Around 160 million hector unused is available in India. India is the world’s largest producer of castor oil,
producing over 75% of the total world’s supply. There are over a hundred companies in India-small and
medium-that are into castor oil production, producing a variety of the basic grades o castor oil. All the above
factors make it imperative that the India industry relooks at the castor oil sector in order to devise suitable
strategies to derive the most benefits from such an attractive confluence of factors. Castor oil is unique owing to
its exceptional diversity of application. The oil and its derivatives are used in over 100 different applications in
diverse industries such as paints, lubricants, pharma, cosmetics, paper, rubber and more. Recent developments
have successfully derived polyol from natural oils and synthesized range of PU product from them. However,
making flexible solution from natural oil polyol is still proving challenging. The goal of this thesis is to
understand the potentials and the limitations of natural oil as an alternative to petroleum polyol. An initial
attempt to understand natural oil polyol showed that flexible solution could be synthesized from castor oil,
which produced a rigid solution. Characterization results indicate that the glass transition temperature (Tg) was
the predominant factor that determines the rigidity of the solution. The high Tg of solution was attributed to the
low number of covalent bond between cross linkers.
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.
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.
Nowadays there are several security tools that used to protect computer systems, computer networks, smart devices and etc. against attackers. Intrusion detection system is one of tools used to detect attacks. Intrusion Detection Systems produces large amount of alerts, security experts could not investigate important alerts, also many of that alerts are incorrect or false positives. Alert management systems are set of approaches that used to solve this problem. In this paper a new alert management system is presented. It uses K-nearest neighbor as a core component of the system that classify generated alerts. The suggested system serves precise results against huge amount of generated alerts. Because of low classification time per each alert, the system also could be used in online systems.
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.
Managing Intrusion Detection Alerts Using Support Vector MachinesCSCJournals
In the computer network world Intrusion detection systems (IDS) are used to identify attacks
against computer systems. They produce security alerts when an attack is done by an intruder.
Since IDSs generate high amount of security alerts, analyzing them are time consuming and error
prone. To solve this problem IDS alert management techniques are introduced. They manage
generated alerts and handle true positive and false positive alerts. In this paper a new alert
management system is presented. It uses support vector machine (SVM) as a core component of
the system that classify generated alerts. The proposed algorithm achieves high accurate result
in false positives reduction and identifying type of true positives. Because of low classification
time per each alert, the system also could be used in active alert management systems.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
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.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
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.
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.
A web application detecting dos attack using mca and tameSAT Journals
Abstract
Interconnected systems, such as all kind of servers including web servers, are been always under the threats of network attackers. There are many popular attacks like man in middle attack, cross site scripting, spamming etc. but Denial of service attack is considered to be one of most dangerous attack on the networked applications. The attack causes many serious issues on these computing systems A denial-of-service (DoS) attack is an attempt to make a machine or network resource unavailable to the intended users. The performance of the server is reduced by the DoS attack, so, to increase the efficiency of the server, detection of the attack is necessary. Hence Multivariate Correlation Analysis’ issued, this approach employs triangle area for extracting the correlation information between network traffic. Our implemented system is evaluated using KDD Cup 99 data set, and the treatment of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The implemented system has capability of learning new patterns of legitimate network traffic hence it detect both known and unknown types of DoS attacks and we can say that It is working on the principle of anomaly based attack detection. Triangle-area-based technique is used to speed up the process. The stored legitimate profiles has to keep secured so Detection e=mechanism for the SQL injection is also implemented in the system. The system designed to carry out attack detection is a question-answer portal i.e. a web application and hence the system is using HTTP protocol unlike previous systems which were using TCP. Keywords: Denial-of-Service attack, Features Normalization, Triangle Area Map(TAM), Multivariate Correlation Analysis(MCA), anomaly based detection, SQL injection, HTTP, and TCP,
False positive reduction by combining svm and knn algoeSAT Journals
Abstract
With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate.
Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.
CYBERSECURITY INFRASTRUCTURE AND SECURITY AUTOMATIONacijjournal
AI-based security systems utilize big data and powerful machine learning algorithms to automate the security management task. The case study methodology is used to examine the effectiveness of AI-enabled security solutions. The result shows that compared with the signature-based system, AI-supported security applications are efficient, accurate, and reliable. This is because the systems are capable of reviewing and correlating large volumes of data to facilitate the detection and response to threats.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
The purpose of this paper is to describe two objective fuzzy genetics-based learning algorithms
and discusses its usage to detect intrusion in a computer network. Experiments were performed
with KDD-cup data set, which have information on computer networks, during normal behavior
and intrusive behavior. The performance of final fuzzy classification system has been
investigated using intrusion detection problem as a high dimensional classification problem.
This task is formulated as optimization problem with two objectives: To minimize the number of
fuzzy rules and to maximize the classification rate. We show a two-objective genetic algorithm
for finding non-dominated solutions of the fuzzy rule selection problem
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.
Nowadays there are several security tools that used to protect computer systems, computer networks, smart devices and etc. against attackers. Intrusion detection system is one of tools used to detect attacks. Intrusion Detection Systems produces large amount of alerts, security experts could not investigate important alerts, also many of that alerts are incorrect or false positives. Alert management systems are set of approaches that used to solve this problem. In this paper a new alert management system is presented. It uses K-nearest neighbor as a core component of the system that classify generated alerts. The suggested system serves precise results against huge amount of generated alerts. Because of low classification time per each alert, the system also could be used in online systems.
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.
Managing Intrusion Detection Alerts Using Support Vector MachinesCSCJournals
In the computer network world Intrusion detection systems (IDS) are used to identify attacks
against computer systems. They produce security alerts when an attack is done by an intruder.
Since IDSs generate high amount of security alerts, analyzing them are time consuming and error
prone. To solve this problem IDS alert management techniques are introduced. They manage
generated alerts and handle true positive and false positive alerts. In this paper a new alert
management system is presented. It uses support vector machine (SVM) as a core component of
the system that classify generated alerts. The proposed algorithm achieves high accurate result
in false positives reduction and identifying type of true positives. Because of low classification
time per each alert, the system also could be used in active alert management systems.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
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.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
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.
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.
A web application detecting dos attack using mca and tameSAT Journals
Abstract
Interconnected systems, such as all kind of servers including web servers, are been always under the threats of network attackers. There are many popular attacks like man in middle attack, cross site scripting, spamming etc. but Denial of service attack is considered to be one of most dangerous attack on the networked applications. The attack causes many serious issues on these computing systems A denial-of-service (DoS) attack is an attempt to make a machine or network resource unavailable to the intended users. The performance of the server is reduced by the DoS attack, so, to increase the efficiency of the server, detection of the attack is necessary. Hence Multivariate Correlation Analysis’ issued, this approach employs triangle area for extracting the correlation information between network traffic. Our implemented system is evaluated using KDD Cup 99 data set, and the treatment of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The implemented system has capability of learning new patterns of legitimate network traffic hence it detect both known and unknown types of DoS attacks and we can say that It is working on the principle of anomaly based attack detection. Triangle-area-based technique is used to speed up the process. The stored legitimate profiles has to keep secured so Detection e=mechanism for the SQL injection is also implemented in the system. The system designed to carry out attack detection is a question-answer portal i.e. a web application and hence the system is using HTTP protocol unlike previous systems which were using TCP. Keywords: Denial-of-Service attack, Features Normalization, Triangle Area Map(TAM), Multivariate Correlation Analysis(MCA), anomaly based detection, SQL injection, HTTP, and TCP,
False positive reduction by combining svm and knn algoeSAT Journals
Abstract
With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate.
Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.
CYBERSECURITY INFRASTRUCTURE AND SECURITY AUTOMATIONacijjournal
AI-based security systems utilize big data and powerful machine learning algorithms to automate the security management task. The case study methodology is used to examine the effectiveness of AI-enabled security solutions. The result shows that compared with the signature-based system, AI-supported security applications are efficient, accurate, and reliable. This is because the systems are capable of reviewing and correlating large volumes of data to facilitate the detection and response to threats.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
The purpose of this paper is to describe two objective fuzzy genetics-based learning algorithms
and discusses its usage to detect intrusion in a computer network. Experiments were performed
with KDD-cup data set, which have information on computer networks, during normal behavior
and intrusive behavior. The performance of final fuzzy classification system has been
investigated using intrusion detection problem as a high dimensional classification problem.
This task is formulated as optimization problem with two objectives: To minimize the number of
fuzzy rules and to maximize the classification rate. We show a two-objective genetic algorithm
for finding non-dominated solutions of the fuzzy rule selection problem
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.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Review of Intrusion and Anomaly Detection Techniques IJMER
Intrusion detection is the act of detecting actions that attempt to compromise the
confidentiality, integrity or availability of a resource. With the tremendous growth of network-based
services and sensitive information on networks, network security is getting more and more importance
than ever. Intrusion poses a serious security threat in a huge network environment. The increasing use of
internet has dramatically added to the growing number of threats that inhabit within it. Intrusion
detection does not, in general, include prevention of intrusions. Now a days Network intrusion detection
systems have become a standard component in the area of security infrastructure. This review paper tries
to discusses various techniques which are already being used for intrusion detection.
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.
Supervised Machine Learning Algorithms for Intrusion Detection.pptxssuserf3a100
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A SURVEY ON GENETIC ALGORITHM
FOR INTRUSION DETECTION SYSTEM
MS. DIMPI K PATEL
Department of Computer Science and Engineering,
Hasmukh Goswami college of Engineering,
Ahmedabad, Gujarat
ABSTRACT
The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of
business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of
information between various organizations.
Intrusion Detection Systems have become a needful component in terms of computer and network security. Intrusion detection is
one of the important security constraints for maintaining the integrity of information. Intrusion detection systems are the tools
used for prevention and detection of threats to computer systems. Various approaches have been applied in past that are less
effective to curb the menace of intrusion.
In this paper, a survey on applications of genetic algorithms in intrusion detection systems is carried out.
Keyword: Intrusion Detection System (IDS), Network Intrusion detection system (NIDS), Host Intrusion Detection Systems
(HIDS), Genetic algorithm (GA).
1. INRODUCTION
The computer technology has evolved at a very fast pace since last few decades. With this rapid growth of computer technology
has resulted in the transfer of more and more services to computer based systems. The dependency of such services on computer
technology has resulted in the increase of computer related threats. Viruses, Worms and Trojan horses and hacking are some of
the major attacks becoming painful for any network systems.
Most of the security mechanisms designed so far, try to prevent unauthorized access to system resources and data. However,
designed such systems are not able to completely prevent intrusions into computer systems. The main need is to detect intrusions
efficiently and from that their impact can be realized and damages can be repaired. Thus, Intrusion Detection System (IDS) has
become one of the hottest research areas in Computer Security now a days.
The conventionally used security tools like firewalls, intrusion detection systems (IDS) has become with supreme significance.
Intrusion Detections Systems (IDS) is a new path of security systems, which provides efficient approaches to secure computer
networks. Artificial Intelligence approaches have been used at large level to produce a lot of IDS. Some of these approaches
dependant on Genetic Algorithms to provide the network with an efficient classifier to recognize and detect intrusions actions.
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2. SYSTEM OVERVIEW
2.1 Intrusion Detection Systems
An intrusion detection system is a device or software application which monitor network or system activities for malicious
activities and providing reports to a management stations[1]. The main aim of Intrusion Detection Systems (IDS) is to protect the
availability, confidentiality and integrity of critical networked information systems.
Intrusions Detection can be classified into two major categories as network intrusion detection systems (NIDS), host intrusion
detection systems (HIDS). In Network Intrusion Detection System, it evaluate information captured from network
communications and analyze the stream of packets which travel across the network. In Host Intrusion Detection System, It
evaluate information found on a single or multiple host systems, including contents of operating systems, system and application
files.
An intrusion detection system generally composed of three functional components [6].
The first component is also known as the 'event generator', is a data source. Data sources can be categorized into four categories
namely Host-based monitors, Network-based monitors, Application-based monitors and Target-based monitors.
The second component of an intrusion detection system is also known as the 'analysis engine'. This component takes information
from the data source and it examines the data for symptoms of attacks or other policy violations. The analysis engine can use one
or both of the following analysis approaches:
I. Misuse/Signature-Based Detection: This type of detection engine detects intrusions which follows well-known patterns
of attacks (or signatures) that exploit known software vulnerabilities [9]. The main demerit of this approach is that it only
looks for the known weaknesses and may not care about detecting unknown future intrusions .
II. Anomaly/Statistical Detection: An anomaly based detection engine will search for something rare or unusual [13]. They
analyses system event streams, using statistical techniques to find patterns of activity that appear to be abnormal. The
main demerits of this system are that they are highly expensive and they can recognize an intrusive behavior as normal
behavior because of insufficient data.
The third component of an intrusion detection system is the response manager. In basic terms, the response manager will only act
when inaccuracies (possible intrusion attacks) are found on the system, by informing someone or something in the form of a
response.
Figure 1[14], gives a generic architecture of an intrusion detection system.
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In figure 1, the audit source represents the input to the intrusion detection system. The format of input data can be of different
types depending upon the type and location of the intrusion detection system. The collector samples and preprocesses the audit
source data. The data is transformed into a standard format known to the internal components of the intrusion detection system.
The knowledge database contains information about attacks. The classification engine determines the legitimateness of the
received data by comparing it with the attack information stored in the knowledge database. The policy rules are used to configure
the response and detection of intrusion system. The response unit produces different types of responses depending upon the
incoming events and their severity. The event database stores the detailed information about the events, which is used for various
purposes like attack report generation, and framing new rules.
2.2 Fuzzy logic
Fuzzy Logic was introduced as a means to the model of uncertainty of natural language. And due to the uncertainty nature of
intrusions fuzzy sets are strongly used in discovering attack events and reducing the rate of false alarms at the same time.
Basically, intrusion detection systems distinguish between two distinct types of behaviors, normal and abnormal, which create
two distinct sets of rules and information. Fuzzy logic could create sets that have in-between values where the differences
between the two sets are not well defined. In this case the logic depends on linguistics by taking the minimum of set of events or
maximum instead of stating OR, AND or NOT operation in the if-then-else condition. This feature strongly participates in
reducing the false positive alarm rates in the system. Applying fuzzy methods for the development of IDS yield some advantages,
compared to the classical approach. Therefore, Fuzzy logic techniques have been employed in the computer security field since
the early 90’s. The fuzzy logic provides some flexibility to the uncertain problem of intrusion detection and allows much greater
complexity for IDS[3].
2.3 Genetic Algorithms
“A Genetic Algorithm (GA) is a programming technique that mimics biological evolution as a problem-solving strategy”[12]. It is
based on Darwinian’s principle of evolution and survival of fittest to optimize a population of candidate solutions towards a
predefined fitness[2].
The process of a genetic algorithm usually begins with a randomly selected population of chromosomes. These chromosomes are
representations of the rules. According to the attributes of the rules, different positions of each chromosome are encoded in binary
bits. These positions are sometimes referred to as genes and are changed randomly within a range during evolution. The set of
chromosomes during a stage of evolution are called a population. An evaluation function is used to calculate the “Fitness” of each
chromosome. During evaluation, two basic operators, crossover and mutation, are used to simulate the natural reproduction and
mutation of species. The selection of chromosomes for survival and combination is biased towards the fittest chromosomes.
The following figure 2 taken from [2] shows the structure of a simple genetic algorithm. Starting by a random generation of initial
population, then evaluate and evolve through selection, crossover, and mutation. Finally, the best individual (chromosome) is
picked out as the final result once the optimization meet its target.
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Major Steps in Genetic Algorithm
Algorithm: Rule set generation using genetic algorithm.
Input: Network audit data, number of generations, and population size.
Output: A set of classification rules
1. Initialize the population
2. Check the fitness function
3. Select only those rules that that meets the fitness criteria.
4. Perform crossover for reproduction of new rule by exchanging some bits
5. Perform mutation by flipping some bits
6. Again go to line 2, until the specified numbers of rules are not generated.
3. LITERATURE REVIEW
Several case studies and experiments were applied regarding the use of genetic algorithm in intrusion detection system. Some
import applications of soft computing techniques for network intrusion detection is described in this section. Several Genetic
Algorithms and Genetic Programming has been used for detecting intrusion detection of different kinds in different scenarios.
In [5], Chittur A applied detection of computer intrusions and malicious computer behavior and analyzed the effectiveness of a
Genetic Algorithm.
In [2], Li described a method using GA to detect anomalous network intrusion . His approach includes both Quantitative and
categorical features of network data for deriving classification rules. He addressed the Factors affecting to Genetic Algorithm are
in detail. The inclusion of quantitative feature can increase detection rate but experimental results are not available.
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In [4], Xia et al. detected anomalous network behaviors based on information theory by using GA. Some network features can be
identified with network attacks based on mutual information between network features and type of intrusions and then using these
features a linear structure rule and also a GA is derived. The advantage of the approach of using mutual information and resulting
linear rule seems very effective because of the reduced complexity and higher detection rate. The approach has disadvantage that
it considered only the discrete features.
In [11], Abdullah showed A GA based performance evaluation algorithm to network intrusion detection. The traffic data was
filtered by information theory in his approach.
In [10], Lu and Traore used support-confidence framework as the fitness function and accurately classified several network
intrusions. Disadvantage of their approach is that, use of genetic programming made the implementation procedure very difficult
and also for training procedure more data and time is required.
In [8], Crosbie and Spafford applied the multiple agent technology and GP to detect network anomalies. For both agents they
used GP to determine anomalous network behaviors and each agent can monitor one parameter of the network audit data.
Advantage of this technique is that it acts well when many small autonomous agents are used. And Disadvantage is if the agents
are not properly initialized the training process can be time consuming, when communicating among the agents.
In [7], Gong presented An implementation of GA based approach to Network Intrusion Detection using GA and showed software
implementation. In this approach he derived a set of classification rules using a support-confidence framework to judge fitness
function.
4.CONCLUSION
In this survey an introduction to intrusion detection and genetic algorithms are presented. It mainly focuses on various IDS
models. It is realized that various techniques can be used to implement IDS. The paper provides enough information for
newcomers to the field of genetic algorithms based intrusion detection.
5.ACKNOWLEDGEMENT
I want to thank my supervisor Prof. Indr Jeet Rajput, Assistant Professor in HGCE, Ahmadabad not only for his continued
support but for the motivation and fruitful advises in accomplishing this task.
6.REFERENCES
[1] http://en.wikipedia.org/wiki/Intrusion_detection_system
[2] W. Li, “Using Genetic Algorithm for Network Intrusion Detection”. SANS Institute, USA, 2004.
[3] N. Bashah Idris, B. Shanmugam “Novel Attack Detection Using Fuzzy Logic and Data Mining”, International Conference of
Soft Computing and Pattern Recognition, SOCPAR '2009.
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[4] T. Xia, G. Qu, S. Hariri, M. Yousif, “An Efficient Network Intrusion Detection Method Based on Information Theory and
Genetic Algorithm”, Proceedings of the 24th IEEE International Performance Computing and Communications Conference
(IPCCC ’05), Phoenix, AZ, USA, 2005.
[5] A. Chittur, “Model Generation for an Intrusion Detection System Using Genetic Algorithms”. January 2005.
[6] R. G. Bace, “Intrusion Detection”, Macmillan Technical Publishing, 2000.
[7] R. H. Gong, M. Zulkernine, P. Abolmaesumi, “A Software Implementation of a Genetic Algorithm Based Approach to
Network Intrusion Detection”, 2005.
[8] M. Crosbie, E. Spafford, “Applying Genetic Programming to Intrusion Detection”, Proceedings of the AAAI Fall
Symposium, 1995.
[9] S. Kumar, E. Spafford, “A Software architecture to Support Misuse Intrusion Detection”, in the 18th National Information
Security Conference, pp. 194-204, 1995.
[10] W. Lu, I. Traore, “Detecting New Forms of Network Intrusion Using Genetic Programming”. Computational Intelligence,
vol. 20, pp. 3, Blackwell Publishing, Malden, pp. 475-494, 2004.
[11] B. Abdullah, I. Abd-alghafar, Gouda I. Salama, A. Abd-alhafez, “Performance Evaluation of a Genetic Algorithm Based
Approach to Network Intrusion Detection System”, 2009.
[12] Md. Sazzadul Hoque, Md. Abdul Mukit , Md. Abu Naser Bikas, "An Implementation of Intrusion Detection System using
Genetic Algorithm", International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
[13] S. Kumar, “Classification and Detection of Computer Intrusions”, Purdue University, 1995.
[14] M. Arvidson and M. Carlbark, “Intrusion Detection Systems: Technologies, Weaknesses, and Trends,” 2003.