The transmission of users’ personal data via networks poses an increased risk of being exposed to phishing threats. Phishing URLs hiding malicious web addresses have become a widespread means of compromising user pri...
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The transmission of users’ personal data via networks poses an increased risk of being exposed to phishing threats. Phishing URLs hiding malicious web addresses have become a widespread means of compromising user privacy. This paper is to improve the accuracy and efficiency of detecting and defending against phishing attacks in multimedia applications, aiming to safeguard users'personal data from exposure via phishing URLs. The proposed approach can be summarized in three phases. In the first phase, cosine and Jaccard similarities between training and test datasets are calculated to obtain a comprehensive understanding of the similarities of data points. A hybrid similarity score is built by combining these two measures in the second phase of the experiment to effectively blend content-related and quantity-related similarities. The third and final phase involves training a model on XGBoost that uses a hybrid similarity score as an input and then tunning up its hyperparameters using a genetic algorithm with the optimization of classification performance. Finally, the created powerful classifiers are merged to form a stacked ensemble model, increasing the overall accuracy of the classification. The strenuous test evaluation of 11,429 rows, 88 columns, through key metrics, such as Precision, Recall, F-Score, and Accuracy show that the proposed anti-phishing model has outperformance parameters in other models. With this lean approach, accuracy in classification can be improved by the incorporation of similarity-based techniques and machine learning and evolutionary optimization, in which defense can be performed against phishing threats in multimedia environments. The new innovation involved in the proposed work is based on stacking an ensemble of similarity-based techniques with a learning and optimization framework of machines, ensuring robustness toward phishing threat defense.
Heart disease continues to be a major worldwide health problem, taking many lives annually. Reducing death rates and moving forward persistent results depend heavily on early recognizable proof and anticipation. Conve...
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ISBN:
(数字)9798331528713
ISBN:
(纸本)9798331528720
Heart disease continues to be a major worldwide health problem, taking many lives annually. Reducing death rates and moving forward persistent results depend heavily on early recognizable proof and anticipation. Conventional techniques for diagnosing cardiac disease can be time-consuming and can require intrusive strategies. Machine learning has ended up a practical strategy for handling these issues in later a long time. Machine learning calculations are able of distinguishing patterns and making exceptionally exact forecasts almost the possibility of cardiac infection by assessing enormous databases of persistent data. This study looks into how machine learning may be utilized to estimate the hazard of heart infection depending on distinctive quiet characteristics. A huge dataset was utilized, which included details approximately blood weight, cholesterol, age, gender, smoking status, and the presence of certain medical disorders. Through the utilize of cutting-edge machine learning strategies such as Random Forest, Support Vector Machine, and Neural Networks, our goal was to make models that may dependably distinguish individuals as either tall or low chance heart malady patients. The comes about of this think about illustrate how machine learning has the capacity to totally change the field of heart illness anticipation. Healthcare experts can act early with focused interventions, such as pharmaceutical, lifestyle changes, or more advanced demonstrative strategies, by accurately recognizing those who are at high risk. This individualized approach to treatment can lower the overall burden of heart illness and enormously upgrade understanding results. This study appears how well machine learning can anticipate the hazard of heart infection. In specific, the Random Forest demonstrate turned out to be a valuable asset for deciding which individuals would benefit from early mediation. We may expect the advancement of continuously more complex and precise models as ma
Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection Systems (IDS) ...
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With the increase in the popularity of the social media sites, there arises lot of adverse effects including the spread of fake news. This research study proposes a novel algorithm, called DeepTweet, which leverages t...
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This work assesses the advancement achieved in cross-domain Chinese text categorization through implementing FL. Audible, explicit, and traditional methods of centralized text categorization present severe privacy and...
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Energy is utilized in a different form across the globe, which includes electrical and mechanical energy. To increase the efficiency of renewable energy, which comes from a variety of sources, several researchers have...
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Training in the area of CCI has become a current topic of debate. It is necessary to address the specificities related to this target audience in terms of ethical aspects, evaluation techniques and instruments and the...
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In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage...
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Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when sea...
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The vast majority of automobiles now have intelligent controllers. During the hot summer months, the interior temperature of the car rapidly rises, necessitating the use of the air conditioner to maintain comfort. Thi...
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