Misuse detection approaches aim to detect known intrusion patterns by encoding them as signatures. Signatures precisely define the patterns of events that characterize an intrusion. This allows misuse detection to be fully effective against known attacks but provides no detection for unknown attacks. Common misuse detection techniques include pattern matching, rule-based systems, and state-based analysis. Pattern matching searches for encoded signatures in audit data, while rule-based systems apply rules to detect intrusion scenarios. State-based systems represent intrusions as state transitions to identify compromised system states.
A Survey On Genetic Algorithm For Intrusion Detection SystemIJARIIE JOURNAL
The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of
business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of
information between various organizations.
Intrusion Detection Systems have become a needful component in terms of computer and network security. Intrusion detection is
one of the important security constraints for maintaining the integrity of information. Intrusion detection systems are the tools
used for prevention and detection of threats to computer systems. Various approaches have been applied in past that are less
effective to curb the menace of intrusion.
In this paper, a survey on applications of genetic algorithms in intrusion detection systems is carried out.
Computer Worms Based on Monitoring Replication and Damage: Experiment and Eva...IOSRjournaljce
This paper presents the experiments of the proposed worm detection system WDS and its evaluation. More specifically, initially there will be an explanation of the various experiment designs and how the experiments will be conducted. The results are presented and an evaluation will take place against a set of predetermined criteria. The experiments involve networking three machines over wireless links and transferring files between them which may contain worms in order to test the W DS. The three machines are Host 1, Host 2 (Dummy Host) and Host 3. The evaluation of the system showed that all evaluation criteria were successfully met
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Intrusion detection system based on web usage miningIJCSEA Journal
This artical present a system developed to find cyber threats automatically based on web usage mining
methods in application layer. This system is an off-line intrusion detection system which includes different
part to detect attacks and as a result helps find different kinds of attacks with different dispersals. In this
study web server access logs used as the input data and after pre-processing, scanners and all identified
attacks will be detected. As the next step, vectors feature from web access logs and parameters sent by
HTTP will derived by three different means and at the end by employment of two clustering algorithms
based on K-Means, anomaly behaviour of data are detached. Tentative results derived from this system
represent that used methods are more applicable than similar systems because this system covers different
kinds of attacks and mostly increase the accuracy and decrease false alarms.
A Survey On Genetic Algorithm For Intrusion Detection SystemIJARIIE JOURNAL
The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of
business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of
information between various organizations.
Intrusion Detection Systems have become a needful component in terms of computer and network security. Intrusion detection is
one of the important security constraints for maintaining the integrity of information. Intrusion detection systems are the tools
used for prevention and detection of threats to computer systems. Various approaches have been applied in past that are less
effective to curb the menace of intrusion.
In this paper, a survey on applications of genetic algorithms in intrusion detection systems is carried out.
Computer Worms Based on Monitoring Replication and Damage: Experiment and Eva...IOSRjournaljce
This paper presents the experiments of the proposed worm detection system WDS and its evaluation. More specifically, initially there will be an explanation of the various experiment designs and how the experiments will be conducted. The results are presented and an evaluation will take place against a set of predetermined criteria. The experiments involve networking three machines over wireless links and transferring files between them which may contain worms in order to test the W DS. The three machines are Host 1, Host 2 (Dummy Host) and Host 3. The evaluation of the system showed that all evaluation criteria were successfully met
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Intrusion detection system based on web usage miningIJCSEA Journal
This artical present a system developed to find cyber threats automatically based on web usage mining
methods in application layer. This system is an off-line intrusion detection system which includes different
part to detect attacks and as a result helps find different kinds of attacks with different dispersals. In this
study web server access logs used as the input data and after pre-processing, scanners and all identified
attacks will be detected. As the next step, vectors feature from web access logs and parameters sent by
HTTP will derived by three different means and at the end by employment of two clustering algorithms
based on K-Means, anomaly behaviour of data are detached. Tentative results derived from this system
represent that used methods are more applicable than similar systems because this system covers different
kinds of attacks and mostly increase the accuracy and decrease false alarms.
A web application detecting dos attack using mca and tameSAT Journals
Abstract
Interconnected systems, such as all kind of servers including web servers, are been always under the threats of network attackers. There are many popular attacks like man in middle attack, cross site scripting, spamming etc. but Denial of service attack is considered to be one of most dangerous attack on the networked applications. The attack causes many serious issues on these computing systems A denial-of-service (DoS) attack is an attempt to make a machine or network resource unavailable to the intended users. The performance of the server is reduced by the DoS attack, so, to increase the efficiency of the server, detection of the attack is necessary. Hence Multivariate Correlation Analysis’ issued, this approach employs triangle area for extracting the correlation information between network traffic. Our implemented system is evaluated using KDD Cup 99 data set, and the treatment of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The implemented system has capability of learning new patterns of legitimate network traffic hence it detect both known and unknown types of DoS attacks and we can say that It is working on the principle of anomaly based attack detection. Triangle-area-based technique is used to speed up the process. The stored legitimate profiles has to keep secured so Detection e=mechanism for the SQL injection is also implemented in the system. The system designed to carry out attack detection is a question-answer portal i.e. a web application and hence the system is using HTTP protocol unlike previous systems which were using TCP. Keywords: Denial-of-Service attack, Features Normalization, Triangle Area Map(TAM), Multivariate Correlation Analysis(MCA), anomaly based detection, SQL injection, HTTP, and TCP,
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
Self Evolving Antivirus Based on Neuro-Fuzzy Inference SystemIJRES Journal
With today’s world filled with information and data, it is very important for one to know which information or data is harmless and which is harmful. Right from cellular phones to big MNCs and Server companies require a security system that is as competent and adaptive as its ever-updating and evolving viruses or malware. The paper talks about the development and implementation of a new idea Adaptive anti-virus based on Anfis logic. An adaptive anti-virus system that will catch up to the speed at which the viruses update and evolve.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
INTRUSION DETECTION SYSTEM CLASSIFICATION USING DIFFERENT MACHINE LEARNING AL...ijcsit
Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.
Novel Malware Clustering System Based on Kernel Data Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Architecture for Intrusion Detection System with Fault Tolerance Using Mobile...IJNSA Journal
This paper is a survey of the work, done for making an IDS fault tolerant.Architecture of IDS that uses mobile Agent provides higher scalability. Mobile Agent uses Platform for detecting Intrusions using filter Agent, co-relater agent, Interpreter agent and rule database. When server (IDS Monitor) goes down, other hosts based on priority takes Ownership. This architecture uses decentralized collection and analysis for identifying Intrusion. Rule sets are fed based on user-behaviour or applicationbehaviour.This paper suggests that intrusion detection system (IDS) must be fault tolerant; otherwise, the intruder may first subvert the IDS then attack the target system at will.
Vulnerability scanners a proactive approach to assess web application securityijcsa
With the increasing concern for security in the network, many approaches are laid out that try to protect
the network from unauthorised access. New methods have been adopted in order to find the potential
discrepancies that may damage the network. Most commonly used approach is the vulnerability
assessment. By vulnerability, we mean, the potential flaws in the system that make it prone to the attack.
Assessment of these system vulnerabilities provide a means to identify and develop new strategies so as to
protect the system from the risk of being damaged. This paper focuses on the usage of various vulnerability
scanners and their related methodology to detect the various vulnerabilities available in the web
applications or the remote host across the network and tries to identify new mechanisms that can be
deployed to secure the network.
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.
Synthesis of Polyurethane Solution (Castor oil based polyol for polyurethane)IJARIIE JOURNAL
Around 160 million hector unused is available in India. India is the world’s largest producer of castor oil,
producing over 75% of the total world’s supply. There are over a hundred companies in India-small and
medium-that are into castor oil production, producing a variety of the basic grades o castor oil. All the above
factors make it imperative that the India industry relooks at the castor oil sector in order to devise suitable
strategies to derive the most benefits from such an attractive confluence of factors. Castor oil is unique owing to
its exceptional diversity of application. The oil and its derivatives are used in over 100 different applications in
diverse industries such as paints, lubricants, pharma, cosmetics, paper, rubber and more. Recent developments
have successfully derived polyol from natural oils and synthesized range of PU product from them. However,
making flexible solution from natural oil polyol is still proving challenging. The goal of this thesis is to
understand the potentials and the limitations of natural oil as an alternative to petroleum polyol. An initial
attempt to understand natural oil polyol showed that flexible solution could be synthesized from castor oil,
which produced a rigid solution. Characterization results indicate that the glass transition temperature (Tg) was
the predominant factor that determines the rigidity of the solution. The high Tg of solution was attributed to the
low number of covalent bond between cross linkers.
A web application detecting dos attack using mca and tameSAT Journals
Abstract
Interconnected systems, such as all kind of servers including web servers, are been always under the threats of network attackers. There are many popular attacks like man in middle attack, cross site scripting, spamming etc. but Denial of service attack is considered to be one of most dangerous attack on the networked applications. The attack causes many serious issues on these computing systems A denial-of-service (DoS) attack is an attempt to make a machine or network resource unavailable to the intended users. The performance of the server is reduced by the DoS attack, so, to increase the efficiency of the server, detection of the attack is necessary. Hence Multivariate Correlation Analysis’ issued, this approach employs triangle area for extracting the correlation information between network traffic. Our implemented system is evaluated using KDD Cup 99 data set, and the treatment of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The implemented system has capability of learning new patterns of legitimate network traffic hence it detect both known and unknown types of DoS attacks and we can say that It is working on the principle of anomaly based attack detection. Triangle-area-based technique is used to speed up the process. The stored legitimate profiles has to keep secured so Detection e=mechanism for the SQL injection is also implemented in the system. The system designed to carry out attack detection is a question-answer portal i.e. a web application and hence the system is using HTTP protocol unlike previous systems which were using TCP. Keywords: Denial-of-Service attack, Features Normalization, Triangle Area Map(TAM), Multivariate Correlation Analysis(MCA), anomaly based detection, SQL injection, HTTP, and TCP,
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
Self Evolving Antivirus Based on Neuro-Fuzzy Inference SystemIJRES Journal
With today’s world filled with information and data, it is very important for one to know which information or data is harmless and which is harmful. Right from cellular phones to big MNCs and Server companies require a security system that is as competent and adaptive as its ever-updating and evolving viruses or malware. The paper talks about the development and implementation of a new idea Adaptive anti-virus based on Anfis logic. An adaptive anti-virus system that will catch up to the speed at which the viruses update and evolve.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
INTRUSION DETECTION SYSTEM CLASSIFICATION USING DIFFERENT MACHINE LEARNING AL...ijcsit
Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.
Novel Malware Clustering System Based on Kernel Data Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Architecture for Intrusion Detection System with Fault Tolerance Using Mobile...IJNSA Journal
This paper is a survey of the work, done for making an IDS fault tolerant.Architecture of IDS that uses mobile Agent provides higher scalability. Mobile Agent uses Platform for detecting Intrusions using filter Agent, co-relater agent, Interpreter agent and rule database. When server (IDS Monitor) goes down, other hosts based on priority takes Ownership. This architecture uses decentralized collection and analysis for identifying Intrusion. Rule sets are fed based on user-behaviour or applicationbehaviour.This paper suggests that intrusion detection system (IDS) must be fault tolerant; otherwise, the intruder may first subvert the IDS then attack the target system at will.
Vulnerability scanners a proactive approach to assess web application securityijcsa
With the increasing concern for security in the network, many approaches are laid out that try to protect
the network from unauthorised access. New methods have been adopted in order to find the potential
discrepancies that may damage the network. Most commonly used approach is the vulnerability
assessment. By vulnerability, we mean, the potential flaws in the system that make it prone to the attack.
Assessment of these system vulnerabilities provide a means to identify and develop new strategies so as to
protect the system from the risk of being damaged. This paper focuses on the usage of various vulnerability
scanners and their related methodology to detect the various vulnerabilities available in the web
applications or the remote host across the network and tries to identify new mechanisms that can be
deployed to secure the network.
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.
Synthesis of Polyurethane Solution (Castor oil based polyol for polyurethane)IJARIIE JOURNAL
Around 160 million hector unused is available in India. India is the world’s largest producer of castor oil,
producing over 75% of the total world’s supply. There are over a hundred companies in India-small and
medium-that are into castor oil production, producing a variety of the basic grades o castor oil. All the above
factors make it imperative that the India industry relooks at the castor oil sector in order to devise suitable
strategies to derive the most benefits from such an attractive confluence of factors. Castor oil is unique owing to
its exceptional diversity of application. The oil and its derivatives are used in over 100 different applications in
diverse industries such as paints, lubricants, pharma, cosmetics, paper, rubber and more. Recent developments
have successfully derived polyol from natural oils and synthesized range of PU product from them. However,
making flexible solution from natural oil polyol is still proving challenging. The goal of this thesis is to
understand the potentials and the limitations of natural oil as an alternative to petroleum polyol. An initial
attempt to understand natural oil polyol showed that flexible solution could be synthesized from castor oil,
which produced a rigid solution. Characterization results indicate that the glass transition temperature (Tg) was
the predominant factor that determines the rigidity of the solution. The high Tg of solution was attributed to the
low number of covalent bond between cross linkers.
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.
An intrusion detection system plays a major role in network security. We
propose a model “DB-OLS: An Approach for IDS” which is a Deviation Based-Outlier
approach for Intrusion detection using Self Organizing Maps. In this model “Self
Organizing Map” approach is to be used for behavior learning and “Outlier mining”
approach, for detecting an intruder by calculating deviation from known user profile.
This model aims to improve the capability of detecting intruders.
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
Review of Intrusion and Anomaly Detection Techniques IJMER
Intrusion detection is the act of detecting actions that attempt to compromise the
confidentiality, integrity or availability of a resource. With the tremendous growth of network-based
services and sensitive information on networks, network security is getting more and more importance
than ever. Intrusion poses a serious security threat in a huge network environment. The increasing use of
internet has dramatically added to the growing number of threats that inhabit within it. Intrusion
detection does not, in general, include prevention of intrusions. Now a days Network intrusion detection
systems have become a standard component in the area of security infrastructure. This review paper tries
to discusses various techniques which are already being used for intrusion detection.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal1
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
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.
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security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring
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review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
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Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
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Cyber risk predictions
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Ids 013 detection approaches
1. Detection Approaches: Misuse Detection
The basic principle of intrusion detection is based on the assumption that intrusive
activities are noticeably different from normal ones and thus are detectable.
Anomaly based intrusion detection approaches
Establishing a model of the data flow that is monitored under normal conditions
without the presence of any intrusive procedures
Misuse detection approaches
Aim to encode knowledge about patterns in the data flow that are known to
correspond to intrusive procedures in form of specific signatures
Specification based detection approaches
Security experts predefine the allowed system behaviors and thus events that do
not match the specifications are labeled as attacks
Detection Approaches
Misuse Detection Anomaly Detection Specification-based
Detection
2. Misuse Detection
Intrusions are detected by matching actual behavior recorded in audit trails with
known suspicious patterns.
Misuse detection is fully effective in uncovering known attacks
Limitations
It is useless when faced with unknown or novel forms of attacks for which the
signatures are not yet available.
For known attacks, defining a signature that encompasses all possible variations of
the attack is difficult.
Any mistakes in the definition of these signatures will increase the false alarm rate
and decrease the effectiveness of the detection technique.
A typical misuse detection model
The model consists of four components:
1. Data collection
2. Systemprofile
3. Misuse detectionand
4. Response
3. Data are collected from one or many data sources including audit trails, network
traffic, system call trace, etc.
Collected data are transferred into a format is used to characterize normal and
abnormal behaviors.
The profiles characterize what a normal subject behavior should be and what
operations the subjects typically would perform or do not perform on the objects.
The profiles are matched with actual system activities and reported as intrusions in
case of deviations.
Techniques commonly used to implement Misuse Detection
Pattern Matching
Commonly used in the network based intrusion detection systems
Attack patterns are modeled, matched and identified based on the packet
head, packet content or both
In host-based intrusion detection systems, the system calls in a system audit
trail is used for misuse detection
Misuse Detection Techniques
Pattern Matching
Rule-based
Techniques
Techniques based on
Data Mining
State-based
Techniques
4. With the continual emerging of new types and varied forms of attacks the
number of signatures is constantly growing, thus making the pattern
matching more expensive in terms of the computational cost.
In order to address this limitation, Abbes et al (2004) propose a method
combining a novel protocol analysis approach with traditional pattern
matching to improve the performance of pattern matching
The protocol analysis checks patterns in specific parts of the packet rather
than in the entire payload and it is implemented based on the construction of
a decision tree.
Adv: The biggest advantage of this approach is that it can reduce the search
space for patterns that results in a fast search.
Adv: The approach has the potential to reduce the number of false positives
since patterns are matched only in the extracted protocol fields.
Kreibich and Crowcroft (2004) studied the performance evaluation of
pattern matching based intrusion detection approaches
A workload model is proposed to provide reasonably accurate estimates
compared to real workloads
The model attempts to emulate a traffic mix of different applications,
reflecting the characteristics of each application and the way these
applications interact with the system.
The model has been implemented as part of a traffic generator
Rule-based Techniques
Rule-based expert system is one of the earliest techniques used
5. Expert systems encode intrusive scenarios as a set of rules, which are
matched against audit or network traffic data
Any deviation in the rule matching process is reported as an intrusion.
MIDAS (Multics Intrusion Detectionand Alerting System)
Developed by the National Computer Security Center (NCSC) to monitor
intrusions for NCSC’s networked mainframe
It uses and analyzes audit log data by combining the expert system
technology with statistical analysis
MIDAS uses the Production Based Expert System Toolset (PBEST) in
discriminating and implementing the rule base, which is written in LISP
language
The structure of the rules in the P-BEST rule base includes two layers.
Rule-based Techniques
MIDAS IDES NIDES
6. The first(lower) layer is used to match certain types of events such as
number of user logins, and then fires new events by setting up a particular
threshold of suspicion.
Rules in the second (higher) layer process these suspicions and decide
whether the system should raise an alert or not.
Figure 2.2 illustrates an example of MIDAS rule. The rule defines an
intrusion scenario involving some unusual login time. It determines whether
the time when the user logins is outside normal hours or not. The rule also
illustrates that an unusual behavior does not necessarily stands for an
intrusion.
(Lower Layer): Check Events
(
(Higher Layer): Process Events
Alarm
Generator
Event Listener
Rules
7. IDES (Intrusion DetectionExpert System)
The IDES model based on the assumption that normal interactions between
subjects (e.g. users) and objects (e.g. files, programs, or devices) can be
characterized
users always behave in a consistent manner when they perform operations
on the computer system
These usages can be characterized by computing various statistics and
correlated with established profiles of normal behaviors
New audit records are verified by matching known profiles for both subjects
and their correspondinggroups.
Deviations are then flagged as intrusions
To improve the detection rate, IDES monitors the subject depending on
whether the activity happens on an on or off day since user activities on
different day types are usually different.
For example, activities for normal users on a working day may be abnormal
on an off-working day.
IDES uses P-BEST to describe its rule base
8. IDES consisting of two types of rules: generic rules and specific rules.
Generic rules can be used for different target systems
Specific rules are strictly dependent on the operating system and the
corresponding implementation.
IDES architecture consists of three main components, namely audit
database, profiles database and the system security officer (SSO) user
interface.
Audit Database
User
Profile Database
User 1 User 2 User 3
System
Security
Officer
(SSO)
User
Interface
Administrator
9. NIDES (Next-generationIntrusion DetectionExpert System)
NIDES is a hybrid intrusion detection system consisting of a signature-based
expert system component as well as a detection component based on
statistical approaches.
The expert system improves the old IDES version by encoding more known
intrusion scenarios and updating the P-BEST version used.
Detection component based on statistical approaches is based on anomaly
detection.
In these approaches over 30 criteria are used to establish normal user
profiles including CPU or I/O usage, command used, local network activity,
system errors, etc.
The NIDES system is highly modularized with well-defined interfaces
between components.
Compared with the IDES, NIDES has higher detection rate since it includes
two complementary detection components: intrusions missed by one
component maybe caught by the other one.
Limitationsof Rule-based Techniques
1. Using rule-based techniques for misuse detection, quite often the burden of
extending the rule-base.
2. Developing intrusion scenarios is not an easy task and requires a certain level of
expertise and security insight and awareness.
3. Determining the relations between rules is difficult.
4. When many related rules are included in an expert system, correctness of rules
is difficult to verify due to the interactions among these rules.
5. Most of the rule bases are outdated and quickly become obsolete.
10. State-based Techniques
State-based techniques detect known intrusions by using expressions of the
system state and state transitions
In state-based techniques, activities contributing to intrusion scenarios are
defined as transitions between system states, and thus intrusion scenarios are
defined in the form of state transition diagrams.
The state of the system is a function of users or processes. Intrusion
scenarios defined by the state transition diagram include three types of
states, namely initial state, transition state and compromised state.
An initial state refers to the beginning of the attack, while a compromised
state stands for the successful completion of the attack.
Transition states correspond to the successive states occurring between an
initial state and a compromised state.
An intrusion occurs if and only if a compromised state is finally reached.
11. UNIX State Transition AnalysisTool (USTAT)
USTAT is a host-based intrusion detection based on the State Transition
Analysis Technique.
USTAT) is based on the assumption that all attackers start from an initial
state where they possess limited authorization to access a target system, and
then after completing some operations on the target system, they acquire
some previously unauthorized capabilities.
USTAT is a prototype implementation of the state transition analysis
technique for intrusion detection
It monitors the system state transition from safe to unsafe by representing all
known vulnerabilities or intrusion scenarios in the form of a state transition
diagram.
In USTAT, over 200 audit events are represented by ten USTAT actions,
such as read(file var), modify owner(file var), where the parameter file var
stands for the name of certain files.
Known attacks are modeled as a sequence of state transitions that lead from
an initial limited authorization state to a final compromised state.
An inference engine in USTAT maintains a state transition table and
determines whether the current action will cause a state transition to its
successor state by matching the next state with the state transition table.
State Transition Based Techniques
USTAT Colored Petri-nets
12. Once the next new state is matched to the final state of the transition table,
the intrusion alarm is raised.
Some other members of STAT family
o NetSTAT is designed for real-time state-transition analysis of
network data.
o WebSTAT and logSTAT are two other STAT family members that
operate at the application level.
o WebSTAT parses the logs produced by Apache web servers, and
logSTAT uses UNIX syslog files as input.
Colored Petri-nets
IDIOT, the acronym of “Intrusion Detection In Our Time”, is a state-based
misuse detection system which uses pattern matching techniques based on
the colored petrinets (CPN) model.
Intrusion scenarios are encoded into patterns in IDIOT, and incoming events
are verified by matching them against these patterns.
In the CPN model used in the implementation of IDIOT, a guard represents
an intrusive signature context and the vertices represent system states. The
selected CPN model is referred to as colored petri automata (CPA). The
CPA defines a strict declarative specification of intrusions and specifies
what patterns need to be matched instead of how to match them.
A simple example of CPA describing the following intrusion scenario: if the
number of unsuccessful login attempts exceeds four within one minute
report an intrusion.
The combination of arrows and vertical bar stands for a transition between
system states.
For example, the transition from states S1 to S2 occurs when there is a
token in S1; this stands for an unsuccessful login attempt. The time of first
unsuccessful login attempt is saved in the token variable T1. The transition
13. from S4 to S5 happens if there is a token in S4. The time difference between
this and the first unsuccessful login attempt should be more than one minute,
otherwise the system state is transferred to the final state S5, in which an
alarm will be generated.
Advantages
1. First, since the intrusion signatures are written in a system independent
script, they can be exchanged across different operating systems and
different audit logs.
2. Second, the IDIOT system achieves an excellent real-time performance;
only 5-6% CPU overhead was reported when it scanned for 100 different
patterns.
3. Third, multiple event sessions can be processed independently and then
corresponding detection results are analyzed together to make the final
decision.
Limitation
It can only detect known vulnerabilities.
Translating known intrusions into patterns is not always easy.
Sophisticated attackers can easily bypass the detection system by changing
their attack strategies.
14. Techniques based on Data Mining
intrusion detection is considered as a data analysis process, in which data mining
techniques are used to automatically discover and model features of user’s normal
or intrusive behaviors.
The three types of algorithms are particularly useful for mining audit data, namely-
1. Classification
Classification algorithms such as decision tree generate classifiers by
learning based on a sufficient amount of normal or abnormal audit data. New
audit data are labeled as either normal or abnormal according to the
classifier.
2. Link analysis
Link analysis determines the relation between fields in the audit database
records and normal profiles are usually derived from these relations.
3. Sequence analysis
Sequence analysis is used to find sequential patterns in audit data and embed
these patterns into intrusion detection models.