This document summarizes research on automatic insider threat detection in email systems using n-gram techniques. It discusses how n-grams can be used to classify documents and detect potential data leakage through email. The system would verify emails sent outside the network using SHA, n-grams and thresholds. If a threat is detected, the user would be blocked. The document also provides a literature review on 10 other papers related to insider threat detection using methods like user profiling, activity logs, data mining and visualization techniques. It describes how n-grams work by breaking words into character sequences and creating profiles based on frequency to classify documents.
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 %.
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.
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 %.
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.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
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.
System Dynamics Based Insider Threats ModelingIJNSA Journal
Insider threat has been recognized as one of the most dangerous security threats and become a much more complex issue. Insider threat is resulted from the legitimate users abusing their privileges and cause tremendous damage or losses. Not always being friends, insiders can be main threats to the organization. Currently, there is no equivalent prevention solution for insider threat to an intrution prevention system or vulnerability scanner. From the survey of literature of insider threat studies, we conclude that the system dynamics (SD) is an effective tool to analyze the root causes of insider threat incidents and evaluate mitigation strategies from people, process, and technology perspectives. A generized case based SD model can be tailored and applied to analyze and evaluate specific insider threat incidents. We
present a well known insider threat incident of Taiwan and tailor the generized case based SD model to analyze it. The simulation results indicate that the risk of insider threats can be reduced and the probability of detecting insider threats can be increased.
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.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
"In any organization it is an essential task to protect the data from unauthorized users. Information Systems hardware, software, networks, and data resources need to be protected and secured to ensure quality, performance, and integrity. Security management deals with the accuracy, integrity, and safety of information resources. When effective security measures are in place, they can reduce errors, fraud, and losses. In the current work, the authors have proposed a model for encryption of a text phrase employing genetic algorithm. The entropy inherently available in genetic algorithm is exploited for introducing chaos in a text phrase thereby rendering it unreadable. The no of cross over points and mutation points decides the strength of the algorithm. The prototype of the model is implemented for testing the operational feasibility of the model and the few test cases are presented Dr. Poornima G. Naik | Mr. Pandurang M. More | Dr. Girish R. Naik ""A Model for Encryption of a Text Phrase using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23063.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23063/a-model-for-encryption-of-a-text-phrase-using-genetic-algorithm/dr-poornima-g-naik"
A Novel and Advanced Data Mining Model Based Hybrid Intrusion Detection Frame...Radita Apriana
An Intrusion can be defined as any practice or act that attempt to crack the integrity,
confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access
the information, manipulate the data, or make a system unreliable or unusable. With the expansion of
computer networks at an alarming rate during the past decade, security has become one of the serious
issues of computer systems.IDS, is a detection mechanism for detecting the intrusive activities hidden
among the normal activities. The revolutionary establishment of IDS has attracted analysts to work
dedicatedly enabling the system to deal with technological advancements. Hence, in this regard, various
beneficial schemes and models have been proposed in order to achieve enhanced IDS. This paper
proposes a novel hybrid model for intrusion detection. The proposed framework in this paper may be
expected as another step towards advancement of IDS. The framework utilizes the crucial data mining
classification algorithms beneficial for intrusion detection. The Hybrid framework would hence forth, will
lead to effective, adaptive and intelligent intrusion detection.
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 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.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
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-based detectors.
Intrusion Detection System using Self Organizing Map: A SurveyIJERA Editor
Due to usage of computer every field, Network Security is the major concerned in today’s scenario. Every year the number of users and speed of network is increasing, along with it online fraud or security threats are also increasing. Every day a new attack is generated to harm the system or network. It is necessary to protect the system or networks from various threats by using Intrusion Detection System which can detect “known” as well as “unknown” attack and generate alerts if any unusual behavior in the traffic. There are various approaches for IDS, but in this paper, survey is focused on IDS using Self Organizing Map. SOM is unsupervised, fast conversion and automatic clustering algorithm which is able to handle novelty detection. The main objective of the survey is to find and address the current challenges of SOM. Our survey shows that the existing IDS based on SOM have poor detection rate for U2R and R2L attacks. To improve it, proper normalization technique should be used. During the survey we also found that HSOM and GHSOM are advance model of SOM which have their own unique feature for better performance of IDS. GHSOM is efficient due to its low computation time. This survey is beneficial to design and develop efficient SOM based IDS having less computation time and better detection rate.
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
Applicability of Network Logs for Securing Computer SystemsIDES Editor
Logging the events occurring on the network has
become very essential and thus playing a major role in
monitoring the events in order to keep check over them so
that they doesn’t harm any resources of the system or the
system itself. The analysis of network logs are becoming the
beneficial security research oriented field which will be desired
in the computer era. Organizations are reluctant to expose
their logs due to risk of attackers stealing the sensitive
information from their respective logs. In this paper we are
defining architecture and the security measures that can be
applied for a particular network log.
Phishing Websites Detection Using Back Propagation Algorithm: A Reviewtheijes
Phishing is an illicit modus operandi employing both societal engineering and technological subterfuge to theft client’s private identity data and monetary account credentials. Influence of phishing is pretty radical as it engrosses the menace of identity larceny and financial losses. This paper elucidates the back propagation paradigm to instruct the neural network for phishing forecast. We execute the root-cause analysis of phishing and incentive for phishing. This analysis is intended at serving developers the effectiveness of neural networks in data mining and provides the grounds proving neural networks in phishing detection.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
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.
System Dynamics Based Insider Threats ModelingIJNSA Journal
Insider threat has been recognized as one of the most dangerous security threats and become a much more complex issue. Insider threat is resulted from the legitimate users abusing their privileges and cause tremendous damage or losses. Not always being friends, insiders can be main threats to the organization. Currently, there is no equivalent prevention solution for insider threat to an intrution prevention system or vulnerability scanner. From the survey of literature of insider threat studies, we conclude that the system dynamics (SD) is an effective tool to analyze the root causes of insider threat incidents and evaluate mitigation strategies from people, process, and technology perspectives. A generized case based SD model can be tailored and applied to analyze and evaluate specific insider threat incidents. We
present a well known insider threat incident of Taiwan and tailor the generized case based SD model to analyze it. The simulation results indicate that the risk of insider threats can be reduced and the probability of detecting insider threats can be increased.
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.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
"In any organization it is an essential task to protect the data from unauthorized users. Information Systems hardware, software, networks, and data resources need to be protected and secured to ensure quality, performance, and integrity. Security management deals with the accuracy, integrity, and safety of information resources. When effective security measures are in place, they can reduce errors, fraud, and losses. In the current work, the authors have proposed a model for encryption of a text phrase employing genetic algorithm. The entropy inherently available in genetic algorithm is exploited for introducing chaos in a text phrase thereby rendering it unreadable. The no of cross over points and mutation points decides the strength of the algorithm. The prototype of the model is implemented for testing the operational feasibility of the model and the few test cases are presented Dr. Poornima G. Naik | Mr. Pandurang M. More | Dr. Girish R. Naik ""A Model for Encryption of a Text Phrase using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23063.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23063/a-model-for-encryption-of-a-text-phrase-using-genetic-algorithm/dr-poornima-g-naik"
A Novel and Advanced Data Mining Model Based Hybrid Intrusion Detection Frame...Radita Apriana
An Intrusion can be defined as any practice or act that attempt to crack the integrity,
confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access
the information, manipulate the data, or make a system unreliable or unusable. With the expansion of
computer networks at an alarming rate during the past decade, security has become one of the serious
issues of computer systems.IDS, is a detection mechanism for detecting the intrusive activities hidden
among the normal activities. The revolutionary establishment of IDS has attracted analysts to work
dedicatedly enabling the system to deal with technological advancements. Hence, in this regard, various
beneficial schemes and models have been proposed in order to achieve enhanced IDS. This paper
proposes a novel hybrid model for intrusion detection. The proposed framework in this paper may be
expected as another step towards advancement of IDS. The framework utilizes the crucial data mining
classification algorithms beneficial for intrusion detection. The Hybrid framework would hence forth, will
lead to effective, adaptive and intelligent intrusion detection.
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 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.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
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-based detectors.
Intrusion Detection System using Self Organizing Map: A SurveyIJERA Editor
Due to usage of computer every field, Network Security is the major concerned in today’s scenario. Every year the number of users and speed of network is increasing, along with it online fraud or security threats are also increasing. Every day a new attack is generated to harm the system or network. It is necessary to protect the system or networks from various threats by using Intrusion Detection System which can detect “known” as well as “unknown” attack and generate alerts if any unusual behavior in the traffic. There are various approaches for IDS, but in this paper, survey is focused on IDS using Self Organizing Map. SOM is unsupervised, fast conversion and automatic clustering algorithm which is able to handle novelty detection. The main objective of the survey is to find and address the current challenges of SOM. Our survey shows that the existing IDS based on SOM have poor detection rate for U2R and R2L attacks. To improve it, proper normalization technique should be used. During the survey we also found that HSOM and GHSOM are advance model of SOM which have their own unique feature for better performance of IDS. GHSOM is efficient due to its low computation time. This survey is beneficial to design and develop efficient SOM based IDS having less computation time and better detection rate.
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
Applicability of Network Logs for Securing Computer SystemsIDES Editor
Logging the events occurring on the network has
become very essential and thus playing a major role in
monitoring the events in order to keep check over them so
that they doesn’t harm any resources of the system or the
system itself. The analysis of network logs are becoming the
beneficial security research oriented field which will be desired
in the computer era. Organizations are reluctant to expose
their logs due to risk of attackers stealing the sensitive
information from their respective logs. In this paper we are
defining architecture and the security measures that can be
applied for a particular network log.
Phishing Websites Detection Using Back Propagation Algorithm: A Reviewtheijes
Phishing is an illicit modus operandi employing both societal engineering and technological subterfuge to theft client’s private identity data and monetary account credentials. Influence of phishing is pretty radical as it engrosses the menace of identity larceny and financial losses. This paper elucidates the back propagation paradigm to instruct the neural network for phishing forecast. We execute the root-cause analysis of phishing and incentive for phishing. This analysis is intended at serving developers the effectiveness of neural networks in data mining and provides the grounds proving neural networks in phishing detection.
Exploring network security threats through text mining techniques: a comprehe...CSITiaesprime
In response to the escalating cybersecurity threats, this research focuses on leveraging text mining techniques to analyze network security data effectively. The study utilizes user-generated reports detailing attacks on server networks. Employing clustering algorithms, these reports are grouped based on threat levels. Additionally, a classification algorithm discerns whether network activities pose security risks. The research achieves a noteworthy 93% accuracy in text classification, showcasing the efficacy of these techniques. The novelty lies in classifying security threat report logs according to their threat levels. Prioritizing high-risk threats, this approach aids network management in strategic focus. By enabling swift identification and categorization of network security threats, this research equips organizations to take prompt, targeted actions, enhancing overall network security.
Systematic Review Automation in Cyber SecurityYogeshIJTSRD
Many aspects of cyber security are carried by automation systems and service applications. The initial steps of cyber chain mainly focus on different automation tools with almost same task objective. Automation operations are carried only after detail study on particular task pre engagement phase , the tool is going to perform, measurement of dataset handling of tool produced output. The algorithm is going to make use of after comparing the existing tools efficiency, the throughput time, output format for reusable input and mainly the resource’s consumption. In this paper we are going to study the existing methodology in application and system pen testing, automation tool’s efficiency over growing technology and their behaviour study on unintended platform assignment. Nitin | Dr. Lakshmi J. V. N "Systematic Review: Automation in Cyber Security" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41315.pdf Paper URL: https://www.ijtsrd.comcomputer-science/computer-security/41315/systematic-review-automation-in-cyber-security/nitin
Abstract—With the heightening reliance on Information Technology in recent times, it has becoming more relevant to find measures to secure every online device, data and information. A Network Intrusion Detection System (NIDS) is one of the security options to consider to help protect such devices, data and information. However, IDS needs to be up to date to mitigate current threats to secure systems. A critical issue in the development of the right IDS is the scarcity of current data sets used for training these IDS and the impact on system performance. This paper presents an On-demand Network Data Set Creation Application (ONDaSCA) a Graphical User Interface software capable of generating labelled network intrusion data set. ONDaSCA grants IDS users or researchers the option to choose a raw data set and processed this data set as output, real-time packet capture and offline upload of existing PCAP file and two (2) difference packet capturing methods (Tshark and Dumpcap). ONDaSCA is highly customisable and an IDS user or researcher can leverage its capabilities to suit their needs. The abilities of this software are compared with other similar products that generate data set for use by IDS model.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
FEATURE EXTRACTION AND FEATURE SELECTION: REDUCING DATA COMPLEXITY WITH APACH...IJNSA Journal
Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cyber security threats and attacks while utilizing machine learning. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.
Use of network forensic mechanisms to formulate network securityIJMIT JOURNAL
Network Forensics is fairly a new area of research which would be used after an intrusion in various
organizations ranging from small, mid-size private companies and government corporations to the defence
secretariat of a country. At the point of an investigation valuable information may be mishandled which
leads to difficulties in the examination and time wastage. Additionally the intruder could obliterate tracks
such as intrusion entry, vulnerabilities used in an entry, destruction caused, and most importantly the
identity of the intruder. The aim of this research was to map the correlation between network security and
network forensic mechanisms. There are three sub research questions that had been studied. Those have
identified Network Security issues, Network Forensic investigations used in an incident, and the use of
network forensics mechanisms to eliminate network security issues. Literature review has been the
research strategy used in order study the sub research questions discussed. Literature such as research
papers published in Journals, PhD Theses, ISO standards, and other official research papers have been
evaluated and have been the base of this research. The deliverables or the output of this research was
produced as a report on how network forensics has assisted in aligning network security in case of an
intrusion. This research has not been specific to an organization but has given a general overview about
the industry. Embedding Digital Forensics Framework, Network Forensic Development Life Cycle, and
Enhanced Network Forensic Cycle could be used to develop a secure network. Through the mentioned
framework, and cycles the author has recommended implementing the 4R Strategy (Resistance,
Recognition, Recovery, Redress) with the assistance of a number of tools. This research would be of
interest to Network Administrators, Network Managers, Network Security personnel, and other personnel interested in obtaining knowledge in securing communication devices/infrastructure. This research provides a framework that can be used in an organization to eliminate digital anomalies through network forensics, helps the above mentioned persons to prepare infrastructure readiness for threats and also enables further research to be carried on in the fields of computer, database, mobile, video, and audio.
USE OF NETWORK FORENSIC MECHANISMS TO FORMULATE NETWORK SECURITYIJMIT JOURNAL
Network Forensics is fairly a new area of research which would be used after an intrusion in various
organizations ranging from small, mid-size private companies and government corporations to the defence
secretariat of a country. At the point of an investigation valuable information may be mishandled which
leads to difficulties in the examination and time wastage. Additionally the intruder could obliterate tracks
such as intrusion entry, vulnerabilities used in an entry, destruction caused, and most importantly the
identity of the intruder. The aim of this research was to map the correlation between network security and
network forensic mechanisms. There are three sub research questions that had been studied. Those have
identified Network Security issues, Network Forensic investigations used in an incident, and the use of
network forensics mechanisms to eliminate network security issues. Literature review has been the
research strategy used in order study the sub research questions discussed. Literature such as research
papers published in Journals, PhD Theses, ISO standards, and other official research papers have been
evaluated and have been the base of this research. The deliverables or the output of this research was
produced as a report on how network forensics has assisted in aligning network security in case of an
intrusion. This research has not been specific to an organization but has given a general overview about
the industry. Embedding Digital Forensics Framework, Network Forensic Development Life Cycle, and
Enhanced Network Forensic Cycle could be used to develop a secure network. Through the mentioned
framework, and cycles the author has recommended implementing the 4R Strategy (Resistance,
Recognition, Recovery, Redress) with the assistance of a number of tools. This research would be of
interest to Network Administrators, Network Managers, Network Security personnel, and other personnel
interested in obtaining knowledge in securing communication devices/infrastructure. This research
provides a framework that can be used in an organization to eliminate digital anomalies through network
forensics, helps the above mentioned persons to prepare infrastructure readiness for threats and also
enables further research to be carried on in the fields of computer, database, mobile, video, and audio.
Study on Exploring Data Mining Techniques for Network Intrusion Detection AM Publications,India
Network intrusion detection is very important mechanism for detecting intrusions in networks. Data mining techniques play very important role in detecting intrusions in networks. Intrusions cause damage to the data and compromise integrity and confidentiality and availability of the data. Though many intrusions preventing software's are developed and installed in network stations and network operating systems still finds vulnerabilities of the systems using network hacking techniques and tries to break the security walls of the system and enters despite the intrusion preventive mechanism built in. Hence network administrators and data management team feels the intrusion detection techniques are essential in-order to know that intrusion occurred or not and track them in and out entries in the network, so that steps can be taken for further for preventing intrusions or to block the intrusions that keep coming in. though In Corporate world many intrusion detection software's and detections techniques of different solutions are being developed still the people are not satisfied with the performance of the IDS. So many researches are still carrying on in this area to seek efficient techniques which are used for Intrusion detection. In this research papers gives the idea of what is network intrusion detection system and what it supposed to do and what are the problems with this technology and finally focus on Data Mining Techniques & data mining process to build more effective intrusion detection systems.
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.
Similar to Automatic Insider Threat Detection in E-mail System using N-gram Technique (20)
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.