@article {831,
	title = {Blockchain meets Internet of Things (IoT) forensics: A unified framework for IoT ecosystems},
	journal = {Internet of Things},
	year = {2023},
	month = {oct},
	pages = {100968},
	abstract = {<p>With the continuously increasing number of security incidents targeting sensors, embedded devices, and other electronics appliances and systems that comprise the so-called Internet of things (IoT), the field of IoT forensics emerged as a branch of digital forensics focusing on the investigation of attacks in the IoT ecosystem. Digital evidence authenticity, integrity, confidentiality, and privacy are among the factors affecting the investigation process in IoT forensics, which can be addressed by employing the blockchain as the means not only for the preservation of digital evidence, but also for other steps of a digital forensics investigation process. The high-level goal of the paper is three-fold: to establish a holistic IoT forensics process as a reference point against which blockchain integration patterns and best practices will be identified in order to yield a robust, widely accepted and scalable architecture of the blockchain-enabled solution; to integrate the blockchain with the proposed IoT forensics process for addressing the above challenges; and to evaluate the overall solution highlighting improvements and potential performance bottlenecks. The proposed blockchain-enabled platform leverages multi-access edge computing and has been implemented with Hyperledger Fabric on an extensive virtualized testbed providing a realistic smart home environment. A thorough evaluation was conducted with real cyber-attacks generating digital evidence at high rates for testing the platform{\textquoteright}s behavior at high load. The experimental results showed that the proposed platform achieved high throughput, excessively low latency, and zero error rate in the operation of the blockchain network.</p>
},
	keywords = {blockchain, cyber-security, Digital forensics, Edge computing, Internet of Things},
	doi = {10.1016/j.iot.2023.100968},
	url = {https://www.sciencedirect.com/science/article/pii/S2542660523002913},
	author = {Sotirios Brotsis and Konstantinos P. Grammatikakis and Dimitrios Kavallieros and Antonio I. Mazilu and Nicholas Kolokotronis and Konstantinos Limniotis and Costas Vassilakis}
}
@conference {591,
	title = {A crawler architecture for harvesting the clear, social, and dark web for IoT-related cyber-threat intelligence},
	booktitle = {In Proceedings of the IEEE Workshop on Cyber Security \& Resilience in the Internet of Things (CSRIoT @ IEEE Services)},
	year = {2016},
	month = {July 2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Milan, Italy},
	abstract = {The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that {\textendash}given the appropriate tools and methods{\textendash} may be identified, crawled and subsequently leveraged to actionable cyber- threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker fo- rums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state- of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.},
	keywords = {crawling architecture, cyber-security, cyber-threat intelligence, IoT, language models, Machine Learning},
	url = {http://users.uop.gr/ trifon/papers/pdf/csriot19-KCTS.pdf},
	author = {Paris Koloveas and Thanasis Chantzios and Christos Tryfonopoulos and Spiros Skiadopoulos}
}
