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Jay Daftari

Bio: Jay Daftari is an academic researcher. The author has contributed to research in topics: Computer science & Computer security. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches.
Abstract: The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.

5 citations

Journal Article
TL;DR: In this paper , supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset and the results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient.
Abstract: Water is known as a "universal solvent" as it is extraordinarily frail against contamination. Water quality standards are developed based on logical evidence on the effects of hazardous compounds on a certain quantity of water used. Classification technique of machine learning can be employed to under-stand the water quality status. In this work, supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset. Artificial neural net-work model is built using the features such as Oxygen, pH, temperature, total suspended sediment, turbidity, nitrogen, and phosphorus as inputs and water quality check as target variable. This target variable is created using Canadian Council of Ministers of the Environment Water Quality Index, and the model works with an accuracy of 87%. The classification is done on XGBoost model as well and it performs with an accuracy of 90%. The explanations for predictions of these models for a data instance were performed using explainable artificial intelligence tools such as LIME and SHAP. The results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient. Through our re-search we can benefit our readers by providing them clarity about exactly what features are having more influence on water quality than others from different machine learning algorithms. This will help the developers to gain insights about the significant factors of poor water quality and how to overcome that.

Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors proposed a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta learner based on the output of each base algorithm, including Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts.
Abstract: Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.

1 citations

Journal Article
TL;DR: In this paper , supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset and the results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient.
Abstract: Water is known as a "universal solvent" as it is extraordinarily frail against contamination. Water quality standards are developed based on logical evidence on the effects of hazardous compounds on a certain quantity of water used. Classification technique of machine learning can be employed to under-stand the water quality status. In this work, supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset. Artificial neural net-work model is built using the features such as Oxygen, pH, temperature, total suspended sediment, turbidity, nitrogen, and phosphorus as inputs and water quality check as target variable. This target variable is created using Canadian Council of Ministers of the Environment Water Quality Index, and the model works with an accuracy of 87%. The classification is done on XGBoost model as well and it performs with an accuracy of 90%. The explanations for predictions of these models for a data instance were performed using explainable artificial intelligence tools such as LIME and SHAP. The results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient. Through our re-search we can benefit our readers by providing them clarity about exactly what features are having more influence on water quality than others from different machine learning algorithms. This will help the developers to gain insights about the significant factors of poor water quality and how to overcome that.
Journal ArticleDOI
TL;DR: In this paper , a methodology is proposed for detecting Zeus malware network traffic flows by using machine learning (ML) binary classification algorithms, which can be used to detect both older and newer versions of the Zeus malware.
Abstract: Banking malware are malicious programs that attempt to steal confidential information, such as banking authentication credentials, from users. Zeus is one of the most widespread banking malware variants ever discovered. Since the Zeus source code was leaked, many other variants of Zeus have emerged, and tools such as anti-malware programs exist that can detect Zeus; however, these have limitations. Anti-malware programs need to be regularly updated to recognise Zeus, and the signatures or patterns can only be made available when the malware has been seen. This limits the capability of these anti-malware products because they are unable to detect unseen malware variants, and furthermore, malicious users are developing malware that seeks to evade signature-based anti-malware programs. In this paper, a methodology is proposed for detecting Zeus malware network traffic flows by using machine learning (ML) binary classification algorithms. This research explores and compares several ML algorithms to determine the algorithm best suited for this problem and then uses these algorithms to conduct further experiments to determine the minimum number of features that could be used for detecting the Zeus malware. This research also explores the suitability of these features when used to detect both older and newer versions of Zeus as well as when used to detect additional variants of the Zeus malware. This will help researchers understand which network flow features could be used for detecting Zeus and whether these features will work across multiple versions and variants of the Zeus malware.
Book ChapterDOI
01 Jan 2023
TL;DR: In this paper , the authors proposed a stress prediction method using machine learning to detect the development of stress or anxiety problems at an early stage, which can prevent serious consequences as sometimes patients suffering from mental illness are not aware of the severity of their condition or do not keep up with counseling for longer period of time.
Abstract: With the primary focus of healthcare technologies being on the physical health of a person, mental health issues sometimes go unattended. Stress, anxiety, and depression are becoming increasingly common problems in our community leading to serious heart-related problems such as high blood pressure, episodes of heart attack and can even lead to chronic illness. Prediction of stress or depression at an earlier stage can prevent serious consequences as sometimes patients suffering from mental illness are not aware of the severity of their condition or do not keep up with counseling for a longer period of time. In this context this paper proposes a stress prediction method using machine learning to detect the development of stress or anxiety problems at an early stage. Our proposed method observes any changes in the human body under stress or depression by monitoring the ECG values and other physiological factors to predict any kind of possible stress or depression. The proposed model provided high accuracy of 98% in predicting stress. On detecting stress, appropriate actions such as informing the patient's guardian and doctor are taken. As compared with other models, our model outperforms the other state of the art models, making it a real-world predication model.
Journal ArticleDOI
TL;DR: In this article , a Kernel-based Ensemble Meta Classifier (KEMC) strategy is proposed to choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, and particle swarm optimization and genetic algorithm intelligent optimization algorithms are used to establish the ideal order.
Abstract: Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. Security guards have used honeypots successfully in several computer security defence systems. Honeypots are frequently utilised in botnet defence because they can draw botnet compromises, reveal spies in botnet membership, and deter attacker behaviour. Attackers who build and maintain botnets must devise ways to avoid honeypot traps. Machine learning methods support identification and inhibit bot threats to address the problems associated with botnet attacks. To choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, a Kernel-based Ensemble Meta Classifier (KEMC) Strategy is suggested in this work. And particle swarm optimization (PSO) and genetic algorithm (GA) intelligent optimization algorithms are used to establish the ideal order. The model covered in this paper is employed to forecast Internet cyber security circumstances. The Binary Cross-Entropy (loss), the GA-PSO optimizer, the Softsign activation functions and ensembles were used in the experiment to produce the best results. The model succeeded because Forfileless malware, gathered from well-known datasets, achieved a total accuracy of 93.3% with a True Positive (TP) Range of 87.45% at zero False Positive (FP).