The MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures. Its innovative approach has been broadly welcomed by both vendors and enterprise customers in the industry. Its usage extends from adversary emulation, red teaming, behavioral analytics development to a defensive gap and SOC (Security Operations Center) maturity assessment. While extensive research has been done on analyzing specific attacks or specific organizational culture and human behavior factors leading to such attacks, a holistic view on the association of both is currently missing. In this paper, we present our research results on associating a comprehensive set of organizational and individual culture factors (as described on our developed cyber-security culture framework) with security vulnerabilities mapped to specific adversary behavior and patterns utilizing the MITRE ATT&CK framework. Thus, exploiting MITRE ATT&CK’s possibilities towards a scientific direction that has not yet been explored: security assessment and defensive design, a step prior to its current application domain. The suggested cyber-security culture framework was originally designed to aim at critical infrastructures and, more specifically, the energy sector. Organizations of these domains exhibit a co-existence and strong interaction of the IT (Information Technology) and OT (Operational Technology) networks. As a result, we emphasize our scientific effort on the hybrid MITRE ATT&CK for Enterprise and ICS (Industrial Control Systems) model as a broader and more holistic approach. The results of our research can be utilized in an extensive set of applications, including the efficient organization of security procedures as well as enhancing security readiness evaluation results by providing more insights into imminent threats and security risks.

In today’s fast-paced world, the rates of stress and depression present a surge. People use social media for expressing their thoughts and feelings through posts. Therefore, social media provide assistance for the early detection of mental health conditions. Existing methods mainly introduce feature extraction approaches and train shallow machine learning (ML) classifiers. For addressing the need of creating a large feature set and obtaining better performance, other research studies use deep neural networks or language models based on transformers. Despite the fact that transformer-based models achieve noticeable improvements, they cannot often capture rich factual knowledge. Although there have been proposed a number of studies aiming to enhance the pretrained transformer-based models with extra information or additional modalities, no prior work has exploited these modifications for detecting stress and depression through social media. In addition, although the reliability of a machine learning (ML) model’s confidence in its predictions is critical for high-risk applications, there is no prior work taken into consideration the model calibration. To resolve the above issues, we present the first study in the task of depression and stress detection in social media, which injects extra-linguistic information in transformer-based models, namely, bidirectional encoder representations from transformers (BERT) and MentalBERT. Specifically, the proposed approach employs a multimodal adaptation gate for creating the combined embeddings, which are given as input to a BERT (or MentalBERT) model. For taking into account the model calibration, we apply label smoothing. We test our proposed approaches in three publicly available datasets and demonstrate that the integration of linguistic features into transformer-based models presents a surge in performance. Also, the usage of label smoothing contributes to both the improvement of the model’s performance and the calibration of the model. We finally perform a linguistic analysis of the posts and show differences in language between stressful and nonstressful texts, as well as depressive and nondepressive posts.

This paper presents a cyber-security culture framework for assessing and evaluating the current security readiness of an organization’s workforce. Having conducted a thorough review of the most commonly used security frameworks, we identify core security human-related elements and classify them by constructing a domain agnostic security model. We then proceed by presenting in detail each component of our model and attempt to quantify them in order to achieve a feasible assessment methodology. The paper thereafter presents the application of this methodology for the design and development of a security culture evaluation tool, that offers recommendations and alternative approaches to workforce training programs and techniques. The model has been designed to easily adapt on various application domains while focusing on their unique characteristics. The paper concludes on applications of our instrument on security-critical domains, and its contribution to current research by providing deeper insights regarding the human factor in cybersecurity.

Insider threat has been recognized by both scientific community and security professionals as one of the gravest security hazards for private companies, institutions, and governmental organizations. Extended research on the types, associated internal and external factors, detection approaches and mitigation strategies has been conducted over the last decades. Various frameworks have been introduced in an attempt to understand and reflect the danger posed by this threat, whereas multiple identified cases have been classified in private or public databases. This paper aims to present how a cyber-security culture framework with a clear focus on the human factor can assist in detecting possible threats of both malicious and unintentional insiders. We link current insider threat categories with specific security domains of the framework and introduce an assessment methodology of the core contributing parameters. Specific approach takes into consideration technical, behavioral, cultural, and personal indicators and assists in identifying possible security perils deriving from privileged individuals.