The dark web is an overwhelming and mysterious place that comprises hidden services. Dark web hidden services contain illegal or offensive content. Hidden services are not accessible through regular search engines or browsers and can only be accessed via specific software. The proposed work aims to identify these hidden services by analyzing their associated images and text data. Doing so, one can better understand the types of activities on the dark web and what kind of content is available. First, a dark web crawler is developed to collect dark web services. Images are then manually classified into four categories: Cards, Devices, Hackers, and Money. Next, preprocessing the collected dataset removed irrelevant images, and a Convolutional Neural Network (CNN) was trained to identify new dark web image classes. Finally, quantum Transfer Learning (QTL) improved the model’s performance. The proposed work goes beyond conventional methods of categorizing datasets by including new categories of image classes of dark web hidden services that have not been considered before. Also, the work examines image data and related text to establish a strong correlation between them. The proposed approach will provide insights into the dark web hidden service by confirming the relationship between the image and text data of the respective hidden-services.

The increasing frequency and sophistication of cyberattacks targeting institutions have necessitated proactive measures to prevent losses and mitigate damages. One of these measures is to monitor the dark web. The dark web is a complex network of hidden services and encrypted communication protocols, with the primary purpose of providing anonymity to its users. However, criminals use the dark web to sell stolen data, launch zero-day attacks, and distribute malware. Therefore, identifying suspicious activity on the dark web is necessary for businesses to counter these threats. 
An analysis of dark web monitoring as an emerging trend in cyber security strategy is presented in this article. The article presents a systematic review of (a) why dark web surveillance enhances businesses' cybersecurity strategies, (b) how advanced tools and technologies are used to monitor dark web data in the commercial sector, (c) the key features of threat monitoring frameworks proposed by researchers, and (d) the limitations and challenges associated with dark web monitoring solutions. In summary, the proposed work involves analyzing various sources of information related to the topic and presenting a thorough assessment of the need and challenges of dark web surveillance to enhance the security measures of businesses.