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Despite long-contested viability, numerous applications still rely upon Advance Encryption Standard (AES) in Counter mode (AES-CTR). Research supports that the vulnerabilities associated with CTR from a mathematical perspective, mainly forgery attempts, stem from misusing the nonce. When paired with cryptographic algorithms, assuming no nonce misuse increases the complexity of unraveling CTR. This paper examines the pairing of CTR with AES-128 (AES-CTR). It includes (1) full key recovery for a software implementation of AES-CTR utilizing a template attack (TA) and (2) enhancing the TA analysis's point of interest (POI) using first-order analysis and known key to identify leaky samples. 

Researchers are looking into solutions to support the enormous demand for wireless communication, which has been exponentially increasing along with the growth of technology. The sixth generation (6G) Network emerged as the leading solution for satisfying the requirements placed on the telecommunications system. 6G technology mainly depends on various machine learning and artificial intelligence techniques. The performance of these machine learning algorithms is high. Still, their security has been neglected for some reason, which leaves the door open to various vulnerabilities that attackers can exploit to compromise systems. Therefore, it is essential to evaluate the security of machine learning algorithms to prevent them from being spoofed by malicious hackers. Prior research has shown that the decision tree is one of the most popular algorithms used by 80% of researchers for classification problems. In this work, we collect the dataset from a laboratory testbed of over 100 Internet of things (IoT) devices. The devices include smart cameras, smart light bulbs, Alexa, and others. We evaluate classifiers using the original dataset during the experiment and record a 98% accuracy. We then use the label-flipping attack approach to poison our dataset and record the output. As a result, flipping 10%, 20%, 30%, 40%, and 50% of the poison data generated accuracies of 86%, 74%, 64%, 54%, and 50%, respectively.