arXiv subjects

Nicholas Kolokotronis

Publications and source records attributed to Nicholas Kolokotronis.

25 records · Page 2Linked to original sources

arXiv2019

A machine-learning approach to Detect users' suspicious behaviour through the Facebook wall

Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.

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arXiv2019

Secured by Blockchain: Safeguarding Internet of Things Devices

Blockchain is a disruptive technology that has been characterised to be the next big thing and has already gained a broad recognition by experts in diverse fields. In this paper, we consider possible use cases and applications of the blockchain for the consumer electronics (CE) industry and its interplay with the Internet of things. Instead of discussing how the blockchain can revolutionise the supply chain, we focus on how it could be employed for enhancing the security of networked CE devices. This work is motivated by the large number of recent attacks that use easily hackable devices as a weaponry. Towards this direction, privacy and data protection aspects of blockchain solutions are also presented and are linked to regulatory framework provisions. Information on existing blockchain solutions is also provided.

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arXiv2019

A Novel Malware Detection System Based On Machine Learning and Binary Visualization

The continued evolution and diversity of malware constitutes a major threat in modern systems. It is well proven that security defenses currently available are ineffective to mitigate the skills and imagination of cyber-criminals necessitating the development of novel solutions. Deep learning algorithms and artificial intelligence (AI) are rapidly evolving with remarkable results in many application areas. Following the advances of AI and recognizing the need for efficient malware detection methods, this paper presents a new approach for malware detection based on binary visualization and self-organizing incremental neural networks. The proposed method's performance in detecting malicious payloads in various file types was investigated and the experimental results showed that a detection accuracy of 91.7% and 94.1% was achieved for ransomware in .pdf and .doc files respectively. With respect to other formats of malicious code and other file types, including binaries, the proposed method behaved well with an incremental detection rate that allows efficiently detecting unknown malware at real-time.

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arXiv2019

Blockchain Solutions for Forensic Evidence Preservation in IoT Environments

The technological evolution brought by the Internet of things (IoT) comes with new forms of cyber-attacks exploiting the complexity and heterogeneity of IoT networks, as well as, the existence of many vulnerabilities in IoT devices. The detection of compromised devices, as well as the collection and preservation of evidence regarding alleged malicious behavior in IoT networks emerge as a areas of high priority. This paper presents a blockchain-based solution, which is designed for the smart home domain, dealing with the collection and preservation of digital forensic evidence. The system utilizes a private forensic evidence database, where the captured evidence is stored, along with a permissioned blockchain that allows providing security services like integrity, authentication, and non-repudiation, so that the evidence can be used in a court of law. The blockchain stores evidences' metadata, which are critical for providing the aforementioned services, and interacts via smart contracts with the different entities involved in an investigation process, including Internet service providers, law enforcement agencies and prosecutors. A high-level architecture of the blockchain-based solution is presented that allows tackling the unique challenges posed by the need for digitally handling forensic evidence collected from IoT networks.

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arXiv2019

Data Protection by Design for Cybersecurity Systems in a Smart Home Environment

The present paper deals with the elucidation and implementation of the Data Protection by Design (DPbD) principle as recently introduced in the European Union data protection law, specifically with regards to cybersecurity systems in a Smart Home environment, both from a legal and a technical perspective. Starting point constitutes the research conducted in the Cyber-Trust project, which endeavours the development of an innovative and customisable cybersecurity platform for cyber-threat intelligence gathering, detection and mitigation within the Internet of Things ecosystem. During the course of the paper, the requirements of DPbD with regards to the conceptualisation, design and actual development of the system are presented as prescribed in law. These requirements are then translated into technical solutions, as envisaged in the Cyber-Trust system. For trade-offs are not foreign to the DPbD context, technical limitations and legal challenges are also discussed in this interdisciplinary dialogue.

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arXiv2019

A Novel Blockchain-based Trust Model for Cloud Identity Management

Secure and reliable management of identities has become one of the greatest challenges facing cloud computing today, mainly due to the huge number of new cloud-based applications generated by this model, which means more user accounts, passwords, and personal information to provision, monitor, and secure. Currently, identity federation is the most useful solution to overcome the aforementioned issues and simplify the user experience by allowing efficient authentication mechanisms and use of identity information from data distributed across multiple domains. However, this approach creates considerable complexity in managing trust relationships for both the cloud service providers and their clients. Poor management of trust in federated identity management systems brings with it many security, privacy and interoperability issues, which contributes to the reluctance of organizations to move their critical identity data to the cloud. In this paper, we aim to address these issues by introducing a novel trust and identity management model based on the Blockchain for cloud identity management with security and privacy improvements.

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arXiv2019

Are cracked applications really free? An empirical analysis on Android devices

Android is among the popular platforms running on millions of smart devices, like smartphones and tablets, whose widespread adoption is seen as an opportunity for spreading malware. Adding malicious payloads to cracked applications, often popular ones, downloaded from untrusted third markets is a prevalent way for achieving the aforementioned goal. In this paper, we compare 25 applications from the official and third-party application stores delivering cracked applications. The behavioral analysis of applications is carried out on three real devices equipped with different Android versions by using five indicators: requested permissions, CPU usage, RAM usage and the number of opened ports for TCP and HTTP. Based on these indicators, we compute an application intention score and classify cracked applications as malicious or benign. The experimental results show that cracked applications utilize on average more resources and request access to more (dangerous) permissions than their official counterparts.

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