An Efficient Machine Learning Method to Prevent IOT Cyber Attacks
DOI:
https://doi.org/10.32628/IJSRST251365Keywords:
IoT Cybersecurity, Machine Learning, Cyberattacks, Anomaly Detection, Threat Prediction, Network Defence, Vulnerability Exploitation, Adversarial Machine LearningAbstract
This abstract explores a double-edged sword: the potential for machine learning to both empower and threaten IoT cybersecurity. On the one hand, machine learning algorithms can be harnessed to analyze vast amounts of data collected from IoT devices. This analysis can unearth hidden patterns in network traffic, identify anomalies indicative of cyberattacks, and predict future threats. By implementing such machine learning models, we can proactively strengthen IoT network defenses and minimize the impact of potential attacks. However, the same machine learning techniques could be exploited by malicious actors to launch more sophisticated cyberattacks. Adversaries could train algorithms to exploit vulnerabilities in IoT devices or networks, potentially bypassing traditional security measures. Therefore, it's crucial to acknowledge the potential for misuse while harnessing the power of machine learning for robust IoT cybersecurity.
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