Exploring and Identifying Malicious Sites in Dark Web Using Machine Learning

Influence of COVID-19 Epidemic on Dark Web Contents

10.20944/preprints202110.0165.v1 ◽

2021 ◽

Author(s):

Abdul Razaque ◽

Bakhytzhan Valiyev ◽

Bandar Alotaibi ◽

Munif Alotaibi ◽

Saule Amanzholova ◽

...

Keyword(s):

Machine Learning ◽

Technical Progress ◽

Confidential Information ◽

Web Contents ◽

Global Events ◽

Dark Web

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

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Cyber Threat Discovery from Dark Web

10.29007/nkfk ◽

2019 ◽

Cited By ~ 1

Author(s):

Azene Zenebe ◽

Mufaro Shumba ◽

Andrei Carillo ◽

Sofia Cuenca

Keyword(s):

Machine Learning ◽

Random Forest Classifier ◽

Online Forums ◽

Security Breaches ◽

Threat Intelligence ◽

Cyber Threat ◽

Ibm Watson ◽

Cyber Threat Intelligence ◽

Dark Web ◽

Machine Learning Models

In the darknet, hackers are constantly sharing information with each other and learning from each other. These conversations in online forums for example can contain data that may help assist in the discovery of cyber threat intelligence. Cyber Threat Intelligence (CTI) is information or knowledge about threats that can help prevent security breaches in cyberspace. In addition, monitoring and analysis of this data manually is challenging because forum posts and other data on the darknet are high in volume and unstructured. This paper uses descriptive analytics and predicative analytics using machine learning on forum posts dataset from darknet to discover valuable cyber threat intelligence. The IBM Watson Analytics and WEKA machine learning tool were used. Watson Analytics showed trends and relationships in the data. WEKA provided machine learning models to classify the type of exploits targeted by hackers from the form posts. The results showed that Crypter, Password cracker and RATs (Remote Administration Tools), buffer overflow exploit tools, and Keylogger system exploits tools were the most common in the darknet and that there are influential authors who are frequent in the forums. In addition, machine learning helps build classifiers for exploit types. The Random Forest classifier provided a higher accuracy than the Random Tree and Naïve Bayes classifiers. Therefore, analyzing darknet forum posts can provide actionable information as well as machine learning is effective in building classifiers for prediction of exploit types. Predicting exploit types as well as knowing patterns and trends on hackers’ plan helps defend the cyberspace proactively.

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Influence of COVID-19 Epidemic on Dark Web Contents

Electronics ◽

10.3390/electronics10222744 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2744

Author(s):

Abdul Razaque ◽

Bakhytzhan Valiyev ◽

Bandar Alotaibi ◽

Munif Alotaibi ◽

Saule Amanzholova ◽

...

Keyword(s):

Machine Learning ◽

Technical Progress ◽

Confidential Information ◽

Web Contents ◽

Global Events ◽

Dark Web

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA) in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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