# Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
## 1 Introduction
This number has only risen since then, due to the Internet's rapid expansion. Navigating the Darknet layer necessitates the use of specialized tools and applications. It cannot be accessed without them. The Darknet, on the other hand, is where consumers are likely to find their stolen files and compromised data for sale, as well as any other unauthorized product for sale. This layer is built on peer-to-peer computer networking and routing communications via so much uncertainty that monitoring is almost impossible. That is why there are so many unauthorized activities taking place within it. One of the configurations on which the Darknet is built is a Tor network [11], which employs an onion routing protocol. Tor software allows users to connect to the Darknet and communicate/browse over huge peer-to-peer connections. To offer security, large obscurity and constantly changing channels, circuits, and connections are employed. Figure 2 depicts the basic concept of Darknet utilization of TorNet. According to the figure, in the onion routing protocol, the data to be sent are encapsulated in encryption layers, similar to the layers of an onion [11]. The encrypted data are then sent via a succession of network nodes known as onion routers, each of which \"peels\" away (or decrypts) a single layer of encryption, revealing the data's next destination. The data are delivered to their destination when the last layer is decrypted. Because each intermediate only knows the position of the nodes immediately before and after it, the sender remains anonymous [11]. Because of the Darknet's huge scale and reach, more effective measures for mitigating its potential threats are required. Modern methods must be utilized to trace down the black market and its transactions in order to grasp the criminals. The unindexed, fragmented, and multi-layered structure of the Darknet makes it more difficult to locate the criminals therein. Forensic law enforcement agencies want strong digital evidence to overcome the problems of finding and prosecuting criminals in the Dark Web environment. There are several relevant studies on IoT in the literature that address various areas of machine learning in cybersecurity. Many of the latest cybersecurity solutions were created by combining machine learning with cybersecurity. The majority of new IoT intrusions are minor modifications from previously known vulnerabilities [12]. These little variations in IoT attacks have been shown to be difficult to identify/classify using typical machine-learning approaches. Deep neural networks have been used in promising state-of-the-art cybersecurity research [13; 14; 15; 16; 17]. Table 1 highlights the standard and traditional machine-learning research [18]. Figure 2: Most popular IoT architecture. The improved IoT security (based on IoT-specific threats) methodology used in a few studies [19; 20; 21; 22; 23] gave a comprehensive background on all IoT areas and provided a comprehensive background on all IoT areas. Those studies did not, however, explore the areas of Darknet-based IDS for IoT in the same way that we did. The study in Table 2 demonstrates the important nature of anomaly-based intrusion detection systems for the security of things [24]. To help illustrate their solution context and process, this table shows gaps in prior survey studies inside the conventional architectural layers of IoT systems and links them with IDSs, such as anomaly-based IDS. While improved intelligent intrusion detection systems for IoT networks have been significantly investigated and proposed in the literature, only a few studies have explored the areas of Darknet intrusion (blackholes or sinkholes) detection systems for IoT networks. Therefore, in this paper, we propose an efficient machine-learning-based system for identifying and classifying Darknet traffic for IoT networks.