Dark web forum correlation analysis research

Yang, Ying; Yang, Lina; Yang, Meihong; Yu, Huanhuan; Zhu, Guichun; Chen, Zhenya; Chen, Lijuan

doi:10.1109/itaic.2019.8785760

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…In addition, there is research dedicated to network traffic classification [22][23][24], whereas the Internet of Things (IoT) has recently attracted a significant amount of attention in machine learning and in network traffic analysis [13,15,16,25]. Yang et al [26] introduce the current mainstream dark network communication system TOR and develop a visual dark web forum post association analysis system to graphically display the relationship between various forum messages and posters, which helps analysts to explore deep levels. In addition, another paper [14] designs a framework based on Hadoop in hidden threat intelligence.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…The part of the internet that is not accessible by search engines is also known as the Deep Web. Darknet is a subset of the Deep Web in the sense that it is also undetectable by search engines, but it also has one characteristic [21]. Anonymous communication networks -commonly referred to as Darknets -are becoming increasingly popular with criminals for trafficking drugs, weapons, fake IDs, theft, and child pornography.…”

Section: Tor Hidden Services On Darknetmentioning

confidence: 99%

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Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

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Section: Literature Reviewsmentioning

confidence: 99%

Section: Tor Hidden Services On Darknetmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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“…As far as limitations are concerned no in-depth detailed analysis of the encryption principle and anonymous implementation of the I2P protocol. Whereas in [16], authors presented I2P characteristics to point out the security flaws and problems finding artifacts of the I2P. The results shows that the I2P artifacts collected and analyzed on the basis of vulnerabilities of I2P networks and specific security issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Windows 10's Browser Forensic Analysis for Tracing P2P Networks’ Anonymous Attacks

Kauser¹,

Malik²,

Hasan³

et al. 2022

Computers, Materials &Amp; Continua

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A web browser is the most basic tool for accessing the internet from any of the machines/equipment. Recently, data breaches have been reported frequently from users who are concerned about their personal information, as well as threats from criminal actors. Giving loss of data and information to an innocent user comes under the jurisdiction of cyber-attack. These kinds of cyber-attacks are far more dangerous when it comes to the many types of devices employed in an internet of things (IoT) environment. Continuous surveillance of IoT devices and forensic tools are required to overcome the issues pertaining to secure data and assets. Peer to peer (P2P) applications have been utilized for criminal operations on the web. Therefore, it is a challenge for a forensic investigator to perform forensic analysis of the evolving hardware and software platforms for IoT. For identity concealment and privacy protection, the Onion Router (Tor) and Chrome with the Invisible Internet Project (I2P) as the foundation browser are often used. Confirmation is required to determine whether Tor is truly anonymous and private as they claim. Some people, on the other hand, utilize the Tor browser for evil reasons. Tools and techniques are available for the collection of artifacts, identifying problem areas, further processing and analysis of data on the computer and IoT. Present research tried to explore a few tools for the tracing of I2P activities over computer on windows 10 that reflects IoT devices. According to the results of this research, it leaves an excessive amount of important digital evidence on the operating system that can be exploited to attack the information of users. This research is based on windows operating system and does not support other operating systems.

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“…A notice indicates that immediate action is required or is merely informative. Categorizing the different types of alerts is useful and makes it possible to develop a monitoring strategy [35]. Notifications are categorized according to the level of completion of the relevant research, conducted by managers and analysts.…”

Section: Visualization and User Interfacementioning

confidence: 99%