Detecting Cyber Threats in Non-English Dark Net Markets: A Cross-Lingual Transfer Learning Approach

Ebrahimi, Mohammadreza; Surdeanu, Mihai; Chen, Hsinchun

doi:10.1109/isi.2018.8587404

Cited by 20 publications

(11 citation statements)

References 21 publications

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“…Models built using subword tokenizers have achieved good performance on authorship attribution tasks for specific languages (e.g., Polish (Grzybowski et al, 2019)) and also across multilingual social media data (Andrews and Bishop, 2019). Non-English as well as multilingual darknet markets have been increasing in number since 2013 (Ebrahimi et al, 2018). Our work builds upon all these ideas by using CNN models and experimenting with both character and subword level tokens.…”

Section: Authorship Attribution Of Short Textmentioning

confidence: 97%

SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

Maneriker¹,

He²,

Parthasarathy³

2021

Preprint

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Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed, making it difficult to link users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and interaction modeling using graph embeddings to construct lowdimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-theart, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.

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Section: Authorship Attribution Of Short Textmentioning

confidence: 97%

SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

Maneriker¹,

He²,

Parthasarathy³

2021

Preprint

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“…Universal communication is important for sharing techniques and advice. Deep cross-lingual models have been used to jointly learn the common representation from two languages [209]. The model from [209] exceeds the functionality of previous monolingual models previously used to translate non-English cyberthreats.…”

Section: Ai-assisted Threat Managementmentioning

confidence: 99%

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.

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“…By transferring knowledge from English Dark Web marketplaces to non-English ones, Ebrahimi et al [51] proposed an approach to detect cyber threats from non-English marketplaces without the need for mono-or bilingual word embeddings or automatic translation. e system utilizes Deep Cross-Lingual Modeling that simultaneously learns common representations from two languages (English and Russian in the study).…”

Section: Language Variationsmentioning

confidence: 99%

Threats from the Dark: A Review over Dark Web Investigation Research for Cyber Threat Intelligence

Basheer

Alkhatib

2021

Journal of Computer Networks and Communications

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From proactive detection of cyberattacks to the identification of key actors, analyzing contents of the Dark Web plays a significant role in deterring cybercrimes and understanding criminal minds. Researching in the Dark Web proved to be an essential step in fighting cybercrime, whether with a standalone investigation of the Dark Web solely or an integrated one that includes contents from the Surface Web and the Deep Web. In this review, we probe recent studies in the field of analyzing Dark Web content for Cyber Threat Intelligence (CTI), introducing a comprehensive analysis of their techniques, methods, tools, approaches, and results, and discussing their possible limitations. In this review, we demonstrate the significance of studying the contents of different platforms on the Dark Web, leading new researchers through state-of-the-art methodologies. Furthermore, we discuss the technical challenges, ethical considerations, and future directions in the domain.

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Detecting Cyber Threats in Non-English Dark Net Markets: A Cross-Lingual Transfer Learning Approach

Cited by 20 publications

References 21 publications

SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Threats from the Dark: A Review over Dark Web Investigation Research for Cyber Threat Intelligence

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