In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.

With the growing internet services, cybersecurity becomes one of the major research problems of the modern digital era. Cybersecurity involves techniques to protect and control the systems, hardware, software, network, and electronic data from unauthorized access. It is necessary to build a cyber-security system to detect different types of attacks. Implementing various intelligent algorithms in cybersecurity led to detect and analyz attack actions occurring in field of computer networks. Cybersecurity uses artificial intelligence, machine learning, and deep learning algorithms capable of extracting optimal feature representation from the big data set. This has been applied to various cybersecurity cases, such as attacks detection, prediction, and analysis. This work aims to perform an analysis of cybersecurity attacks datasets by using intelligent approaches. It also provides a detailed comparison with the performance of algorithms, field implementation to describe network protection optimization technologies benefits.

The vulnerabilities in most web applications enable hackers to gain access to confidential and private information. Structured query injection poses a significant threat to web applications and is one of the most common and widely used information theft mechanisms. Where hackers benefit from errors in the design of systems or existing gaps by not filtering the user's input for some special characters and symbols contained within the structural query sentences or the quality of the information is not checked, whether it is text or numerical, which causes unpredictability of the outcome of its implementation. In this paper, we review PHP techniques and other techniques for protecting SQL from the injection, methods for detecting SQL attacks, types of SQL injection, causes of SQL injection via getting and Post, and prevention technology for SQL vulnerabilities.

since the cybersecurity needs have increased, many companies have implemented awareness-raising campaigns to guarantee their workers are educated about and aware of its risks and safe operation. Technological solutions can not necessarily provide full defense on their own, since employees pose a huge threat to information security and also play an essential part in the willingness of companies to meet security protection criteria. Rising employee knowledge about the security policy of the organization plays a critical role in the successful establishment of policies. The development of diverse and useful security awareness programs remains the best way to increase security awareness. Several approaches have been used to increase security awareness and behavior among users. However, the effectiveness of these approaches remains an open question. Concepts of gamification have become more popular recently and have been used to teach people a wide variety of topics such as security awareness. This paper investigates the most recent researches on cybersecurity awareness. The paper highlights the major obstacles in successfully implementing security awareness among users together with the main factors required to aware of different peoples most effectively. Finally, the paper shows how gamification being used for security awareness among other techniques with the gaps in those methods and suggests recommendations on how to deliver information security awareness effectively.

The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a methodology that involves dataset preprocessing, including data cleaning, encoding, and normalization. The class imbalance is addressed by employing the Synthetic Minority Oversampling Technique (SMOTE) and performing feature reduction using correlation analysis. Multiple ML classifiers, including Logistic Regression, multi-layer perceptron, Decision Trees, Random Forest, and XGBoost, are employed to model IoT attacks. The effectiveness and robustness of the proposed method evaluate using the IoTID20 dataset, which represents current imbalanced IoT scenarios. The results highlight that the XGBoost model, integrated with SMOTE, achieves outstanding attack detection accuracy of 0.99 in binary classification, 0.99 in multi-class classification, and 0.81 in multiple sub-classifications. These findings demonstrate our approach’s significant improvements to attack detection in imbalanced IoT datasets, establishing its superiority over existing IDS frameworks.

Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the main principle of information security, and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed, including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms.