A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence

Koloveas, Paris; Chantzios, Thanasis; Tryfonopoulos, Christos; Skiadopoulos, Spiros

doi:10.1109/services.2019.00016

Cited by 31 publications

(14 citation statements)

References 32 publications

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“…The Cyber Threat Intelligence (CTI) is based on the information gathered about robotic threats and threat actors which would help in mitigating harmful cyber-events based on the Advanced Persistent Threat (APT) concept through early detection and prevention. In fact, CTI sources include information gathered from HUMman INTelligence (HUMINT), Open Source INTelligence (OSINT), TECHnical INTelligence (TECHINT) and intelligence gathered from the dark web (silk road) [232,233]. This allows an enhancement in the robotic domain via an evidence-based malware analy- CIT includes three intelligence types that can be described as follows:…”

Section: Cyber Threat Intelligencementioning

confidence: 99%

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Yaacoub

Noura

Salman

et al. 2021

Int. J. Inf. Secur.

122

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The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.

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Section: Cyber Threat Intelligencementioning

confidence: 99%

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Yaacoub

Noura

Salman

et al. 2021

Int. J. Inf. Secur.

122

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“…Methodologically we followed a four-stage approach to developing INTIME: (i) in the analysis phase we explored the state-of-the-art in cyber-security and CTI management, defined use case scenarios and gathered end-user requirements, (ii) in the research phase we designed advanced novel methods that allowed INTIME to achieve its mission regarding proactive technologies and cyber-attack detection, (iii) in the development phase we implemented, integrated, and tested the tools, components, and services of the platform, and (iv) in the validation phase we developed initial evaluation plans on real-world datasets and scenarios, and designed the more detailed evaluation of the individual components. The first iteration of this architecture was initially developed by Koloveas et al [96], while specifically focusing on the crawling and ranking tasks. In this section, we outline the architecture of the proposed system, as illustrated in Figure 1.…”

Section: System Architecturementioning

confidence: 99%

inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence

et al. 2021

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In today’s world, technology has become deep-rooted and more accessible than ever over a plethora of different devices and platforms, ranging from company servers and commodity PCs to mobile phones and wearables, interconnecting a wide range of stakeholders such as households, organizations and critical infrastructures. The sheer volume and variety of the different operating systems, the device particularities, the various usage domains and the accessibility-ready nature of the platforms creates a vast and complex threat landscape that is difficult to contain. Staying on top of these evolving cyber-threats has become an increasingly difficult task that presently relies heavily on collecting and utilising cyber-threat intelligence before an attack (or at least shortly after, to minimize the damage) and entails the collection, analysis, leveraging and sharing of huge volumes of data. In this work, we put forward inTIME, a machine learning-based integrated framework that provides an holistic view in the cyber-threat intelligence process and allows security analysts to easily identify, collect, analyse, extract, integrate, and share cyber-threat intelligence from a wide variety of online sources including clear/deep/dark web sites, forums and marketplaces, popular social networks, trusted structured sources (e.g., known security databases), or other datastore types (e.g., pastebins). inTIME is a zero-administration, open-source, integrated framework that enables security analysts and security stakeholders to (i) easily deploy a wide variety of data acquisition services (such as focused web crawlers, site scrapers, domain downloaders, social media monitors), (ii) automatically rank the collected content according to its potential to contain useful intelligence, (iii) identify and extract cyber-threat intelligence and security artifacts via automated natural language understanding processes, (iv) leverage the identified intelligence to actionable items by semi-automatic entity disambiguation, linkage and correlation, and (v) manage, share or collaborate on the stored intelligence via open standards and intuitive tools. To the best of our knowledge, this is the first solution in the literature to provide an end-to-end cyber-threat intelligence management platform that is able to support the complete threat lifecycle via an integrated, simple-to-use, yet extensible framework.

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“…Koloveas et al [46] used classification and language modeling methods to support the crawling tasks by representing the collected information in a latent low-dimensional feature space, to analyze the content relevant to a specific hacking topic (IoT in the proposed study).…”

Section: Performance and Optimization Deliu Et Al [44]mentioning

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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A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence

Cited by 31 publications

References 32 publications

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence

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

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