
4) Probing Attack: is an task to gather information about a network of computers for the
apparent resolution of circumventing its security controls[36].
Table 5.1: Basic Characteristics of the KDD 99 Intrusion Detection Datasets In Terms of Samples
Sr.No Dataset DOS Probe U2r R2l Normal
1 KDD10 391458 4107 52 1126 97277
2 Whole KDD 3883370 41102 52 1126 972780
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6. RESULTS
6.1 CONSTRAINT SELECTION ALGORITHIM
By applying constraint selection algorithm we get following results as shown in table 1 &
features which are selected are marked as a status true and features which are rejected are marked
as a false. For selecting features following condition must be satisfied.
i.e Coh(Wi) >= Coh(Ω) then
ΩS = ΩS U {Wi}
Table 1. Result of constraint selection algorithm
Sr.No Coh(Wi) Coh(Ω) Status
1 0.777303234 0.640956891 True
2 0.73947529 0.640956891 True
3 0.730933496 0.640956891 True
4 0.749847468 0.640956891 True
5 0.640591966 0.640956891 False
6 0.765100671 0.640956891 True
7 0.573542736 0.640956891 False
8 0.584113561 0.640956891 False
9 0.653881003 0.640956891 True
10 0.776082977 0.640956891 True
11 0.761439902 0.640956891 True
12 0.761439902 0.640956891 True
13 0.55602537 0.640956891 False
14 0.572032619 0.640956891 False
15 0.785845027 0.640956891 True
Total Constraint=4950
Selected constraint=3928
6.2 FAST ALGORITHIM
Figure 3 shows how the features are selected by using symmetric uncertainty. Here we have
implemented FAST algorithim to select features. Features which are marked as a blue are
selected features.
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Figure 3. Selection of features by using symmetric uncertainty
6.3 FUZZY ARTMAP ALGORITHM
Figure 4 shows comparison between ARTMAP & Improved ARTMAP algorithms in terms of
classification accuracy. Intrusion detection system will identified attacks as a normal or anomaly
after applying fuzzy ARTMAP classifier.
Figure 4. Comparison of ARTMAP and improved ARTMAP after reducing features
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41
7. CONCLUSION
Security tools installation, monitoring to ensure security is the responsibility of the Security
administrator in an organization. IDS generate a large number of alerts (false positives). Most of
these alerts demand manual intervention from Administrator. Continuous monitoring of alerts
and there by evolving judgment for improving security is the major concern. Thus, Feature
relevance analysis is performed on KDD 99 training set, which is widely used by machine
learning researchers.. This framework for feature selection is based on constraint selection and
redundancy elimination for semi-supervised dimensionality reduction. A new score function was
developed to evaluate the relevance of features based on both, the locally geometrical structure of
unlabelled data and the constraint preserving ability of labelled data. Learning time of fuzzy
ARTMAP classifier is very less as compare to other classifier.
In future we can use another classifier for classification of intrusion detection system so that it
will save more time. Thus, Feature selection is an essential step in successful data mining
applications which can effectively reduce data dimensionality by removing irrelevant features if
we use that data our processing time will reduced.
ACKNOWLEDGMENT
Sincerely thank to all anonymous researchers for providing us such helpful opinion, findings,
conclusions and recommendations. Also thank to guide Prof. Roshani Ade, HOD Prof Arti
Mohanpurkar, Principal Dr.Uttam Kalwane & colleagues for their support and guidance.
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