Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTI and the concealment of relations. To improve the performance of CTI relation extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Additionally, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation called Cyber Threat Intelligence Analysis (CTIA). Inspired by Generative Adversarial Networks, we integrate contextual semantics to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context-embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect in the cybersecurity domain. We also took general knowledge graph completion tests. The results demonstrate that AT4CTIRE outperforms existing methods with improved extraction accuracy and a remarkable expedited training convergence rate....
With the rapid development of network technology, information security is facing increasingly complex challenges. Deep learning technology, due to its strong capabilities in data processing and pattern recognition, has become a key technology to improve the detection efficiency and accuracy in the field of information security. This paper delves into the application of deep learning in various aspects such as malware detection, network intrusion identification, User and Entity Behavior Analytics (UEBA), privacy protection technology, model explainability, and network security vulnerability detection, and proposes deep learning-based information security methods. Through experimental validation, our methods have outperformed traditional machine learning models in multiple evaluation metrics, providing new solutions for the field of information security. In the future, we will continue to explore new applications of deep learning in the field of information security to cope with the ever-changing network security threats....
Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. The phishing attacker spreads messages containing false URL links through emails, social media platforms, or messages, targeting people to steal sensitive data like credentials. Attackers generate phishing URLs that resemble those of legitimate websites to gain these confidential data. Hence, there is a need to prevent the siphoning of data through the duplication of trustworthy websites and raise public awareness of such practices. For this purpose, many machine learning and deep learning models have been employed to detect and prevent phishing attacks, but due to the ever-evolving nature of these attacks, many systems fail to provide accurate results. In this study, we propose a deep learning-based system using a 1D convolutional neural network to detect phishing URLs. The experimental work was performed using datasets from Phish-Tank, UNB, and Alexa, which successfully generated 200 thousand phishing URLs and 200 thousand legitimate URLs. The experimental results show that the proposed system achieved 99.7% accuracy, which was better than the traditional models proposed for URL-based phishing detection....
The high rate of growth in the number of IoT devices has resulted in more than a billion interconnected things exchanging data, creating new security threats. Traditional security, when facing advanced cyber-attacks, especially in the era of quantum computing, is getting weaker. This paper explains novel way methods, a combination of post-quantum blockchain technology and deep learning to improve security on IoT networks. With the correct preparations in place, such as implementing post-quantum cryptography, which is secure against quantum attacks, your data remains confidential, and integrity-related issues are protected. It is a distributed framework that blockchain technology has been using to secure IoT communications since tamper resistance and transparency in the environment are key. At the same time, deep learning algorithms capable of processing large amounts of data allow for more sophisticated ways to detect and respond to threats quicker than before. In this article, we will explain how a mixture of these technologies can be applied in the framework that allows building such robust cyber defense systems for IoT networks. Post-quantum blockchain is integrated for secure communication channels and immutable transaction records, ongoing traffic monitoring using deep learning models that are able to dynamically update threat detection signatures instantly. We perform an in-depth system architecture analysis, illustrating blockchain's decentralized security and deep learning predictive analytics. The possibility of a practical integration received 95 percent success. The paper evaluates PQCrypto, Blockchain, and Deep Learning technically to get quantized accuracy, efficiency, and the possibility of a practical integration. It received 95% percent success....
In today’s interconnected world, the ubiquitous influence of digital technology emphasizes the critical need to confront the growing menace of cybercrime. The unrelenting rise of cyber-attacks in the United States poses substantial hazards to individuals, corporations, and nations, jeopardizing economic stability, security, and personal privacy. This study aims to draw awareness of the seriousness of cyber dangers and emphasize employing proactive steps to protect our digital future. This paper stresses the necessity of cybersecurity, digital literacy education, and cybercrime awareness in mitigating these widespread hazards through extensive analysis and empirical research. A more resilient and secure digital environment for current and future generations can be established by cultivating a collective understanding of cyber dangers and developing effective preventative measures....
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