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
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
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 Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
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 Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
Forecasting number of vulnerabilities using long short-term neural memory net...IJECEIAES
Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
With the increasing importance of cyberspace security, the research and application of network situational awareness is getting more attention. The research on network security situational awareness is of great significance for
improving the network monitoring ability, emergency response capability and predicting the development trend of network security
Abstract—Classical machine learning techniques have been employed severally in intrusion detection. But due to the rising cases and sophistication of attacks, more advanced machine learning techniques including ensemble-based methods, neural networks and deep learning techniques have been applied. However, there is still need for improved machine learning approach to detect attacks more effectively and efficiently. Stacked generalization approach has been shown to be capable of learning from features and meta-features but has been limited by the deficiencies of base classifiers and lack of optimization in the choice of meta-feature combination. This paper therefore proposes a stacked generalization ensemble approach based on two-tier meta-learner, in which the outputs of classical stacked ensemble are passed to multi-feature-based stacked ensemble, which is optimized. A Grid-search approach is used for the optimization. Nine data features and four meta-features derived from Logistic Regression, Support Vector Machine, Naïve Bayes, and Multilayer Perceptron neural network are used for the machine learning classification task. By applying neural networks as the meta-learner for the classification of NSL-KDD data, improved performances in terms of accuracy, precision, recall and F-measure of 0.97, 0.98, 0.98 and 0.98, respectively are achieved.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
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"
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
Defects in modules of software systems is a major problem in software development. There are a variety of data mining
techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned
with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural
network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is
that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural
networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use
a range of software defect prediction models to complement each other rather than relying on a single technique.
A critical review on Adversarial Attacks on Intrusion Detection Systems: Must...PhD Assistance
The present article helps the USA, the UK, Europe and the Australian students pursuing their computer Science postgraduate degree to identify right topic in the area of computer science specifically on deep learning, adversarial attacks and intrusion detection system. These topics are researched in-depth at the University of Spain, Cornell University, University of Modena and Reggio Emilia, Modena, Italy, and many more
http://www.phdassistance.com/industries/computer-science-information/
PhD Assistance offers UK Dissertation Research Topics Services in Computer Science Engineering Domain. When you Order Computer Science Dissertation Services at PhD Assistance, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts http://www.phdassistance.com/services/phd-literature-review/gap-identification/
For Any Queries : Website: www.phdassistance.com
Phd Research Lab : www.research.phdassistance.com
Email: info@phdassistance.com
Phone : +91-4448137070
Contact Name Ganesh / Vinoth Kumar
I take our currently implemented real-time analytics platform which makes decisions and takes autonomous action within our environment and repurpose it for a hypothetical solution to a phishing problem at a hypothetical startup.
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.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
Benchmarks for Evaluating Anomaly Based Intrusion Detection SolutionsIJNSA Journal
Anomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time. There are many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, however there is no standard benchmark to compare them based on quantifiable measures. In this paper, we propose a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. We then use this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering. The benchmark evaluation is performed on the popular NSL-KDD dataset. The experimental results show the differences in accuracy and performance between these Anomaly-based IDS solutions on the dataset. The results also demonstrate how this benchmark can be used to create useful metrics for such comparisons.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion. Our observations confirm the conjecture
that both the feature selection and stochastic based genetic operators improves the accuracy and the
effectiveness. The training time is shown to be reduced tremendously by 98.59% and accuracy improved to
98.75%.
Forecasting number of vulnerabilities using long short-term neural memory net...IJECEIAES
Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
With the increasing importance of cyberspace security, the research and application of network situational awareness is getting more attention. The research on network security situational awareness is of great significance for
improving the network monitoring ability, emergency response capability and predicting the development trend of network security
Abstract—Classical machine learning techniques have been employed severally in intrusion detection. But due to the rising cases and sophistication of attacks, more advanced machine learning techniques including ensemble-based methods, neural networks and deep learning techniques have been applied. However, there is still need for improved machine learning approach to detect attacks more effectively and efficiently. Stacked generalization approach has been shown to be capable of learning from features and meta-features but has been limited by the deficiencies of base classifiers and lack of optimization in the choice of meta-feature combination. This paper therefore proposes a stacked generalization ensemble approach based on two-tier meta-learner, in which the outputs of classical stacked ensemble are passed to multi-feature-based stacked ensemble, which is optimized. A Grid-search approach is used for the optimization. Nine data features and four meta-features derived from Logistic Regression, Support Vector Machine, Naïve Bayes, and Multilayer Perceptron neural network are used for the machine learning classification task. By applying neural networks as the meta-learner for the classification of NSL-KDD data, improved performances in terms of accuracy, precision, recall and F-measure of 0.97, 0.98, 0.98 and 0.98, respectively are achieved.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
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"
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
Defects in modules of software systems is a major problem in software development. There are a variety of data mining
techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned
with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural
network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is
that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural
networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use
a range of software defect prediction models to complement each other rather than relying on a single technique.
A critical review on Adversarial Attacks on Intrusion Detection Systems: Must...PhD Assistance
The present article helps the USA, the UK, Europe and the Australian students pursuing their computer Science postgraduate degree to identify right topic in the area of computer science specifically on deep learning, adversarial attacks and intrusion detection system. These topics are researched in-depth at the University of Spain, Cornell University, University of Modena and Reggio Emilia, Modena, Italy, and many more
http://www.phdassistance.com/industries/computer-science-information/
PhD Assistance offers UK Dissertation Research Topics Services in Computer Science Engineering Domain. When you Order Computer Science Dissertation Services at PhD Assistance, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts http://www.phdassistance.com/services/phd-literature-review/gap-identification/
For Any Queries : Website: www.phdassistance.com
Phd Research Lab : www.research.phdassistance.com
Email: info@phdassistance.com
Phone : +91-4448137070
Contact Name Ganesh / Vinoth Kumar
I take our currently implemented real-time analytics platform which makes decisions and takes autonomous action within our environment and repurpose it for a hypothetical solution to a phishing problem at a hypothetical startup.
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.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
Benchmarks for Evaluating Anomaly Based Intrusion Detection SolutionsIJNSA Journal
Anomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time. There are many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, however there is no standard benchmark to compare them based on quantifiable measures. In this paper, we propose a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. We then use this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering. The benchmark evaluation is performed on the popular NSL-KDD dataset. The experimental results show the differences in accuracy and performance between these Anomaly-based IDS solutions on the dataset. The results also demonstrate how this benchmark can be used to create useful metrics for such comparisons.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion. Our observations confirm the conjecture
that both the feature selection and stochastic based genetic operators improves the accuracy and the
effectiveness. The training time is shown to be reduced tremendously by 98.59% and accuracy improved to
98.75%.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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 NOVEL INTRUSION DETECTION MODEL FOR MOBILE AD-HOC NETWORKS USING CP-KNNIJCNCJournal
Mobile ad-hoc network security problems are the subject of in depth analysis. A group of mobile nodes area unit connected to a set wired backbone. In MANET, the node themselves implement the network management in a very cooperative fashion. All the nodes area unit accountable to create a constellation that is dynamically, modification it and conjointly the absence of any clear network boundaries. We tend to project a completely unique intrusion detection model for mobile ad-hoc network victimization. CP-KNN (Conformal Prediction K-Nearest Neighbor) algorithmic rule is to classify the audit knowledge for anomaly detection. The non-conformity score worth is employed to cut back the classification period of time for multi level iteration. It is effectively notice anomalies with high true positive rate, low false positive rate and high confidence that the progressive of assorted anomaly detection ways. Additionally it is interfered
by “noisy” knowledge (unclean data), the projected technique is strong, effective and conjointly it retains
its smart detection performance and to avoid the abnormal activity.
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.
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
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Real Time Intrusion Detection System Using Computational Intelligence and Neural Network: A Review
1. @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 1 | Issue – 6 | Sep - Oct 2017 Page: 1317
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume - 1 | Issue – 6
International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
Real Time Intrusion Detection System using Computational
Intelligence and Neural Network: A Review
Dr. Prabha Shreeraj Nair
Dean Research, Tulsiramji Gayakwade Patil College of
Engineering and Technology, Nagpur
ABSTRACT
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.
Keywords: Intrusion detection; neural network;
computational intelligence;
I. INTRODUCTION
Intrusion prevention methodology such as access
control, firewalls or encryption, unable to completely
protected the network during malwares and attacks.
Thus, intrusion detection systems (IDS) address the
solution of these securities over protection of system
or widespread network.
In intrusion detection system, patterns of intrusion
obtain on the basis of compare audit data to detection
model. Outcomes obtain into two phase that is
intrusion attempt or unsuccessful intrusion attempt,
both are help for intrusion identities. Intrusion
detection model[1] concentrate attention in 1987, at
that time when researchers focused on practically
implementation of these aspects. In 1990, a new
approach has arrived that are combination of
statistical aspects and expert system regarding
detection of normal and abnormal behavior of
automated system or manually transmission over
network. Set of training data generated via machine
learning approach and artificial intelligence.
Generally, set of training data prepared with the help
of following techniques that is classification, data
clustering and rule based induction.
In intrusion detection problems, data is not trivial
when process of automatically constructing models.
There are challenges to define outline between normal
and abnormal behavior during unbalance node and
high traffic of network so as per requirement
dynamically adaptation must be satisfies. As per
requirement of high detection accuracy with respect to
time, machine learning and artificial intelligence have
limitation. However, in this circumstances
computational intelligence approach play very
important roll due to it is able to handle fault tolerance
and adaption at the noisy information over the
network.
The objective of this paper highlight challenges,
review and suggestion regarding common mistakes
done by researcher for intrusion detection models
using computational intelligence (CI) and neural
network.
II. RELATED WORK
A. Intrusion detection
The working strategy of intrusion detection system is
that run time analysis or runtime monitoring over the
system or network. Thus it is able to decide, whatever
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events running on that are normal or abnormal with
respect to system or network
[1] Organization of intrusion detection system as
figure 1, here data /control flow indicated by solid
lines and responses to intrusive activities indicated by
dashed lines.
Fig 1: Organization of intrusion detection system
In intrusion detection system based on anomaly
detection and misuse detection, its divided into two
phases.
Misuse detection, working strategy is that outcomes
of data compare to predefined intrusive behavior and
based on this matching phenomena observed the
intrusions with better accuracy. So due to this
strength, its adopted into commercial projects.
Sometime intrusions are unexpected means that
unable to predict the behavior then misuse detection
has unable to solve such issue that is limitation of
misuse detection for example facing unknown
intrusions. As a solution of this issue is that
continually run time updated the knowledge database
as per requirement of supervised learning algorithms.
It is challenging and costly task for prepared dataset
when its run time change, its behavior or depends on
type of intrusions. The alternate solution of this issue
solve by Denning [5], using anomaly detection model.
In the anomaly detection, let us consider that
abnormal behavior observed rarely and its symptoms
or behavior different from normal behavior.
Therefore, anomaly detection observed by monitoring
the behavior models and compares it from normal
behavior. Based on observation, anomaly detection
divided into two categories static and dynamic [6]. In
static anomaly detection indicates that behavior of
intrusion never changes. The real time example is
system call of operating systems.
In dynamic anomaly detection, check and extract the
profile of end user on the basis of history or predict
habit based on previous profile data corresponding to
particular profile.
As a working strategy of anomaly detection, we can
conclude that it is easily identify new types of
intrusions and required only profiles data. The
challenging task is that identify the outline normal and
abnormal behavior. Secondary challenges are runtime
changes of normal behavior to abnormal behavior.
Thus, for better accuracy we have used some addition
categories of intrusion detection system as soon in
figure 2.
B. Computational intelligence
Computational Intelligence is logical approach [7];
here whatever agent we have design work as
intelligent agents. It has ability to understand the
situation or limitation of particular scenario and take
the decision according to them for finite computation,
it learn from experiences and flexible for integrity,
fault tolerance and adaption.
According to Bezdek[8];
Any system is consider the computational intelligent
[8]when it has capability pattern/features recognition
at only numerical workload and observed pattern
dummy for in terms of knowledge regarding artificial
intelligence; and it has capability to manage following
parameters such error rates, fault tolerance, numerical
adaptively corresponding to human performance.
Fig 2: Characteristics of Intrusion detection
systems over a network
Based on observed data, intrusion detection
techniques categories into parts: anomaly detection
and misuse detection. In misuse detection, working
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strategy is that identifies and compared observed data
with predefined behavior of intrusive. Thus effective
outcomes obtain with low false alarm rate. Due to this
strength and advantage, it’s used in commercial
projects. Behaviors of intrusion are unpredictable and
may be change in run time then it’s unable to handle
by misuse detection. For example if we have found
any unknown intrusion; that is limitation of misuse
detection.
The address of solution for this issue into anomaly
detection, in which updated the knowledge database
as per run time requirement with the help of
supervised learning algorithms. The task for run time
updating the database may be costly and challenging
at run time, in order to consider the better accuracy
analysis of profile and predict behavior of end user[1].
III. ALGORITHMS
Address the solution of intrusion detection system,
there are following aspects are possible as per
dynamic requirement.
A. Artificial neural networks
Neurons are basic processing unit of artificial neural
networks (ANN) that are fully connected basis on
topology. ANN is update or enhanced learning by its
experiences and generalized the outline of the system
from noisy data, limited or incomplete data. It is
successfully wide spectrum over datasets.
A.1 Supervised learning
Supervised learning is first simplest and arguable
artificial neural network devise are feed forward
neural networks. Supervised learning divided into two
types: forward neural and multi layered feed forward.
Multilayered feed forward back propagation (MLFF-
BP)[9,10] is capable to handle work at user behavior
on the following aspects such as host address of login,
command sets, difference between normal and
abnormal behavior[10], so this techniques used into
anomaly detection of intrusion system When
automated intrusion detection system has arrived then
researcher focused on predicts software behavior
using sequences of system calls. According to Ghosh
et al. observed that system call more stable compare
to commands, in it proposed[12] approach apply the
DARPA BSM98 dataset[11].
Fig 3: Categories of Artificial Neural Networks
A.2 Unsupervised learning
Unsupervised neural networks are two typical
categories adaptive resonance theory and self-
organizing maps. As the statistical clustering
algorithms, it has group objects. Unsupervised
learning is suitable for intrusion detection tasks for
normal behavior.
A.2.1 Self-organizing maps
Kohonen maps or Self-organizing maps (SOM) is
feed forward networks single-layer, outputs are
clustered 2D or 3D grid [13]. Based on their
similarity, we preserve topological relationships for
the input data.
Self-organizing maps used anomaly detection for
trained datasets. It able to detect viruses [14] over
multiuser machine in 1990. After few time, some
researchers [15,20] focused on Self-organizing maps
for extract pattern or feature of general system events.
Thus, self organizing map are used into misuse
intrusion detection system.
A.2.2 Adaptive resonance theory (ART)
The adaptive resonance theory is capable of handle
wide spread of neural network models in terms of
pattern recognition, efficiency of unsupervised/
supervised learning. Unsupervised learning models
associated with Fuzzy ART , ART-version.(version
are 1,2,3) and supervised networks are Fuzzy
ARTMAP and Gaussian ARTMAP.
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IV. DATASETS AND PERFORMANCE
EVALUATION TECHNIQUES
There are few misconception, we are observe during
review process and try to addresses the solution with
respect to standards datasets.
Generally, in the reviewed research work data are
collected from three sources: log files, data packets,
CPU/memory usage and system call sequences. We
represent benchmarks regarding intrusion detection
datasets as describe in Table 1. Researchers free to
use these datasets either anomaly detection and
misuse detection. We categories two benchmarks
datasets that are the KDD99 and DARPA-Lincoln.
MIT’s Lincoln laboratory, collect the DARPA-
Lincoln datasets, the implementation of intrusion
detection techniques. In 1998, collection of datasets
into two categories that are training data and test data
during few weeks.
Table 1: Datasets for Intrusion detection system
Source of Data Dataset name Notation
Traffic into TCP Dump File for DARPA in
Network 1998[15] DARPA98
TCP Dump File for DARPA in 1999
[15] DARPA99
Datasets of KDD99 [17] KDD99
Datasets of 10% KDD99 [17] KDD99-10
Internet Exploration Shootout IES
Datasets [18]
User behavior Datasets of UNIX [19] UNIXDS
System call BSM File of DARPA in 1998[15] BSM98
Sequences
BSM File of DARPA in 1999 [15] BSM99
Datasets of New Mexico [6] UNM
A. Performance evaluation Strategy
The intrusion detection systems are effectiveness
evaluation if it is able to produce correct predictions.
In real time scenario when we are compared
prediction to actual outcomes with respect to intrusion
detection system, then obtain four possibility such as
true negatives, true positive, false positive and false
negatives called as confusion matrix. True negatives
and true positives obtain respectively if successfully
execute the events. False positives indicate general
events corresponding to predict as attacks; false
negatives are observe if wrong predicted for normal
events. In this way, performance of intrusion
detection system observes the confusion matrix value.
V. SUMMARY AND SUGGESTION
Here we have focused artificial neural networks and
computational intelligence over intrusion detection.
Therefore, various unsupervised and supervised
artificial neural network are associated anomaly and
misuse detection techniques.
Few researchers [21,22] focused on the contradictory
approach of artificial neural networks. In this strategy,
reduce the training time and cluster approach to
address the solution retraining problem of artificial
neural network if facing a new class of data; Hofmann
et al. [22] proposed solution for black box nature of
artificial neural network via if–then rules over trained
artificial neural network For the purpose of improve
detection accuracy; there are following practices
useful in real time scenario at artificial neural
network:
➢ Temporal locality property:
It is useful property or parameters for normal or
intrusive behavior regarding intrusion detection
domain. Generally, time stamp of artificial neural
network represented into two modes implicitly or
explicitly. Conclude that under the mode of explicitly
representation [23, 24] of time unable to produce
accurate identify intrusions. Either we selected
implicit mode of representing time, at that time
required short-term memory. Due to better utilization
of bandwidth vector concept is used over sliding
windows. Another secondary strategy, evaluate time
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difference between two events, leaky bucket
algorithm, chaotic neurons, layer-window statistical
preprocessors. Thus, temporal locality property play
important role in design and analysis of artificial
neural network detection technique.
➢ Network infrastructure:
Prediction of intrusions is difficult task and
involvement of intrusion are continuously process.
We are unable to predict attackers objective for
example sometimes it’s interested into protocol,
operating system or application based attacks. So it’s
unable to insure that single neural network has
successfully addresses the solution.
➢ Datasets and features:
Neural networks have recognized corresponding to
input datasets. The training datasets has limitation for
unknown feature pattern extraction due to dependency
of input datasets. We obtain complete training set
[16,20] with respect to more network patterns. Based
on selection of optimal feature sets affect the
performance improvements. Sarasamma et al.[25]
proposed different subsets of workload of features, for
the purpose of searching fixed categories of attacks.
According to Kayacik et al. [26] proposal,
hierarchical self organizing maps framework over the
KDD99 data, it has observe that six fundamental
features of sufficient for recognizing a wide scope
over denial of service attacks.
VI. CONCLUSION
It is observed that this research paper focused on
analysis, review and summary with suggestion
regarding existing challenges for intrusion detection
system using computational intelligence and neural
network. It’s described misconception and suggestion
regarding same. On the basis of identities for intrusion
detection system, soft computing play important role
in such a way, disadvantages superimpose and offer
better solutions. However, computational intelligence
and neural network addresses the solution for
intrusion detection system.
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