I am an Information Security Expert by degree, currently involving in teaching and learning process along with a bit of research works. I was initially a Computer Programmer and stayed in the software development field for more than 15 years. Now, it's been 30 years that I am in IT field.I am very simple, down-to-earth and would like to get in touch with more and more professionals and experts. I respect work and know that even a small piece of work requires a dedication and good effort. Supervisors: Huang Chuanhe
LAP LAMBERT Academic Publishing eBooks, Nov 14, 2016
Newer intrusions are coming out every day with the all-way growth of the Internet. In this contex... more Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.
ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no... more ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.
ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent... more ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.
ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Informat... more ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.
MedS Alliance Journal of Medicine and Medical Sciences, 2021
INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental diso... more INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental disorder, visual problems, headache, weight gain and unnecessary time consumption. Therefore, this study aims to identify the information about the practices on the use of digital devices, its impact on physical health and pattern of self-care among the university students involved in different professions. MATERIALS AND METHODS: MPhil scholars involved in different professions (n= 315) of Nepal Open University (NOU) had participated in this cross-sectional online survey during January 2019 to August 2019. Multivariable analysis was employed to obtain rate ratios and chi-square test was used for the association of the use of digital devices with physical health problems. RESULTS: Socio-demographic factors like age was significantly associated with neck pain (p=0.02) and stiffness in hands/arms (p=0.04), while profession was associated with weight gain and difficulty in sleep (p=0.04). Moreov...
2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, 2012
ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Informat... more ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.
2012 Third Asian Himalayas International Conference on Internet, 2012
ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent... more ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.
ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no... more ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.
In the era of Internet of Things and with the explosive worldwide growth of electronic data volum... more In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis and storage of such humongous volume of data, several new challenges are faced in protecting privacy of sensitive data and securing systems by designing novel schemes for secure authentication, integrity protection, encryption and non-repudiation. Lightweight symmetric key cryptography and adaptive network security algorithms are in demand for mitigating these challenges. This book presents some of the state-of-the-art research work in the field of cryptography and security in computing and communications. It is a valuable source of knowledge for researchers, engineers, practitioners, graduate and doctoral students who are working in the field of cryptography, network security and security and privacy issues in the Internet of Things (IoT), and machine learning application in security. It will also be useful for faculty members of graduate s...
This study aimed to investigate ICT competency of mathematics teachers at secondary schools of Ne... more This study aimed to investigate ICT competency of mathematics teachers at secondary schools of Nepal. A cross-sectional survey design was deployed among 336 secondary school teachers of Nepal. The data was analyzed by Mann Whitney U, Kruskal Wallis H, and multiple linear regression. Result showed that teachers’ ICT competency level was found to be proficient in the fundamental concept of computers and the use of Internet. In contrast, it was found to be developing-level in software and hardware. Statistically significant results were found in competencies with respect to age, type of school, culture, and district. Additionally, own laptop, Internet use, work experience, knowledge of software and hardware were significant predictors for ICT competency of teachers. The overall findings clarify that ICT enhancement programs are needed for mathematics teachers at secondary schools in Nepal.
With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) ha... more With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (...
Newer intrusions are coming out every day with the all-way growth of the Internet. In this contex... more Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find t...
LAP LAMBERT Academic Publishing eBooks, Nov 14, 2016
Newer intrusions are coming out every day with the all-way growth of the Internet. In this contex... more Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.
ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no... more ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.
ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent... more ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.
ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Informat... more ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.
MedS Alliance Journal of Medicine and Medical Sciences, 2021
INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental diso... more INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental disorder, visual problems, headache, weight gain and unnecessary time consumption. Therefore, this study aims to identify the information about the practices on the use of digital devices, its impact on physical health and pattern of self-care among the university students involved in different professions. MATERIALS AND METHODS: MPhil scholars involved in different professions (n= 315) of Nepal Open University (NOU) had participated in this cross-sectional online survey during January 2019 to August 2019. Multivariable analysis was employed to obtain rate ratios and chi-square test was used for the association of the use of digital devices with physical health problems. RESULTS: Socio-demographic factors like age was significantly associated with neck pain (p=0.02) and stiffness in hands/arms (p=0.04), while profession was associated with weight gain and difficulty in sleep (p=0.04). Moreov...
2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, 2012
ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Informat... more ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.
2012 Third Asian Himalayas International Conference on Internet, 2012
ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent... more ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.
ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no... more ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.
In the era of Internet of Things and with the explosive worldwide growth of electronic data volum... more In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis and storage of such humongous volume of data, several new challenges are faced in protecting privacy of sensitive data and securing systems by designing novel schemes for secure authentication, integrity protection, encryption and non-repudiation. Lightweight symmetric key cryptography and adaptive network security algorithms are in demand for mitigating these challenges. This book presents some of the state-of-the-art research work in the field of cryptography and security in computing and communications. It is a valuable source of knowledge for researchers, engineers, practitioners, graduate and doctoral students who are working in the field of cryptography, network security and security and privacy issues in the Internet of Things (IoT), and machine learning application in security. It will also be useful for faculty members of graduate s...
This study aimed to investigate ICT competency of mathematics teachers at secondary schools of Ne... more This study aimed to investigate ICT competency of mathematics teachers at secondary schools of Nepal. A cross-sectional survey design was deployed among 336 secondary school teachers of Nepal. The data was analyzed by Mann Whitney U, Kruskal Wallis H, and multiple linear regression. Result showed that teachers’ ICT competency level was found to be proficient in the fundamental concept of computers and the use of Internet. In contrast, it was found to be developing-level in software and hardware. Statistically significant results were found in competencies with respect to age, type of school, culture, and district. Additionally, own laptop, Internet use, work experience, knowledge of software and hardware were significant predictors for ICT competency of teachers. The overall findings clarify that ICT enhancement programs are needed for mathematics teachers at secondary schools in Nepal.
With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) ha... more With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (...
Newer intrusions are coming out every day with the all-way growth of the Internet. In this contex... more Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find t...
Newer intrusions are coming out every day with the all-way growth of the Internet. In this contex... more Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.
With the rapid and wide-spread growth of internet technology, security risks and
threats are also... more With the rapid and wide-spread growth of internet technology, security risks and threats are also increasing day by day. Newer versions of attacks and intrusions are evolving continuously by putting extra challenges to the field of intrusion detection. In this present context, this thesis work proposes a hybrid approach of intrusion detection along with a hybrid architecture of intrusion detection system. The proposed architecture is flexible enough to perform intrusion detection tasks either by using a single hybrid module or by using multiple hybrid modules. The “Clustering-Outlier detection followed by SVM classification” is proposed as the first hybrid IDS module to be used in the architecture, whereas the second module proposed is the “Incremental SVM with Half-partition method”. Аll thе prоpоsеd аpprоаchеs аnd rеsеаrch wоrks hаvе еnhаncеd thе dеtеctіоn rаtе wіth mіnіmum fаlsе pоsіtіvе rаtеs. Thе prоpоsеd аlgоrіthms е.g. Clustеrіng-Оutlіеr Dеtеctіоn аlgоrіthm аnd CSV-ІSVM аrе аlsо tеstеd аnd cоmpаrеd еxpеrіmеntаlly wіth оthеr sіmіlаr mеthоds аnd аrе fоund bеttеr tо bе usеd by ІDS іn rеаl-tіmе еnvіrоnmеnt. Thеsе prоpоsеd mеthоds cаn bе usеd fоr nеtwоrk іntrusіоn dеtеctіоn іn rеаl-tіmе bеcаusе оf іts hіghеr dеtеctіоn rаtе, іmprоvеd fаlsе аlаrm rаtе аs wеll аs аccеptаbly lеss аmоunt оf lеаrnіng tіmе. Kеywоrds: Hybrіd Іntrusіоn Dеtеctіоn, Clustеrіng-Оutlіеr Dеtеctіоn, Іncrеmеntаl SVM, Cаndіdаtе Suppоrt Vеctоr, Hаlf-Pаrtіtіоn Mеthоd.
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threats are also increasing day by day. Newer versions of attacks and intrusions are
evolving continuously by putting extra challenges to the field of intrusion detection.
In this present context, this thesis work proposes a hybrid approach of intrusion
detection along with a hybrid architecture of intrusion detection system. The proposed
architecture is flexible enough to perform intrusion detection tasks either by using a
single hybrid module or by using multiple hybrid modules. The “Clustering-Outlier
detection followed by SVM classification” is proposed as the first hybrid IDS module
to be used in the architecture, whereas the second module proposed is the
“Incremental SVM with Half-partition method”. Аll thе prоpоsеd аpprоаchеs аnd rеsеаrch wоrks hаvе еnhаncеd thе dеtеctіоn rаtе
wіth mіnіmum fаlsе pоsіtіvе rаtеs. Thе prоpоsеd аlgоrіthms е.g. Clustеrіng-Оutlіеr
Dеtеctіоn аlgоrіthm аnd CSV-ІSVM аrе аlsо tеstеd аnd cоmpаrеd еxpеrіmеntаlly
wіth оthеr sіmіlаr mеthоds аnd аrе fоund bеttеr tо bе usеd by ІDS іn rеаl-tіmе
еnvіrоnmеnt. Thеsе prоpоsеd mеthоds cаn bе usеd fоr nеtwоrk іntrusіоn dеtеctіоn іn
rеаl-tіmе bеcаusе оf іts hіghеr dеtеctіоn rаtе, іmprоvеd fаlsе аlаrm rаtе аs wеll аs
аccеptаbly lеss аmоunt оf lеаrnіng tіmе.
Kеywоrds: Hybrіd Іntrusіоn Dеtеctіоn, Clustеrіng-Оutlіеr Dеtеctіоn, Іncrеmеntаl
SVM, Cаndіdаtе Suppоrt Vеctоr, Hаlf-Pаrtіtіоn Mеthоd.