Abstract

Cyberattacks pose a danger to the security and integrity of digital ecosystems, hence proactive defensive measures are required to lower risks and safeguard essential resources. The machine learning (ML) and deep learning (DL) techniques utilized in cyber threat prediction are thoroughly examined in this work. Through a thorough synthesis of the literature, this study explores the state-of-the-art, new trends, and potential futures in cyber threat prediction. According to research, a number of machine learning (ML) and deep learning (DL) techniques are employed for cyber threat prediction, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs). The study uses a range of datasets assessment criteria to show the benefits and downsides of various ways for detecting and preventing cyberattacks in various threat scenarios. The significance of multidisciplinary cooperation, methodological innovation, and ethical concerns in improving the area of cyber threat prediction are among the key conclusions drawn from the literature synthesis. Problems including a lack of data, unequal class distribution, and hostile attacks show up as major roadblocks to the creation of trustworthy and effective prediction models. Future research needs are noted, though, and these include standardizing assessment methods, integrating domain knowledge, and developing strong defenses against adversarial assaults. Overall, the study highlights the importance of sophisticated analytics in boosting cybersecurity resilience and the necessity of constant innovation and adaptation to keep up with changing cyberthreats. Policymakers, researchers, and cybersecurity experts may collaborate to improve the security posture of digital infrastructure and reduce new cyber threats by utilizing the research’s insights.