The document discusses detecting unknown insider threat scenarios. It proposes an ensemble-based, unsupervised technique to robustly detect potential insider threats, including scenarios not previously identified. The approach uses a variety of individual detectors combined using anomaly detection ensemble techniques. It explores factors like the number and variety of detectors, and incorporating existing knowledge from scenario-based detectors. The technique is evaluated on its ability to detect unknown scenarios in real data. Several new insider threat scenarios and solutions are presented, such as wearable technologies, outsourced systems, knowing detection methods, and activity outside work.
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.
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 %.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
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
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.
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 %.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
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
A Survey: Data Leakage Detection Techniques IJECEIAES
Data is an important property of various organizations and it is intellectual property of organization. Every organization includes sensitive data as customer information, financial data, data of patient, personal credit card data and other information based on the kinds of management, institute or industry. For the areas like this, leakage of information is the crucial problem that the organization has to face, that poses high cost if information leakage is done. All the more definitely, information leakage is characterize as the intentional exposure of individual or any sort of information to unapproved outsiders. When the important information is goes to unapproved hands or moves towards unauthorized destination. This will prompts the direct and indirect loss of particular industry in terms of cost and time. The information leakage is outcomes in vulnerability or its modification. So information can be protected by the outsider leakages. To solve this issue there must be an efficient and effective system to avoid and protect authorized information. From not so long many methods have been implemented to solve same type of problems that are analyzed here in this survey. This paper analyzes little latest techniques and proposed novel Sampling algorithm based data leakage detection techniques.
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
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.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Software reusabilitydevelopment through NFL approach For identifying security...IJECEIAES
In component based software reusability development process, the software developers have to choose the best components which are self adaptive future to overcome the functional errors, framework mismatches, violation of user level privacy issues and data leakage feasibilities. The software developers can build high quality software applications by taking the consideration of the reusable components which are more suitable to provide high level data security and privacy. This paper has proposing the neural based fuzzy framework based approach to estimate the reusable components which are directly and indirectly involve the security and privacy to improve the quality of the software system. This approach has considered the twenty effecting factors and fifty three attribute matrices. It has formed with three stages of execution scenarios. The first stage has executed with eleven effecting factors and eighteen attribute matrices for identification of supporting software reusability components, the second stage has executed with four effecting factors and thirty five attribute matrices for identification of subinternal relationships in terms of security-privacy, and the third stage has executed with eight effecting factors and six attribute matrices for identification of sub of sub-internal relationships in terms of security risk estimation. This analytical finding proposes a fuzzy logic model to evaluate the most feasible effecting factors that influence the enterprise level data security-privacy practices at real time environment.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
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.
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.
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.
A Survey: Data Leakage Detection Techniques IJECEIAES
Data is an important property of various organizations and it is intellectual property of organization. Every organization includes sensitive data as customer information, financial data, data of patient, personal credit card data and other information based on the kinds of management, institute or industry. For the areas like this, leakage of information is the crucial problem that the organization has to face, that poses high cost if information leakage is done. All the more definitely, information leakage is characterize as the intentional exposure of individual or any sort of information to unapproved outsiders. When the important information is goes to unapproved hands or moves towards unauthorized destination. This will prompts the direct and indirect loss of particular industry in terms of cost and time. The information leakage is outcomes in vulnerability or its modification. So information can be protected by the outsider leakages. To solve this issue there must be an efficient and effective system to avoid and protect authorized information. From not so long many methods have been implemented to solve same type of problems that are analyzed here in this survey. This paper analyzes little latest techniques and proposed novel Sampling algorithm based data leakage detection techniques.
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
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.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Software reusabilitydevelopment through NFL approach For identifying security...IJECEIAES
In component based software reusability development process, the software developers have to choose the best components which are self adaptive future to overcome the functional errors, framework mismatches, violation of user level privacy issues and data leakage feasibilities. The software developers can build high quality software applications by taking the consideration of the reusable components which are more suitable to provide high level data security and privacy. This paper has proposing the neural based fuzzy framework based approach to estimate the reusable components which are directly and indirectly involve the security and privacy to improve the quality of the software system. This approach has considered the twenty effecting factors and fifty three attribute matrices. It has formed with three stages of execution scenarios. The first stage has executed with eleven effecting factors and eighteen attribute matrices for identification of supporting software reusability components, the second stage has executed with four effecting factors and thirty five attribute matrices for identification of subinternal relationships in terms of security-privacy, and the third stage has executed with eight effecting factors and six attribute matrices for identification of sub of sub-internal relationships in terms of security risk estimation. This analytical finding proposes a fuzzy logic model to evaluate the most feasible effecting factors that influence the enterprise level data security-privacy practices at real time environment.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
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.
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.
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.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
Anomaly Threat Detection System using User and Role-Based Profile Assessmentijtsrd
In network security the organizations are ever-growing to identify insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. We describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the users behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst. U. Indumathy | M. Nivedha | Mrs. K. Alice"Anomaly Threat Detection System using User and Role-Based Profile Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd10956.pdf http://www.ijtsrd.com/engineering/computer-engineering/10956/anomaly-threat-detection-system-using-user-and-role-based-profile-assessment/u-indumathy
AN ISP BASED NOTIFICATION AND DETECTION SYSTEM TO MAXIMIZE EFFICIENCY OF CLIE...IJNSA Journal
End users are increasingly vulnerable to attacks directed at web browsers which make the most of popularity of today’s web services. While organizations deploy several layers of security to protect their systems and data against unauthorised access, surveys reveal that a large fraction of end users do not utilize and/or are not familiar with any security tools. End users’ hesitation and unfamiliarity with security products contribute vastly to the number of online DDoS attacks, malware and Spam distribution. This work on progress paper proposes a design focused on the notion of increased participation of internet service providers in protecting end users. The proposed design takes advantage of three different detection tools to identify the maliciousness of a website content and alerts users through utilising Internet Content Adaptation Protocol (ICAP) by an In-Browser cross-platform messaging system. The system also incorporates the users’ online behaviour analysis to minimize the scanning intervals of malicious websites database by client honeypots. Findings from our proof of concept design and other research indicate that such a design can provide a reliable hybrid detection mechanism while introducing low delay time into user browsing experience.
Optimised Malware Detection in Digital Forensics IJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and problems on any systems are caused by malware and spam. Networks and systems can be accessed and compromised by malware known as botnets, which compromise other systems through a coordinated attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems from the malicious activity of this malware, a new framework is required that aims to develop an optimised technique for malware detection. Hence, this paper demonstrates new approaches to perform malware analysis in forensic investigations and discusses how such a framework may be developed.
An Improved Method for Preventing Data Leakage in an OrganizationIJERA Editor
Data is one of the most important assets an organisation has since it denes each organisations unique- ness.It
includes data on members and prospects, their inter- ests and purchases, your events, speakers, your content,
social media, press, your staff, budget, strategic plan, and much more. As organizations open their doors to
employees, part- ners, customers and suppliers to provide deeper access to sensitive information, the risk
sassociated with business increase. Now, more than ever, within creasing threats of cyber terrorism, cor- porate
governance issues, fraud, and identity theft, the need for securing corporate information has become paramount.
Informa- tion theft is not just about external hackers and unauthorized external users stealing your data, it is also
about managing internal employees and even contractors who may be working within your organization for
short periods of time. Adding to the challenge of securing information is the increasing push for corporate
governance and adherence to legislative or regulatory requirements. Failure to comply and provide privacy,
audit and internal controls could result in penalties ranging from large nes to jail terms. Non-compliance can
result in not only potential implications for executives, but also possible threats to the viability of a corporation.
Insiders too represent a sign cant risk to data security. The task of detecting malicious insiders is very
challenging as the methods of deception become more and more sophisticated. There are various solutions
present to avoid data leakage. Data leakage detection, prevention (DLPM) and monitoring solutions became an
inherent component of the organizations security suite.DLP solutions monitors sensitive data when at rest, in
motion, or in use and enforce the organizational data protection policy.These solutions focus mainly on the data
and its sensitivity level, and on preventing it from reaching an unauthorized person. They ignore the fact that an
insider is gradually exposed to more and more sensitive data,to which she is authorized to access. Such data
may cause great damage to the organization when leaked or misused. Data can be leaked via emails, instant
messaging, le transfer etc. This research is focusing on email data leakage monitoring, detection and
prevention. It is proposed to be carried out in two phases: leakage detection through mining and prevention
through encryption of email content.
Supervised Machine Learning Algorithms for Intrusion Detection.pptxssuserf3a100
Intrusion detection systems using supervised machine learning algorithms are considered one of the most important tools used in the field of information security. These systems analyze data and detect illegal activities and intrusions into networks and systems. These systems rely on machine learning techniques to classify data as either normal activity or a hack. These systems include training and testing phases, where the algorithms are trained on a set of pre-labeled data to learn the natural pattern of the data and distinguish between normal activities and intrusions. Many supervisory machine learning algorithms are available for intrusion detection systems, such as Gaussian Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression.
The problem of security and electronic breaches targeting networks is one of the biggest problems facing organizations today. To solve this problem, intrusion detection systems (IDS) and their tools can be used to detect and prevent these threats. This file provides an introductory overview of this problem
Security breaches
networks
Infiltration (IDS)
Algorithms
Machine learning
A Behavior Based Intrusion Detection System Using Machine Learning AlgorithmsCSCJournals
Humans are consistently referred to as the weakest link in information security. Human factors such as individual differences, cognitive abilities and personality traits can impact on behavior and play a significant role in information security. The purpose of this study is to identify, describe and classify the human factors affecting Information Security and develop a model to reduce the risk of insider misuse and assess the use and performance of the best-suited artificial intelligence techniques in detection of misuse. More specifically, this study provides a comprehensive view of the human related information security risks and threats, classification study of the human related threats in information security, a methodology developed to reduce the risk of human related threats by detecting insider misuse by a behavior-based intrusion detection system using machine learning algorithms, and the comparison of the numerical experiments for analysis of this approach. Specifically, by using the machine learning algorithm with the best learning performance, the detection rates of the attack types defined in the organized five dimensional human threats taxonomy were determined. Lastly, the possible human factors affecting information security as linked to the detection rates were sorted upon the evaluation of the taxonomy.
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.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paper
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging
endlessly. So it is critical to protect the networks from attackers and the Intrusion detection
technology becomes popular. Therefore, it is necessary that this security concern must be articulate
right from the beginning of the network design and deployment. The intrusion detection technology is the
process of identifying network activity that can lead to a compromise of security policy. Lot of work has
been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a
novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and
manage misuse and anomaly detects
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
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Detecting Unknown Insider Threat Scenarios
1. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
DOI:10.5121/ijcsa.2016.6602 15
DETECTING UNKNOWN INSIDER THREAT
SCENARIOS
Manvendra Singh Lodhi and Rahul Kaul
Department of Computer Science Engineering BMCT Indore, Madhya Pradesh,
India
ABSTRACT
Problems from the inside of an organization’s perimeters are a significant threat, since it is very difficult to
differentiate them from outside activity. In this dissertation, evaluate an insider threat detection motto on
its ability to detect different type of scenarios that have not previously been identify or contemplated by the
developers of the system. We show the ability to detect a large variety of insider threat scenario instances
We report results of an ensemble-based, unsupervised technique for detecting potential insider threat,
insider threat scenarios that robustly achieves results. We explore factors that contribute to the success of
the ensemble method, such as the number and variety of unsupervised detectors and the use of existing
knowledge encoded in scenario based detectors made for different known activity patterns. We report
results over the entire period of the ensemble approach and of ablation experiments that remove the
scenario-baseddetectors.
KEYWORDS
Insider, Insider Threat, Scenario, Suspect, Unauthorized Device.
1. INTRODUCTION
WHAT IS AN “INSIDER”?
There exist many different definitions of the terms “insider” and “insider threat”. One common
definition is that “an insider is defined as an individual with privileged access to an electronic
system”. Second is, on the surface, this definition seems satisfactory. When machine access was
limited and the tasks performed on electronic systems were well defined the term “privileged
access to an electronic system” had a common and well-delineated meaning. Two developments
in recent years have served to confound this picture. One is the now ubiquitous networked
computing environment. The other is the increasingly dynamic and porous, if not ill-defined,
boundary between the inside of the organization and the outside (consider the range of joint
ventures, outsourcing arrangements, consultants and temporary workers in the business world
today, for instance).
WHAT IS AN “INSIDER THREAT”?
A definition of what an insider threat is obviously depends heavily on the definition of what an
insider is. If “an insider is a person that has been legitimately empowered with the right to access
organization assets, representation of them, or decide about one or more assets of the
2. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
16
organization”, then what is an insider threat?
One definition is that:
“An insider threat is an individual with privileges who misuses them or whose access results in
misuse”.
Many companies and organizations, at some point, have knowingly or unknowingly been subject
to a cyber-attack. Many cyber attackers that exist on the outside of a company world or an
organizational infrastructure hacking and breaking the information systems to execute their
cyber-attacks. Others cyber-attacks are executed with the help of viruses or system/network
intrusion. Until the past decade however, insiders were often overlooked as potential threats to
commit cyber-attack. Although there are security and access control policies to prevent
organizations from known threats, individuals that are trusted to follow these policies do not at
times. When security policies and access control policies are not followed by anyone who is a
part of organization, it typically exposes organizations to both external and internal cyber threats.
Although the majority of cyber-attacks stem from external entities, insider attacks are often more
damaging and costly due to the knowledge of and access to information systems. The intricacies
surrounding insider threat are more complex than those dealing with external entities. This is
because internal cyber-attacks do not always occur as a direct result of a breach of security or
access policy. Some internal attacks occur without a breach of any security or access policy.
The company uses a patented process to produce goods that are applied in a variety of end-
products. Because of the intellectual property and specific knowledge that is available to insiders
(i.e. employees, business partners, visitors), the question rose on “How to protect intellectual
property and other valuable information against misuse of these insiders?”
Many other modern organizations make use of a sheer amount of information and information
systems. Organizations that value their information need to safeguard it from threat agents that
exploit vulnerabilities in information systems and/or information security measures. Although
attacks originating from outside threat agents, such as hacking attempts or viruses, have gained a
lot of publicity, the more risky attacks come from inside (Schultz, 2002; Baker et al., 2008).
Insiders are trusted and, therefore, have the necessary access to be able to exploit vulnerabilities
more easily.
It is widely accepted that the insider threat activities to enterprises is increasing, and that
significant costs are being incurred. Since insider threat and compromising actions can take a
multitude of forms, there is a diverse experience and understanding of what insider threats are,
and how to detect or prevent them. The purpose of this research is to investigate the potential for
near real-time detection of insider threat activities within a large enterprise environment using
monitoring tools centered on the information infrastructure. As insider threat activities are not
confined solely to cyber-based threats, the research will explore the potential for harnessing a
variety of threat indicators buried in a different enterprise operations connected to or interfacing
with the information infrastructure, enabling human analysts to make informed decisions
efficiently and effectively.
2. RELATED WORKS
Real insider threats are complex and adversarial, which leads us to conclude that an effective
system for detecting these threats must detect scenarios that builders of the system never planned
3. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
17
for or contemplated. Therefore, it is important to evaluate systems on their ability to detect
previously unknown scenarios in real data.
William T. Young, Alex Memory, Henry G. Goldberg, Ted E. Senator in at [1] evaluate some
prototype in their setting and show that by using a variety of diverse individual detectors
combined using an anomaly detection ensemble technique, they achieve a final detection result
with performance that consistently approaches that of the unidentified detector among the set
tested that was found to perform best on each dataset in after-the-fact analysis. Their result holds
on many data sets, including ones containing scenarios they had not contemplated when
designing the detectors. The ensemble result also outperforms many anomaly detectors that are
specifically focused on the scenarios that are known, on data sets containing those scenarios.
Aleksandar Lazarevic, Vipin Kumar in at [2] worked on novel general framework for combining
outlier detection algorithms. Experiments on several synthetic and various real life data sets
indicate that proposed combining methods can result in much better detection performance than
the single outlier detection algorithms. The proposed combining methods successfully utilize
benefits from combining multiple outputs and diversifying individual predictions through
focusing on smaller feature projections. Data sets used in our experiments contained different
percentage of outliers, different sizes and different number of features, thus providing a diverse
test bed and showing wide capabilities of the proposed framework. The universal nature of the
proposed framework allows that the combining schemes can be applied to any combination of
outlier detection algorithms thus enhancing their usefulness in real life applications. Although
performed experiments have provided evidence that the proposed methods can be very successful
for the outlier detection task, future work is needed to fully characterize them especially in very
large and high dimensional databases, where new algorithms for combining outputs from
multiple outlier detection algorithms are worth considering. It would also be interesting to
examine the influence of changing the data distributions when detecting outliers in every round
of combining methods, employing not only the distance-based but also other types of outlier
detection approaches.
3. PROBLEM STATEMENT
Find the Unknown and new scenario which helps to achieves consistent result and performance
without relying any single detector or the best unidentified detector for each analysis.
4. SOLUTION APPROACH AND METHODOLOGY
Find new scenario that are able to incorporate scenario-focused detectors effectively to increase
confidence in results when known scenarios do match with ones in the data. We will also begin
incorporating explanation capabilities with the ensemble approach so that underlying reasons for
detection from individual detectors can be combined in the final result presented to analysts.
In this research, I introduce some new scenarios and their solutions which help any security
system to increase their success percentage by grabbing any suspect and those new scenarios are:
1. Wearable technologies.
2. The conscientious objector
3. Hide system which is handled by outsider.
4. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
18
4. User knows about insider threat detection OR Hiding the illegal activities information from
investigators.
5. Analyzing employee activity outside the origination. All above scenarios have their own
problem as well as own solution by which insider threat system is affected.
4.1 WEARABLE TECHNOLOGIES
Technically, just about any device that’s worn on the body (like a headset) can be considered
wearable technology. We have had smart phones with cameras for years. However, others can see
when someone is taking pictures with a smart phone. New wearable technology could be
recording conversations or copying intellectual property without being detected. They can
connect with network unethically and do whatever they want with the data flow on the network.
OUR PROPOSED ALGORITHM:
New network set-up initiate.
Register all known authorized devices by their MAC address in network.
Start network scan for new node periodically.
Authenticate new device by MAC address while making connection to the network.
Generate alert if any unauthorized device is connected to network.
Block that unauthorized device on network.
Find access point of that unauthorized device.
Find unauthorized user.
Find unauthorized device which is connected to network using proposed algorithm:
Given
t : time interval for network Net scan
N : total number of devices connected to network Net
n : number of unauthorized devices connected to network Net
L : List of registered MAC addresses on network Net
l : List of unauthorized MAC addresses on network Net
Initialize network Net;
Register all known devices MAC address in Net;
Initialize N = total number devices registered on Net; Initialize n = 0;
Initialize L = devices registered with MAC on Net;
Initialize l = 0;
Scan network with t interval; If(unauthorized device found on Net)
{
n = number of unauthorized devices;
l = MAC address of unauthorized
devices;
findIntersectionOf(L, l);// this uses simple intersection algorithm
raiseAlertToAdmin();
blockUnauthorizedDevicesOnNet();
findAccessPointOfUnauthorizedDe
vicesOnNet();
}
5. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
19
It uses simple mapping algorithm which just calculate intersection of registered MAC addresses
and connected MAC addressed and gave unauthorized connected MAC information as an result.
By finding correct access point from where unauthorized person establish its connection to the
network, we can find rouge person. This scenario and its solution help us to find any unauthorized
device which is connected to the network weather it is wearable or any other device like
computer, laptop, mobile or any other device which is able to establish connection to the network
and capture the data for any purpose.
4.2 HIDE SYSTEM WHICH IS HANDLED BY OUTSIDER.
In thus technical era, there is a boom of technology in every field. People have such type of
electronic devices which are easy to hide and control. Those devices either wired or wireless,
people are able to do their data transmission by using those devices. People use hardware or
software for which are able to transmit data, sniff data and collect sensitive data unethically.
Always monitor your network by both means i.e. software as well as hardware.
Monitor unwanted or untrusted software or hardware on network continuously and whenever
those kinds of things are found then take proper action by investigating it properly.
We can imply all these above things by:
a) Scan physical state of network i.e. scan for unknown hardware part in network, in
computers like data packet sniffing card, mini USB devices, network taps, port mirroring
switchesetc.
b) Scan all the machines for untrusted software like Wireshark, Smartsniffetc.
c) Remove USB ports/ CD-DVD drives from all machines, deploy them on employee
request.
d) any type of downloading on the network, if any one wants any software then he/ she
request the same from admin.
e) Assign static IP’s to all the system which will help admin to track every system in the
organization.
6. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
20
4.3 USER KNOWS ABOUT INSIDER THREAT DETECTION OR HIDING THE ILLEGAL
ACTIVITIES INFORMATION FROM INVESTIGATORS.
For insider threat detection, organization deploys an insider threat detection system in
organization. But what happened when employee wants to be whistleblower or wants to perform
illegal activity in organization which will harmful for organization?
What happens when an employee is a part of threat detection system team member or know
everything which will makes him/ her to hide every illegal activity from the system?
Recent exploration performed by the popular security corporation reveals that your computer and
mobile phones can still get hacked even if they aren’t connected to the internet or by malicious
system which is not connected to your network. People uses ELECTROMAGNETIC
RADIATION for tracking down the keyboard activity and get every single key pressed on
keyboard.
To avoid theft of information from such type of techniques, organizations have to monitor their
workplace for such kind of devices or system periodically.
4.4 ANALYZING EMPLOYEE ACTIVITY OUTSIDE THE ORIGINATION
This is not an ethical way to monitor any employee beyond the organization boundaries but, now
days, this is an important thing to monitor your employee 24/7 because of security threat fear.
Let’s take an example, for work flexibility, organization allowed employees to “work from
home” and employees work from home by various remote desktop tools which help them to
access organization non- disclosure environment.
Now, what happened, if any employee of organization misuse this facility and leak organization
classified information to competitor or any other outsider which is harmful for the organization?
These types of scenarios are not adjustable and must be figure out by some ways to resolve such
type of issues.
It is not possible to monitor all employees all the time outside the working place but if admin
have any suspect then organization will have to monitor that suspect outside the working place
for bringing more information for the same.
4.5 THE CONSCIENTIOUS OBJECTOR (GAIN TRUST AND MISUSE IT)
An individual who refuse to follow organization policies weather those policies are right or
wrong. This is a very complex threat for organizations because trust is not any a physical thing
and it cannot be measurable.
What organizations do is, organizations can check background, take feedback from past
organizations, put a legal clause for confidentiality etc. and after doing all the possible validation
of employee, organization allow employee to be a part of organization.
5. CONCLUSIONS AND FUTURE WORK
Dealing with insider threats has been hard for years. No one is exempt from dealing with our
changing technological landscape or our own role in helping secure the enterprise. I added up
some more scenarios by which we can increase the probability of success in capturing the
suspect red handed with proof.As this is not a one-time problem or solution which is stop at
7. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 5/6, December 2016
21
certain point where we can find a static solution for this. In future, there are, for sure, more
scenarios are emerged to breach organizations security or confidentiality which must be
resolved after finding them for avoiding any type of leakages of sensitive data outside the world.
6. ACKNOWLEDGMENTS
This Research Was Supported/Partially Supported By “BM Group Of Colleges Indore”. I Thank
My Mentor From “BM Group Of Colleges Indore” Who Provided Insight And Expertise That
Greatly Assisted The Research. I Thank Rahul Kaul, Professor Of BM Group Of Colleges For
Assistance With Finding New Algorithm, And Kapil Vyas, Professor Of BM Group Of Colleges
For Comments That Greatly Improved The Manuscript.
REFERENCES
[1] Detecting Unknown Insider Threat Scenarios William T. Young, Alex Memory, Henry G. Goldberg,
Ted E. Senator Leidos, Inc. Arlington, VA, USA {youngwil, memoryac, goldberghg,
senatort}@leidos.com
[2] Feature Bagging for Outlier Detection, Aleksandar Lazarevic, United Technologies Research Center,
University of Minnesota, Vipin Kumar, Department of Computer Science, University of Minnesota,
USA.
[3] SC Magazine. (2012) Danger within: Insider threat. [Online]. Available:
http://www.scmagazine.com/report-insider-threat-more-dangerous-than-external-risks/article/455117/
[4] CERT Insider Threat Team.(2013)Unintentional insider threats : A foundational
study.[Online].Available:http://resources.sei.cmu.edu/library/assetview.cfm?assetid=51648
[5] J. Hunker and C. W. Probst, “Insiders and insider threats – an overview of definitions and mitigation
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