This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
Due to diversity, heterogeneity and complexity of the existing healthcare structure, providing suitable
healthcare services is a complicated process. This work describes the conceptual design of an e-healthcare
system, which implements integration strategies and suitable technologies that will handle the
interoperability problem among its essential components. The proposed solution combines intelligent agent
technology and case based reasoning for highly distributed applications in healthcare environment.
Intelligent agents play a critical role in providing correct information for diagnostic, treatment, etc. They
work on behalf of human agents taking care of routine tasks, thus increasing speed and reliability of the
information exchanges. CBR is used to generate advices to a certain e-healthcare problems by analyzing
solutions given to previously solved problems and to build intelligent systems for disease diagnostics and
prognosis. Preliminary experimental simulation based on Agent Development Framework (JADE)
demonstrated the feasibility of this model.
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Bas...acijjournal
Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a
significant opportunity for improving the quality of health care services. Physiological signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria are to solve the real-time data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting
concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in Wireless Body Area Network( WBAN) based health care environment.
USE OF NETWORK FORENSIC MECHANISMS TO FORMULATE NETWORK SECURITYIJMIT JOURNAL
Network Forensics is fairly a new area of research which would be used after an intrusion in various
organizations ranging from small, mid-size private companies and government corporations to the defence
secretariat of a country. At the point of an investigation valuable information may be mishandled which
leads to difficulties in the examination and time wastage. Additionally the intruder could obliterate tracks
such as intrusion entry, vulnerabilities used in an entry, destruction caused, and most importantly the
identity of the intruder. The aim of this research was to map the correlation between network security and
network forensic mechanisms. There are three sub research questions that had been studied. Those have
identified Network Security issues, Network Forensic investigations used in an incident, and the use of
network forensics mechanisms to eliminate network security issues. Literature review has been the
research strategy used in order study the sub research questions discussed. Literature such as research
papers published in Journals, PhD Theses, ISO standards, and other official research papers have been
evaluated and have been the base of this research. The deliverables or the output of this research was
produced as a report on how network forensics has assisted in aligning network security in case of an
intrusion. This research has not been specific to an organization but has given a general overview about
the industry. Embedding Digital Forensics Framework, Network Forensic Development Life Cycle, and
Enhanced Network Forensic Cycle could be used to develop a secure network. Through the mentioned
framework, and cycles the author has recommended implementing the 4R Strategy (Resistance,
Recognition, Recovery, Redress) with the assistance of a number of tools. This research would be of
interest to Network Administrators, Network Managers, Network Security personnel, and other personnel
interested in obtaining knowledge in securing communication devices/infrastructure. This research
provides a framework that can be used in an organization to eliminate digital anomalies through network
forensics, helps the above mentioned persons to prepare infrastructure readiness for threats and also
enables further research to be carried on in the fields of computer, database, mobile, video, and audio.
Due to diversity, heterogeneity and complexity of the existing healthcare structure, providing suitable
healthcare services is a complicated process. This work describes the conceptual design of an e-healthcare
system, which implements integration strategies and suitable technologies that will handle the
interoperability problem among its essential components. The proposed solution combines intelligent agent
technology and case based reasoning for highly distributed applications in healthcare environment.
Intelligent agents play a critical role in providing correct information for diagnostic, treatment, etc. They
work on behalf of human agents taking care of routine tasks, thus increasing speed and reliability of the
information exchanges. CBR is used to generate advices to a certain e-healthcare problems by analyzing
solutions given to previously solved problems and to build intelligent systems for disease diagnostics and
prognosis. Preliminary experimental simulation based on Agent Development Framework (JADE)
demonstrated the feasibility of this model.
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Bas...acijjournal
Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a
significant opportunity for improving the quality of health care services. Physiological signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria are to solve the real-time data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting
concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in Wireless Body Area Network( WBAN) based health care environment.
USE OF NETWORK FORENSIC MECHANISMS TO FORMULATE NETWORK SECURITYIJMIT JOURNAL
Network Forensics is fairly a new area of research which would be used after an intrusion in various
organizations ranging from small, mid-size private companies and government corporations to the defence
secretariat of a country. At the point of an investigation valuable information may be mishandled which
leads to difficulties in the examination and time wastage. Additionally the intruder could obliterate tracks
such as intrusion entry, vulnerabilities used in an entry, destruction caused, and most importantly the
identity of the intruder. The aim of this research was to map the correlation between network security and
network forensic mechanisms. There are three sub research questions that had been studied. Those have
identified Network Security issues, Network Forensic investigations used in an incident, and the use of
network forensics mechanisms to eliminate network security issues. Literature review has been the
research strategy used in order study the sub research questions discussed. Literature such as research
papers published in Journals, PhD Theses, ISO standards, and other official research papers have been
evaluated and have been the base of this research. The deliverables or the output of this research was
produced as a report on how network forensics has assisted in aligning network security in case of an
intrusion. This research has not been specific to an organization but has given a general overview about
the industry. Embedding Digital Forensics Framework, Network Forensic Development Life Cycle, and
Enhanced Network Forensic Cycle could be used to develop a secure network. Through the mentioned
framework, and cycles the author has recommended implementing the 4R Strategy (Resistance,
Recognition, Recovery, Redress) with the assistance of a number of tools. This research would be of
interest to Network Administrators, Network Managers, Network Security personnel, and other personnel
interested in obtaining knowledge in securing communication devices/infrastructure. This research
provides a framework that can be used in an organization to eliminate digital anomalies through network
forensics, helps the above mentioned persons to prepare infrastructure readiness for threats and also
enables further research to be carried on in the fields of computer, database, mobile, video, and audio.
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 %.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
New Hybrid Intrusion Detection System Based On Data Mining Technique to Enhan...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Most of the network habitats retain on facing an ever increasing number of security threats. In early times,
firewalls are used as a security examines point in the network environment. Recently the use of Intrusion
Detection System (IDS) has greatly increased due to its more constructive and robust working than
firewall. An IDS refers to the process of constantly observing the incoming and outgoing traffic of a
network in order to diagnose suspicious behavior. In real scenario most of the environments are dynamic
in nature, which leads to the problem of concept drift, is perturbed with learning from data whose
statistical attribute change over time. Concept drift is impenetrable if the dataset is class-imbalanced. In
this review paper, study of IDS along with different approaches of incremental learning is carried out.
From this study, by applying voting rule to incremental learning a new approach is proposed. Further, the
comparison between existing Fuzzy rule method and proposed approach is done.
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.
Artificial Neural Content Techniques for Enhanced Intrusion Detection and Pre...AM Publications
This paper presents a novel approach for detecting network intrusions based on a competitive training neural
network. In the paper, the performance of this approach is compared to that of the self-organizing map (SOM), which is a
popular unsupervised training algorithm used in intrusion detection. While obtaining a similarly accurate detection rate as
the SOM does, the proposed approach uses only one forth of the computation times of the SOM. Furthermore, the
clustering result of this method is independent of the number of the initial neurons. This approach also exhibits the ability
to detect the known and unknown network attacks. The experimental results obtained by applying this approach to the
KDD-99 data set demonstrate that the proposed approach performs exceptionally in terms of both accuracy and
computation time.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE...ijaia
The principal objective of this work is to be able to use artificial intelligence techniques to be able to
design a predictive model of the performance of a third-generation mobile phone base radio, using the
analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of
these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used.
which will allow faster progress in the deep analysis of large amounts of data statistics and get better
results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base
radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case,
several models were generated based on in various artificial intelligence technique for the prediction of
performance results of a mobile phone base radio of third generation, the same ones that after several tests
were creation of a predictive model that determines the performance of a mobile phone base radio. As a
conclusion of this work, it was determined that the development of a predictive model based on artificial
intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict
complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a
radio base of third generation mobile telephony, these data were obtained through the operator's remote
monitoring and management tool Sure call PRS.
A one decade survey of autonomous mobile robot systems IJECEIAES
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.
A REVIEW ON PREDICTIVE ANALYTICS IN DATA MININGijccmsjournal
The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s
done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is
mainly used to make predictions about future events which are unknown. Predictive analytics which uses
various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for
analyzing the current data and to make predictions about future. The two main objectives of predictive
analytics are Regression and Classification. It is composed of various analytical and statistical techniques used
for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals
with both continuous changes and discontinuous changes. It provides a predictive score for each individual
(healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to
determine, or influence the organizational processes which pertain across huge numbers of individuals, like in
fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law
enforcement.
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 %.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
New Hybrid Intrusion Detection System Based On Data Mining Technique to Enhan...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Most of the network habitats retain on facing an ever increasing number of security threats. In early times,
firewalls are used as a security examines point in the network environment. Recently the use of Intrusion
Detection System (IDS) has greatly increased due to its more constructive and robust working than
firewall. An IDS refers to the process of constantly observing the incoming and outgoing traffic of a
network in order to diagnose suspicious behavior. In real scenario most of the environments are dynamic
in nature, which leads to the problem of concept drift, is perturbed with learning from data whose
statistical attribute change over time. Concept drift is impenetrable if the dataset is class-imbalanced. In
this review paper, study of IDS along with different approaches of incremental learning is carried out.
From this study, by applying voting rule to incremental learning a new approach is proposed. Further, the
comparison between existing Fuzzy rule method and proposed approach is done.
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.
Artificial Neural Content Techniques for Enhanced Intrusion Detection and Pre...AM Publications
This paper presents a novel approach for detecting network intrusions based on a competitive training neural
network. In the paper, the performance of this approach is compared to that of the self-organizing map (SOM), which is a
popular unsupervised training algorithm used in intrusion detection. While obtaining a similarly accurate detection rate as
the SOM does, the proposed approach uses only one forth of the computation times of the SOM. Furthermore, the
clustering result of this method is independent of the number of the initial neurons. This approach also exhibits the ability
to detect the known and unknown network attacks. The experimental results obtained by applying this approach to the
KDD-99 data set demonstrate that the proposed approach performs exceptionally in terms of both accuracy and
computation time.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE...ijaia
The principal objective of this work is to be able to use artificial intelligence techniques to be able to
design a predictive model of the performance of a third-generation mobile phone base radio, using the
analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of
these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used.
which will allow faster progress in the deep analysis of large amounts of data statistics and get better
results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base
radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case,
several models were generated based on in various artificial intelligence technique for the prediction of
performance results of a mobile phone base radio of third generation, the same ones that after several tests
were creation of a predictive model that determines the performance of a mobile phone base radio. As a
conclusion of this work, it was determined that the development of a predictive model based on artificial
intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict
complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a
radio base of third generation mobile telephony, these data were obtained through the operator's remote
monitoring and management tool Sure call PRS.
A one decade survey of autonomous mobile robot systems IJECEIAES
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.
A REVIEW ON PREDICTIVE ANALYTICS IN DATA MININGijccmsjournal
The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s
done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is
mainly used to make predictions about future events which are unknown. Predictive analytics which uses
various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for
analyzing the current data and to make predictions about future. The two main objectives of predictive
analytics are Regression and Classification. It is composed of various analytical and statistical techniques used
for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals
with both continuous changes and discontinuous changes. It provides a predictive score for each individual
(healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to
determine, or influence the organizational processes which pertain across huge numbers of individuals, like in
fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law
enforcement.
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
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.
An intrusion detection system plays a major role in network security. We
propose a model “DB-OLS: An Approach for IDS” which is a Deviation Based-Outlier
approach for Intrusion detection using Self Organizing Maps. In this model “Self
Organizing Map” approach is to be used for behavior learning and “Outlier mining”
approach, for detecting an intruder by calculating deviation from known user profile.
This model aims to improve the capability of detecting intruders.
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.
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.
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.
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.
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.
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.
A Survey on Different Machine Learning Algorithms and Weak Classifiers Based ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal
distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R)
attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,
these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of
KDD and NSL-KDD datasets using different classifiers in WEKA.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...ijaia
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
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.
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 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.
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.
Detecting Anomaly IDS in Network using Bayesian NetworkIOSR Journals
In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive
security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring
unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a
review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting
computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and
classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows
the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian
network.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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Volume 2-issue-6-2190-2194
1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume No. 2, Issue No. 6, June 2013
2190
www.ijarcet.org
A Survey on Intrusion Detection System in Data Mining
1
Sahilpreet Singh,2
Meenakshi Bansal
1,2
Departmentof Computer Engineering, Punjabi University
Yadavindra College of Engineering, Talwandi Sabo
Punjab, India
ABSTRACT
This paper presents a survey of techniques of
intrusion detection system using supervised and
unsupervised learning. The techniques are
categorized based upon different approaches like
Statistics, Data mining, Neural Network Based
and Self Organizing Maps Based approaches. The
detection type is borrowed from intrusion
detection as either misuse detection or anomaly
detection. It provides the reader with the major
advancement in the malware research using these
approaches the features and categories in the
surveyed work based upon the above stated
categories. This served as the major contribution
of this paper.
Keywords: - Intrusion Detection System, Neural
Network, Data Mining
1. INTRODUCTION
Computer networks and systems have become
indispensable tools for modern business much of
this information is, to some degree, confidential
and its protection is required. Not surprisingly then,
intrusion detection systems (IDS) have been
developed to help uncover attempts by
unauthorized persons and/or devices to gain access
to computer networks and the information stored
therein. An intrusion detection system (IDS) is a
device or software application that monitors
network or system activities for malicious activities
or policy violations and produces reports to a
management station. Some systems may attempt to
stop an intrusion attempt but this is neither required
nor expected of a monitoring system. Intrusion
detection and prevention systems (IDPS) are
primarily focused on identifying possible incidents,
logging information about them, and reporting
attempts. The development of IDS is motivated by
the following factors because Most existing
systems have security was that render them
susceptible to intrusions, and finding and fixing all
these deficiencies are not feasible. Prevention
techniques cannot be sufficient. It is almost
impossible to have an absolutely secure system.
Even the most secure systems are vulnerable to
insider attacks. New intrusions continually emerge
and new techniques are needed to defend against
them. Since there are always new intrusions that
cannot be prevented, IDS is introduced to detect
possible violations of a security policy by
monitoring system activities and response. IDSs are
aptly called the second line of defence, since IDS
comes into the picture after an intrusion has
occurred. If we detect the attack once it comes into
the network, a response can be initiated to prevent
or minimize the damage to the system.
It also helps
prevention techniques improve by providing
information about intrusion techniques. Data
mining techniques can be differentiated by their
different model functions and representation,
preference criterion, and algorithms. The main
function of the model that we are interested in is
classification, as normal, or malicious, or as a
particular type of attack. We are also interested in
link and sequence analysis. Additionally, data
mining systems provide the means to easily
perform data summarization and visualization,
aiding the security analyst in identifying areas of
concern. The models must be represented in some
form. Common representations for data mining
techniques include rules, decision trees, linear and
non-linear functions, instance-based examples, and
probability models.
DATA MINING BASED INTRUSION
DETECTION SYSTEM ARCHITECTURE
The overall system architecture is designed to
support a data mining-based IDS with the
properties described. The architecture is consists of
sensors, detectors, a data warehouse, and a model
generation component. This architecture is capable
of supporting not only data gathering, sharing, and
analysis, but also data archiving and model
generation and distribution. The system is designed
to be independent of the sensor data format and
model representation. A piece of sensor data can
contain an arbitrary number of features. Each
2. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume No. 2, Issue No. 6, June 2013
2191
www.ijarcet.org
feature can be continuous or discrete, numerical or
symbolic.
1.1 Sensors
Sensors observe raw data on a monitored system
and compute features for use in model evaluation.
Sensors insulate the rest of the IDS from the
specific low level properties of the target system
being monitored. This is done by having the entire
sensors implement a Basic Auditing Module
(BAM) framework. In a BAM, features are
computed from the raw data and encoded in XML.
1.2 Detectors
Detectors take processed data from sensors and use
a detection model to evaluate the data and
determine if it is an attack. The detectors also send
back the result to the data warehouse for further
analysis and report. There can be several (or
multiple layers of) detectors monitoring the same
system. There can also be a “back-end” detector,
which employs very sophisticated models for
correlation or trend analysis, and several “front-
end” detectors that perform quick and simple
intrusion detection.
1.3 Data Warehouse
The data warehouse serves as a centralized storage
for data and models. One advantage of a
centralized repository for the data is that different
components can manipulate the same piece of data
asynchronously with the existence of a database,
such as off-line training and manually labeling. The
data warehouse also facilitates the integration of
data from multiple sensors. By correlating
data/results from different IDSs or data collected
over a longer period of time, the detection of
complicated and large scale attacks becomes
possible.
1.4 Model Generator
The main purpose of the model generator is to
facilitate the rapid development and distribution of
new (or updated) intrusion detection models. In this
architecture, an attack detected first as an anomaly
may have its exemplary data processed by the
model generator, which in turn, using the archived
normal and intrusion data sets from the data
warehouse, automatically generates a model that
can detect the new intrusion and distributes it to the
detectors. Especially useful are unsupervised
anomaly detection algorithms because they can
operate on unlabeled data which can be directly
collected by the sensors.
Fig 1.The Architecture of Data Mining based IDS
2. DATA MINING BASED APPROACHES
Data mining is used in intrusion detection to
construct rules describing normal network
behaviors. The rules include association rules that
describe frequency associations between any two
fields of the network record database and also
frequent episodes that describe the frequency with
which a field takes a certain value after two other
fields have particular values in a definite time
interval. Deviations from these rules indicate an
attack on the network.
2.1 Supervised Learning-Based Approaches:
Recently, methods from machine learning and
pattern recognition have been utilized to detect
intrusions. Supervised learning and unsupervised
learning are both used. For supervised learning for
intrusion detection, there are mainly supervised
neural network (NN)-based approaches & support
vector machine (SVM)-based approaches
2.2 Unsupervised Learning-Based Approaches:
Supervised learning methods for intrusion detection
can only detect known intrusions. Unsupervised
learning methods can detect the intrusions that have
not been previously learned. An example of
unsupervised learning for intrusion detection
includes K-means-based approaches and self-
organizing feature map (SOM).Current approaches
for intrusion detection have the following two
problems.
3. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume No. 2, Issue No. 6, June 2013
2192
www.ijarcet.org
a) Current approaches often suffer from relatively
high false alarm rates, whereas they have high
detection rates. As most network behaviors are
normal, resources are wasted on checking a large
number of alarms that turn out to be false.
b)Their computational complexities are
oppressively high. This limits the practical
applications of these approaches.
3. RELATED WORK
Anazida Zainal et al. (2008) in paper has
discussed the Efficiency is one of the major issues
in intrusion detection. Inefficiency is often
attributed to high overhead and this is caused by
several reasons. The purpose of the paper is to
address the issue of continuous detection by
introducing traffic monitoring mechanism. In
traffic monitoring, a new recognition paradigm is
proposed in which it minimizes unnecessary
recognition. Therefore, the purpose of traffic
monitoring is two-folds; to reduce amount of data
to be recognized and to avoid unnecessary
recognition. For this Adaptive Neural Fuzzy
Inference System and Linear Genetic Programming
to form ensemble classifiers that shows a small
improvement using the ensemble approach for DoS
and R2L classes (attacks).
G. Zhai et al. (2010) in paper has discussed that
ID3 algorithm was a classic classification of data
mining. It always selected the attribute with many
values. The attribute with many values wasn’t the
correct one, it would created fault alarm and
omission alarm. To this fault, an improved decision
tree algorithm was proposed. The decision tree was
created after the data collected classified correctly.
With the help of using Decision tree algorithm it
shows the maximum attacks and also increases the
alert level after modified the decision tree.
Jorge Blasco et al. (2010) in paper has studied that
one of the central areas in network intrusion
detection is how to build effective systems that are
able to distinguish normal from intrusive traffic. To
avoid the blind use of GP, it provides the search by
means of a fitness function based on recent
advances on IDS evaluation. For the experimental
work use of a well-known dataset (i.e. KDD- 99)
that has become a standard to compare research
although its drawbacks. Results clearly show that
an intelligent use of GP provides better accuracy
and also compare the Hit rate and False Rate to
detect the number of attacks.
Ahmed Youssef et al. (2011) in paper has studied
that Intrusion detection has become a critical
component of network administration due to the
vast number of attacks persistently threaten our
computers. Traditional intrusion detection systems
are limited and do not provide a complete solution
For the problem. However, in many cases, they fail
to detect malicious behaviors (false negative) or
They fire alarms when nothing wrong in the
network (false positive). For this combination of
Data Mining Techniques and Network behavior
analysis were applied and overcome the limitations
of traditional Intrusion Detection System.
Mohd. Junedul Haque et al. (2012) in paper has
said that the Intrusion Detection system is an active
and driving secure technology to compromise the
confidentiality, integrity, availability, or to bypass
the security mechanisms of a network. The main
part of Intrusion Detection Systems (IDSs) is to
produce huge volumes of alarms. The interesting
alarms are always mixed with unwanted, non-
interesting and duplicate alarms. For this Data
mining algorithm, K means clustering, Distributed
IDS are applied to improve the detection rate and
decrease the false alarm rate.
S. Devaraju et al. (2013) in paper has discussed
about the security purpose in information system.
To deal with the problems of networks different
classifiers are used to detect the different kinds of
attacks. In this, the performance of intrusion
detection with various neural network classifiers is
compared. In this proposed research there are five
types of classifiers used. They are Feed Forward
Neural Network (FFNN), Elman Neural Network
(ENN), Generalized Regression Neural Network
(GRNN), Probabilistic Neural Network (PNN) and
Radial Basis Neural Network (RBNN). Finally it is
clear that Probabilistic Neural Network has better
accuracy than rest of other neural networks.
S.A.Joshi et al. (2013) in paper has presented that
with the tremendous growth in information
technology, network security is one of the
challenging issue and so as Intrusion Detection
system (IDS). The traditional IDS are unable to
manage various newly arising attacks. To
overcome this type of problem Data Mining
techniques, Feature Selection, Multiboosting were
applied. With data mining, it is easy to identify
valid, useful and understandable pattern in large
volume of data. Features are selected using binary
classifiers for more accuracy in each type of attack.
Multiboosting is used to reduce both the variance
and bias. Thus the efficiency and accuracy of
Intrusion Detection system are increased and
security of network so is also enhanced.
4. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume No. 2, Issue No. 6, June 2013
2193
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4. COMPARATIVE STUDY
Author(s) Year Paper Name Technique Results
S.A.Joshi, et al. 2013 Network Intrusion
Detection System
(NIDS) based on Data
Mining
Data Mining,
Feature Selection,
Multiboosting
Find high
detection rates
for U2R and
R2L and also to
detect attacks.
S. Devaraju, et al. 2013 Detection of Accuracy
for IDS in Neural
Network
Different types of
Neural Networks
and KDD cup
Probabilistic
Neural network
has better
accuracy than
others Neural
network.
Mohd. Junedul
Haque et al.
2011 An Intelligent
Approach for Intrusion
Detection Based on
Data Mining
Techniques
Data mining
algorithm, K means
clustering,
Distributed IDS
False alarm rate
has been
decreased also
clustering helps
in to identify the
attacked data.
Ahmed Youssef,
et al.
2011 Network Intrusion
Detection using Data
Mining and Network
behavior analysis
Data Mining
Techniques and
Network behavior
analysis
Combination of
both DM and
NBA overcome
the limitation of
traditional IDS
Jorge Blasco, et al. 2010 Improving Network
Intrusion Detection by
Means of Domain-
Aware Genetic
Programming
Use of Genetic
Programming
Explore the Hit
rate and False
Rate on data set
to detect no. of
attacks
G. Zhai et al. 2010 Research and
Improvement on ID3
Algorithm in Intrusion
Detection System
Decision tree
Algorithm
Shows
maximum
attacks and also
increases the
alert level after
modified the
decision tree
Anazida Zainal, et
al.
2008 Data Reduction and
Ensemble Classifiers in
Intrusion Detection
Adaptive Neural
Fuzzy Inference
System and Linear
Genetic
Programming
LGP has better
detection
accuracy than
ANFIS
5. CONCLUSION
It is shown in the paper that there is several
intrusion detections tools with competing features
which are develop for detection of attacks like
known attacks and unknown attacks and also
supervised and Un-supervised approaches are used
to detect the attacks. Unsupervised learning
methods can detect the intrusions that have not
been learned by supervised approaches.
REFRENCES
[1]. Anazida Zainal, Mohd Aizaini Maarof and Siti
Mariyam Shamsuddin “Data Reduction and Ensemble
Classifiers in Intrusion Detection” in 2008 IEEE.
[2]. Guangqun Zhai, Chunyan Liu “Research and
Improvement on ID3 Algorithm in Intrusion Detection
System” in 2010 IEEE
[3]. Jorge Blasco, Agustin Orfila, Arturo Ribagorda
“Improving Network Intrusion Detection by Means of
Domain-Aware Genetic Programming” DOI
10.1109/ARES.2010.53 in IEEE 2010.
[4]. Ahmed Youssef and Ahmed Emam “Network
Intrusion Detection using Data Mining and Network
5. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume No. 2, Issue No. 6, June 2013
2194
www.ijarcet.org
Behavior Analysis” International Journal of Computer
Science & Information Technology (IJCSIT) Vol 3, No 6,
Dec 2011.
[5]. Mohd. Junedul Haque, Khalid.W. Magld, Nisar
Hundewale “An Intelligent Approach for Intrusion
Detection Based on Data Mining Techniques” in 2012
IEEE.
[6] .N.S.Chandolikar, V.D.Nandavadekar “Comparative
analysis of two algorithm for Intrusion attack
classification using dataset” in International Journal of
Computer Science and Engineering ( IJCSE ) in 2012
.
[7]. Devendra kailashiya, Dr. R.C. Jain “Improve
Intrusion Detection Using Decision Tree with Sampling”
in IJCTA | MAY-JUNE 2012
[8]. S.A.Joshi, Varsha S.Pimprale “Network Intrusion
Detection System (NIDS) based on Data Mining”
International Journal of Engineering Science and
Innovative Technology (IJESIT) Volume 2, Issue 1,
January 2013
[9]. S. Devaraju, S .Ramakrishnan “Detection of
Accuracy for Intrusion Detection System using Neural
Network Classifier” International Journal of Emerging
Technology and Advanced Engineering( ISSN 2250-2459
(Online), An ISO 9001:2008 Certified Journal, Volume
3, Special Issue 1, January 2013)
[10] Yacine Bouzida, Frederic Cuppens “Neural
networks vs. decision trees for intrusion detection” in
2011.
Sahilpreet Singh received
his B.Tech degree in Information Technology from
Swami Vivekanand Institute of Engineering &
Technology (Ramnagar, Banur) under Punjab
Technical University in 2011 and pursuing M Tech.
(Regular) degree in computer engineering from
Yadavindra College of Engineering Punjabi
University Guru Kashi Campus Talwandi Sabo
(Bathinda), batch 2011-2013. His research interests
include improvement of Intrusion Detection
System in Data Mining.