… the threats and attacks faced by machine learning systems. As shown in Fig. 2, to date, all the
security threats along the life cycle of machine learning … privacy-preserving solutions [120]. …

… threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning
solutions… , which is built on machine and deep learning technologies that extract insights from …

… With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

… Section 4 presents the latest solutions to IoT security and privacy threats, whereas research
challenges for techniques based on ML and BC to solve security and privacy issues are …

… surrounding data privacy, algorithm bias, and transparency necessitate careful scrutiny to
ensure the responsible and ethical deployment of AI-driven cybersecurity solutions. In light of …

… threats. However, some promising solutions, especially machine learning and deep learning,
are … , which must be considered when proposing solutions against intelligent cyber attacks. …

… We discuss threat model of IoT networks in Section III. In Section IV, we discuss the role
of ML in the IoT networks and briefly review different ML and DL techniques. We survey the …

… We employ machine learning, biometric recognition, data learning, and hybrid approaches
to avoid … biometric prints, which decreases the number of threats that an invader may pose. …

… These learning techniques can detect anomalies or malicious conduct and data-… Machine
learning is a partial but significant departure from traditional well-known security solutions, …

… In this review, the authors defined a general threat model to categorize different attacks
and illustrated their characteristics. The focus of the article was on classification techniques. …