1. The document proposes a genetic-fuzzy based method for automatic intrusion detection using network datasets. It combines fuzzy set theory with genetic algorithms to extract rules for both discrete and continuous attributes to detect normal and intrusion patterns.
2. The method was tested on KDD99 Cup and DARPA98 network intrusion detection datasets and showed high detection rates with low false alarm rates for both misuse detection and anomaly detection.
3. By extracting many rules to represent normal network behavior patterns, the proposed genetic-fuzzy approach can detect new or unknown intrusions based on anomalies without requiring prior domain expertise on intrusion patterns.
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.
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.
Abstract—Classical machine learning techniques have been employed severally in intrusion detection. But due to the rising cases and sophistication of attacks, more advanced machine learning techniques including ensemble-based methods, neural networks and deep learning techniques have been applied. However, there is still need for improved machine learning approach to detect attacks more effectively and efficiently. Stacked generalization approach has been shown to be capable of learning from features and meta-features but has been limited by the deficiencies of base classifiers and lack of optimization in the choice of meta-feature combination. This paper therefore proposes a stacked generalization ensemble approach based on two-tier meta-learner, in which the outputs of classical stacked ensemble are passed to multi-feature-based stacked ensemble, which is optimized. A Grid-search approach is used for the optimization. Nine data features and four meta-features derived from Logistic Regression, Support Vector Machine, Naïve Bayes, and Multilayer Perceptron neural network are used for the machine learning classification task. By applying neural networks as the meta-learner for the classification of NSL-KDD data, improved performances in terms of accuracy, precision, recall and F-measure of 0.97, 0.98, 0.98 and 0.98, respectively are achieved.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
New kind of intrusions causes deviation in the normal behaviour of traffic flow in
computer networks every day. This study focused on enhancing the learning capabilities of IDS
to detect the anomalies present in a network traffic flow by comparing the k-means approach of
data mining for intrusion detection and the outlier detection approach. The k-means approach
uses clustering mechanisms to group the traffic flow data into normal and abnormal clusters.
Outlier detection calculates an outlier score (neighbourhood outlier factor (NOF)) for each flow
record, whose value decides whether a traffic flow is normal or abnormal. These two methods
were then compared in terms of various performance metrics and the amount of computer
resources consumed by them. Overall, k-means was more accurate and precise and has better
classification rate than outlier detection in intrusion detection using traffic flows. This will help
systems administrators in their choice of IDS.
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.
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.
Abstract—Classical machine learning techniques have been employed severally in intrusion detection. But due to the rising cases and sophistication of attacks, more advanced machine learning techniques including ensemble-based methods, neural networks and deep learning techniques have been applied. However, there is still need for improved machine learning approach to detect attacks more effectively and efficiently. Stacked generalization approach has been shown to be capable of learning from features and meta-features but has been limited by the deficiencies of base classifiers and lack of optimization in the choice of meta-feature combination. This paper therefore proposes a stacked generalization ensemble approach based on two-tier meta-learner, in which the outputs of classical stacked ensemble are passed to multi-feature-based stacked ensemble, which is optimized. A Grid-search approach is used for the optimization. Nine data features and four meta-features derived from Logistic Regression, Support Vector Machine, Naïve Bayes, and Multilayer Perceptron neural network are used for the machine learning classification task. By applying neural networks as the meta-learner for the classification of NSL-KDD data, improved performances in terms of accuracy, precision, recall and F-measure of 0.97, 0.98, 0.98 and 0.98, respectively are achieved.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
New kind of intrusions causes deviation in the normal behaviour of traffic flow in
computer networks every day. This study focused on enhancing the learning capabilities of IDS
to detect the anomalies present in a network traffic flow by comparing the k-means approach of
data mining for intrusion detection and the outlier detection approach. The k-means approach
uses clustering mechanisms to group the traffic flow data into normal and abnormal clusters.
Outlier detection calculates an outlier score (neighbourhood outlier factor (NOF)) for each flow
record, whose value decides whether a traffic flow is normal or abnormal. These two methods
were then compared in terms of various performance metrics and the amount of computer
resources consumed by them. Overall, k-means was more accurate and precise and has better
classification rate than outlier detection in intrusion detection using traffic flows. This will help
systems administrators in their choice of IDS.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
The purpose of this paper is to describe two objective fuzzy genetics-based learning algorithms
and discusses its usage to detect intrusion in a computer network. Experiments were performed
with KDD-cup data set, which have information on computer networks, during normal behavior
and intrusive behavior. The performance of final fuzzy classification system has been
investigated using intrusion detection problem as a high dimensional classification problem.
This task is formulated as optimization problem with two objectives: To minimize the number of
fuzzy rules and to maximize the classification rate. We show a two-objective genetic algorithm
for finding non-dominated solutions of the fuzzy rule selection problem
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
"In any organization it is an essential task to protect the data from unauthorized users. Information Systems hardware, software, networks, and data resources need to be protected and secured to ensure quality, performance, and integrity. Security management deals with the accuracy, integrity, and safety of information resources. When effective security measures are in place, they can reduce errors, fraud, and losses. In the current work, the authors have proposed a model for encryption of a text phrase employing genetic algorithm. The entropy inherently available in genetic algorithm is exploited for introducing chaos in a text phrase thereby rendering it unreadable. The no of cross over points and mutation points decides the strength of the algorithm. The prototype of the model is implemented for testing the operational feasibility of the model and the few test cases are presented Dr. Poornima G. Naik | Mr. Pandurang M. More | Dr. Girish R. Naik ""A Model for Encryption of a Text Phrase using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23063.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23063/a-model-for-encryption-of-a-text-phrase-using-genetic-algorithm/dr-poornima-g-naik"
A review on privacy preservation in data miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques for masking sensitive information through data modification. The major issues were how to modify the data and how to recover the data mining result from the altered data. The reports were often tightly coupled with the data mining algorithms under consideration. Privacy preserving data publishing focuses on techniques for publishing data, not techniques for data mining. In case, it is expected that standard data mining techniques are applied on the published data. Anonymization of the data is done by hiding the identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data. This survey carries out the various privacy preservation techniques and algorithms.
A Review on Privacy Preservation in Data Miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
Real Time Intrusion Detection System Using Computational Intelligence and Neu...ijtsrd
Today, Intrusion detection system using neural network is interested and measurable area for the researchers. The computational intelligence describe based on following parameters such as computational speed, adaptation, error resilience and fault tolerance. A good intrusion detection system must be satisfied adaptable as requirements. The objective of this paper, provide an outline of the research progress via computational intelligence and neural network over the intrusion detection. In this paper focused, existing research challenges, review analysis, research suggestion regarding Intrusion detection system. Dr. Prabha Shreeraj Nair"Real Time Intrusion Detection System Using Computational Intelligence and Neural Network: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5781.pdf http://www.ijtsrd.com/engineering/computer-engineering/5781/real-time-intrusion-detection-system-using-computational-intelligence-and-neural-network-a-review/dr-prabha-shreeraj-nair
Benchmarks for Evaluating Anomaly Based Intrusion Detection SolutionsIJNSA Journal
Anomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time. There are many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, however there is no standard benchmark to compare them based on quantifiable measures. In this paper, we propose a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. We then use this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering. The benchmark evaluation is performed on the popular NSL-KDD dataset. The experimental results show the differences in accuracy and performance between these Anomaly-based IDS solutions on the dataset. The results also demonstrate how this benchmark can be used to create useful metrics for such comparisons.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGYcscpconf
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
Defects in modules of software systems is a major problem in software development. There are a variety of data mining
techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned
with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural
network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is
that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural
networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use
a range of software defect prediction models to complement each other rather than relying on a single technique.
Incentive Compatible Privacy Preserving Data Analysisrupasri mupparthi
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining.
In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.
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.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
The purpose of this paper is to describe two objective fuzzy genetics-based learning algorithms
and discusses its usage to detect intrusion in a computer network. Experiments were performed
with KDD-cup data set, which have information on computer networks, during normal behavior
and intrusive behavior. The performance of final fuzzy classification system has been
investigated using intrusion detection problem as a high dimensional classification problem.
This task is formulated as optimization problem with two objectives: To minimize the number of
fuzzy rules and to maximize the classification rate. We show a two-objective genetic algorithm
for finding non-dominated solutions of the fuzzy rule selection problem
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
"In any organization it is an essential task to protect the data from unauthorized users. Information Systems hardware, software, networks, and data resources need to be protected and secured to ensure quality, performance, and integrity. Security management deals with the accuracy, integrity, and safety of information resources. When effective security measures are in place, they can reduce errors, fraud, and losses. In the current work, the authors have proposed a model for encryption of a text phrase employing genetic algorithm. The entropy inherently available in genetic algorithm is exploited for introducing chaos in a text phrase thereby rendering it unreadable. The no of cross over points and mutation points decides the strength of the algorithm. The prototype of the model is implemented for testing the operational feasibility of the model and the few test cases are presented Dr. Poornima G. Naik | Mr. Pandurang M. More | Dr. Girish R. Naik ""A Model for Encryption of a Text Phrase using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23063.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23063/a-model-for-encryption-of-a-text-phrase-using-genetic-algorithm/dr-poornima-g-naik"
A review on privacy preservation in data miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques for masking sensitive information through data modification. The major issues were how to modify the data and how to recover the data mining result from the altered data. The reports were often tightly coupled with the data mining algorithms under consideration. Privacy preserving data publishing focuses on techniques for publishing data, not techniques for data mining. In case, it is expected that standard data mining techniques are applied on the published data. Anonymization of the data is done by hiding the identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data. This survey carries out the various privacy preservation techniques and algorithms.
A Review on Privacy Preservation in Data Miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
Real Time Intrusion Detection System Using Computational Intelligence and Neu...ijtsrd
Today, Intrusion detection system using neural network is interested and measurable area for the researchers. The computational intelligence describe based on following parameters such as computational speed, adaptation, error resilience and fault tolerance. A good intrusion detection system must be satisfied adaptable as requirements. The objective of this paper, provide an outline of the research progress via computational intelligence and neural network over the intrusion detection. In this paper focused, existing research challenges, review analysis, research suggestion regarding Intrusion detection system. Dr. Prabha Shreeraj Nair"Real Time Intrusion Detection System Using Computational Intelligence and Neural Network: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5781.pdf http://www.ijtsrd.com/engineering/computer-engineering/5781/real-time-intrusion-detection-system-using-computational-intelligence-and-neural-network-a-review/dr-prabha-shreeraj-nair
Benchmarks for Evaluating Anomaly Based Intrusion Detection SolutionsIJNSA Journal
Anomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time. There are many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, however there is no standard benchmark to compare them based on quantifiable measures. In this paper, we propose a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. We then use this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering. The benchmark evaluation is performed on the popular NSL-KDD dataset. The experimental results show the differences in accuracy and performance between these Anomaly-based IDS solutions on the dataset. The results also demonstrate how this benchmark can be used to create useful metrics for such comparisons.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGYcscpconf
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
Defects in modules of software systems is a major problem in software development. There are a variety of data mining
techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned
with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural
network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is
that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural
networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use
a range of software defect prediction models to complement each other rather than relying on a single technique.
Incentive Compatible Privacy Preserving Data Analysisrupasri mupparthi
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining.
In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.
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.
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.
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Intrusion Detection System used to discover attacks
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Abstract-Intrusion Detection System used to discover attacks against computers and network Infrastructures. There are many techniques used to determine the IDS such as Outlier Detection Schemes for Anomaly Detection, K-Mean Clustering of monitoring data, classification detection and outlier detection. The data mining approaches help to determine what meets the criteria as an intrusion versus normal traffic, whether a system uses anomaly detection, misuse detection, target monitoring, or stealth probes. This paper attempts to evaluate, categorize, compares and summarizes the performance of data mining techniques to detect the intrusion.
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
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important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
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A Novel and Advanced Data Mining Model Based Hybrid Intrusion Detection Frame...Radita Apriana
An Intrusion can be defined as any practice or act that attempt to crack the integrity,
confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access
the information, manipulate the data, or make a system unreliable or unusable. With the expansion of
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A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets
1. A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets 353
A Study on Genetic-Fuzzy Based Automatic
Intrusion Detection on Network Datasets
Jabez1
and Anadha Mala2
1
Sathyabama University, India
2
St. Joseph’s College of Engineering, India
E-mail: 1
jabezme@gmail.com
ABSTRACT: The intrusion detection aims at distinguishing the attack data and the
normal data from the network pattern database. It is an indispensable part of the
information security system. Due to the variety of network data behaviours and the
rapid development of attack fashions, it is necessary to develop a fast machine-
learning-based intrusion detection algorithm with high detection rates and low false-
alarm rates. In this correspondence, we propose a novel fuzzy method with genetic for
detecting intrusion data from the network database. Genetic algorithm is an
evolutionary optimization technique, which uses Directed graph structures instead of
strings in genetic algorithm or trees in genetic programming, which leads to enhancing
the representation ability with a compact programs derived from the reusability of nodes
in a graph structure. By combining fuzzy set theory with Genetic proposes a new
method that can deal with a mixed of database that contains both discrete and
continuous attributes and also extract many important association rules to contribute
and to enhance the Intrusion data detections ability. Therefore, the proposed method
is flexible and can be applied for both misuse and anomaly detection in data-
intrusion-detection problems. Also the incomplete database will include some of the
missing data in some tuples and however, the proposed methods by applying some rules
to extract these tuples. The Genetic-Fuzzy presents a data Intrusion Detection Systems
for recovering data. It also include following steps in Genetic-Fuzzy rules:
• Process data model as a mathematical representation for Normal data.
• Improving the process data model which improves the Model of normal data and
it should represent the underlying truth of normal Data.
• Uses cluster centers or centroids and use distances away from the centroids and
convert the Data to Training Data.
Keywords: Intrusion, Centroids, Tuples.
1. INTRODUCTION
Many kinds of systems over the Internet such as online shopping, Internet banking,
trading stocks and foreign exchange, and online auction have been developed. However,
due to the open society of the Internet, the security of our computer systems and data is
2. 354 ICSEMA–2012
always at risk. The extensive growth of the Internet has prompted data intrusion detection
to become a critical component of infrastructure protection mechanisms. The data
intrusion detection can be defined by identifying a set of malicious actions that threaten
the integrity, confidentiality and availability of data resources.
Normal Intrusion detection is traditionally divided into two categories, i.e., misuse detection
and anomaly detection. Misuse detection mainly searches for specific patterns of data or
sequences of data and user behaviour data which matches and are well-known as intrusion
scenarios. While, the anomaly detection models are developed for normal data and intrusion
data. The intrusion data that are been detected and evaluated significantly from the normal
data by applying various data mining approaches. The advantage of using anomaly based data
intrusion detection is that it mainly detects novel intrusions that have not been observed.
A significant challenge in providing an effective defense mechanism to a network perimeter
is having the ability to detect intrusions and implement counter measures. Components of
the network perimeter defense capable of detecting intrusions are referred to as Intrusion
Detection Systems (IDS).
IDS is further classified as signature–based (also known as misuse system) or anomaly–
based. Signature–based systems attempt to match observed activities against well defined
patterns, also called signatures. Anomaly–based systems look for any evidence of activities
that deviate from what is considered normal system use. These systems are capable of
detecting attacks for which a well–defined pattern does not exist (such as a new attack or a
variation of an existing attack). A hybrid IDS is capable of using signatures and detecting
anomalies.
While accuracy in data is the essential requirement of an Intrusion Detection System (IDS),
its extensibility and adaptability are also critical in today’s data computing environment.
Currently, building of effective IDS is an enormous knowledge engineering task.
Accepted rely on their intuition and experience to select the statistical measures for anomaly
detection. Experts first analyze and categorize attack scenarios and data vulnerabilities, and
hand-code the corresponding rules and patterns for misuse detection. Due to the manual
and ad-hoc nature of the development process, such IDS have limited extensibility and
adaptability.
A basic premise for intrusion detection is that when audit mechanisms are enabled to record
system data events, distinct evidence of egitimate activities and intrusions will be manifested
in the audit data because of the large amount of audit records and the variety of system
features, efficient and intelligent data analysis tools are required to discover the behaviour
of system activities.
KDD99 Cup dataset and the Defence Advanced Research Projects Agency (DARPA)
datasets rovided by MIT Lincoln Laboratory are widely used as training and testing
3. A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets 355
datasets for the evaluation of IDSs. An evolutionary neural network is introduced for
each specific system-call-level to audit data.
Parikh and Chen discussed a classification system using several sets of neural networks
for specific classes and also proposed a technique for cost minimization in the intrusion-
detection problems. Data mining generally refers to the process of extracting useful rules
from large stores of data.
The recent rapid development in data mining contributes to developing wide variety of
algorithms suitable for network-intrusion-detection problems. Intrusion detection can be
thought of as a classification problem: we wish to classify each audit record into one of
discrete sets of possible categories, normal or a particular kind of intrusion. As one of the
most popular data mining methods for wide range of applications, rule mining is used to
discover new rules or correlations among a set of attributes in a dataset. The relationship
between datasets can be represented as rules. An rule is expressed by X _Ë Y, Where X and Y
contain a set of attributes. This means that if a tuple satisfies X, it is also likely to satisfy Y.
The most popular model for mining rules from databases is the a priori algorithm this
algorithm measures the importance of rules with two factors: support and confidence.
However, this algorithm may suffer form large computational complexity for rule extraction
from a dense database.
In order to discover interesting rules from a dense database, genetic algorithm (GA) and
genetic programming (GP) have been applied to rule mining. In the GA, the method
evolves the rules during generations and individuals or population themselves represent
the association relationships. However, it is not easy for GA to extract enough number of
interesting rules, because a rule is represented as an individual of GA.
GP improves the interpretability of GA by replacing the gene structures with the tree
structures, which enables higher representation ability of association rules. As an extended
evolutionary algorithm of GA and GP, genetic network programming that represents its
solutions using directed graph structures has been proposed. Originally, Genetic-Fuzzy is
applied to dynamic problems based on inherent features of the graph structure such as
reusability of nodes like Automatically Defined Functions (ADFs) in GP, a compact structure
without bloat and applicability to partially observable Markov decision process. However, to
extend the applicable fields of Genetic-Fuzzy and rule mining technique using Genetic-
Fuzzy has been developed.
The advantage of rule mining methods is to extract sufficient number of important rules
for user’s purpose rather than to extract all the rules meeting the criteria. Like most of the
existing rule mining algorithms, conventional rule mining based on Genetic-Fuzzy is able
to extract rules with attributes of binary values. However, in real-world applications,
atabases are more likely to be composed of both binary and continuous values.
4. 356 ICSEMA–2012
This paper describes a novel fuzzy rule mining method based on Genetic-Fuzzy and its
application to intrusion data detection. By combining fuzzy set theory with Genetic-Fuzzy,
the proposed method can deal with the mixed database that contains both discrete and
continuous attributes. Such mixed database is normal in real world applications and Genetic-
Fuzzy can extract rules that include both discrete and continuous attributes consistently.
The initiative of combining association rule mining with fuzzy set theory has been
applied more frequently in recent years. The original idea comes from dealing with
quantitative attributes in a database, where discretization of the quantitative attributes into
intervals would lead to under- or overestimate the values that are near the borders. This is
called the sharp boundary problem. Fuzzy sets can help us to overcome this problem by
allowing different degrees of memberships. Compared with traditional association rules
with crisp sets, fuzzy rules provide good linguistic explanation.
2. OVERVIEW OF THE PROPOSED APPROACH
A novel fuzzy rule mining method based on Genetic-Fuzzy and its application to
intrusion d data detection. By combining fuzzy set theory with Genetic, the proposed
method can deal with the mixed database that contains both discrete and continuous
attributes. Such mixed database is normal in real world applications and Genetic-Fuzzy
can extract rules that include both discrete and continuous attributes consistently.
The initiative of combining association rule mining with fuzzy set theory has been applied
more frequently in recent years. The original idea comes from dealing with quantitative
attributes in a database, where discretization of the quantitative attributes into intervals
would lead to under-or overestimate the values that are near the borders. This is called the
sharp boundary problem. Fuzzy sets can help us to overcome this problem by allowing
different degrees of memberships.
Compared with traditional association rules with crisp sets, fuzzy rules provide good
linguistic explanation. Here, the concept of Genetic-Fuzzy rule mining is introduced in detail.
The fuzzy membership values are used for fuzzy rule extraction, and sub attribute-utilization
mechanism is proposed to avoid the information loss. Meanwhile, a new Genetic-Fuzzy
structure for rule mining is built up so as to conduct the rule extraction step.
In addition, a new fitness function that provides the flexibility of mining more new rules
and mining rules with higher accuracy is given in order to adapt to different kinds of
detection. After the extraction of class-association rules, these rules are used for classification.
In this paper, two kinds of classifiers are built up for misuse detection and anomaly detection,
respectively, in order to classify new data correctly.
For misuse detection, the normal-pattern rules and intrusion-pattern rules are extracted from
the training dataset. Classifiers are built up according to these extracted rules. While, for
anomaly detection, we focus on extracting as many normal-pattern rules as possible.
Extracted normal-pattern rules are used to detect novel or unknown intrusions by evaluating
5. A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets 357
the deviation from the normal behaviour. The decision rules are provided for both categorical
and continuous features.
The relations between categorical and continuous features are handled naturally, without any
forced conversions between these two types of features. A simple over fitting handling is used
to improve the learning results. In the specific case of network intrusion detection, we use
adaptable initial weights to make the trade off between the detection and false-alarm rates.
The experiment results show that our algorithm has a very low falsealarm rate with a high
detection rate, and the run speed of our algorithm is faster in the learning stage compared
with the published run speeds of the existing algorithms.
Features of the proposed method are summarized as follows:
1. Genetic-Fuzzy rule mining can deal with both discrete and Continuous attributes in
the database, which is practically useful for real network-related databases.
2. Sub attribute utilization considers all discrete and continuous attribute values as
information, which contributes to avoid data loss and effective rule mining in Genetic-
Fuzzy.
3. The proposed fitness function contributes to mining more new rules with higher
accuracy.
Table 1: Rules Applied in Genetic-Fuzzy
6. 358 ICSEMA–2012
4. The proposed framework for intrusion detection can be flexibly applied to both
misuse and anomaly detection with specific Designed classifiers.
5. Experienced knowledge on intrusion patterns is not required before the training.
6. High Detection Rates (DRs) are obtained in both misuse detection and anomaly
detection.
Thus the Judgment node Transfer the packet to processing node and receive the ACK and
then Apply Genetic-Fuzzy Rules with attributes Matching Probability produce Misuse
Detection. Normal rule pool Probability produce Anomaly Detection and finally it
Calculates the Detection Rate.
3. PROPOSED ALGORITHM
The decision rules are provided for both categorical and continuous features. The relations
between categorical and continuous features are handled naturally, without any forced
conversions between these two types of features. A simple over fitting handling is used to
improve the learning results. In the specific case of network intrusion detection, we use
adaptable initial weights to make the trade-off between the detection and false-alarm
rates. The experiment results show that our algorithm has a very low false-alarm rate with
a high detection rate, and the run speed of our algorithm is faster in the learning stage
compared with the published run speeds of the existing algorithms.
Fig. 1: Structure of the Proposed Approach
7. A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets 359
Step 1: Record the System Calls
• Special programs such as strace
• Collects process ids and system call numbers
• System call numbers are found by their order in call file.
Step 2: Convert the Data to the Training Data
• List of process Ids and system calls are converted to n Length strings
• n is 6, 10, or 14
• Take a sliding window across the data.
Step 3: Build the Process Data Model
• The process data model is a mathematical representation of normal behaviour
• Improving the process data model improves the model of normal behaviour
• It should represent the underlying truth of normalcy of the Data.
Fig. 2: Overall Architecture Flow
Step 4: Compare New Process Data with the Process Data Model
• New process data is converted to a form that can be compared against the process
data model
• Our form is also a set of strings
• This new data is compared and later classified in step 5 as Normal or abnormal
behaviour.
Step 5: Determine an Intrusion
• Hard limits are given to the intrusion signal to determine if new process data is either
a normal or abnormal behaviour
8. 360 ICSEMA–2012
• One and a half times the maximum self test signal is considered a true negative.
Anything less is a false negative
• Genetic network programming apply
• Probability of loss finding
• Intrusion detection.
4. RESULTS AND DISCUSSION
The effectiveness and efficiency of the proposed method are studied using KDD99 Cup
and DARPA98 database. The features of the proposed method are summarized as follows
and they are Genetic-Fuzzy rule mining can deal with both discrete and continuous attributes
in the database, which is practically useful for real network-related databases. Sub attribute
utilization considers all discrete and continuous attribute values as information, which
contributes to avoid data loss and effective rule mining in Genetic-Fuzzy. The proposed
fitness function contributes to mining more new rules with higher accuracy. Also the
proposed framework for intrusion detection can be flexibly applied to both misuse and
anomaly detection with specific designed classifiers. The experienced knowledge on intrusion
patterns discovery are not required before the training. Also it has a high Detection Rates
(DRs) are obtained in both misuse detection and anomaly detection.
Fig. 23
(a) Trained input vs. Attacks
(b) Detection Rate vs. Category
9. A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets 361
Test were conducted which are shown in Figure 3a was taken on the training of different
types of trained input versus Attacks were highlighted. The experiment was conducted again
and again which provided a 100% result. Also the experiment was proved in Figure 3b has
a high detection rate.
5. CONCLUSION
A Genetic-Fuzzy rule mining with sub attribute utilization and the classifiers based on the
extracted rules have been proposed, which can consistently use and combine discrete and
continuous attributes in a rule and efficiently extract many good rules for classification.
As an application, intrusion-detection classifiers for both misuse detection and anomaly
detection have been developed and their effectiveness is confirmed using KDD99 Cup
and DARPA98 data. In the misuse detection show that the proposed method shows high
DR and low PFR, which are two important criteria for security systems. In the anomaly
detection, the results show high DR and reasonable PFR even without pre experienced
knowledge, which is an important advantage of the proposed method. In order to analyze
the proposed method in the intrusion-detection problem in detail, Genetic-Fuzzy data
mining is compared with that with crisp data mining, and the result clarifies the necessity
to introduce fuzzy membership functions into Genetic-Fuzzy based data mining. The
important function of the proposed method is to efficiently extract many rules that are
statistically significant and they can be used for several purposes. The matching of a new
connection with the normal rules and the intrusion rules are calculated, respectively, and
the connection is classified into the normal class or intrusion class. When we use the rules
for anomaly detection, only the rules of the normal connections are used to calculate the
deviation of a new connection from the normal data. Therefore, many rules extracted by
Genetic-Fuzzy cover the spaces of the classes widely.
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