Data policy violation

Attack trees are an important modeling formalism to identify and quantify attacks on security and privacy. They are very useful as a tool to understand step by step the ways through a system graph that lead to the violation of security policies. In this paper, we present how attacks can be refined based on the violation of a policy. To that end we provide a formal definition of attack trees in Isabelle’s Higher Order Logic: a proof calculus that defines how to refine sequences of attack steps into a valid attack. We use a notion of Kripke semantics as formal foundation that then allows to express attack goals using branching time temporal logic CTL. We illustrate the use of the mechanized Isabelle framework on the example of a privacy attack to an IoT healthcare system.

The author examines whether the application of foreign law on forced heirship as specified by the EU Succession Regulation could lead to a violation of German public policy and thus trigger the application of Article 35 of the Regulation on public policy. Particularly, the author explores whether and under what conditions this could happen if the law of another EU Member State, which allows deprivation of forced heirship under circumstances that are not deemed sufficient pursuant to German law, is applicable to a deceased's succession. The problem of a potential violation of public policy due to the application of foreign law on forced heirship is especially significant in Germany due to the (unusual) constitutional protection that attaches to an entitlement to forced heirship and due to the large number of foreigners living in the jurisdiction. The article considers the relevant provisions of the EU Succession Regulation, German public policy doctrine, and the law on deprivation of forced heirship in selected EU Member States.

Inter-domain policy violations in multi-hop overlay routes: Analysis and mitigation

PPCU: Proportional per-packet consistent updates for SDNs using data plane time stamps

Intruders and thieves are important threats to most business and large organizations. These threats and unwanted materials create many disturbances in storing of the data on large scale especially in cloud computing. So maintaining security against these threats is important in any organizations. Security could be of different types like hardware security, software security, malicious behavior of attackers, and many others besides security; many organizations try to introduce many methods which will provide malicious behaviors of attackers so that an alert message will propagate throughout the whole system so that even if some attacker tries to break down the security an alert message is generated. Such a model is the intrusion detection system. An intrusion detection system (IDS) is a hardware device or software application that monitors network and/or system or host activities for malicious activities’ policy violations, creates and sends reports to a management station or system administrator which decides whether to take an action on the intrusion or it was only a false alarm. In this paper, we introduce a model or system called snort which is an intrusion detection system based upon rules detection and has the ability to control traffic and matching data with the original database and allows only data to flow which matches with the original database.

Determining whether a given policy meets a site's high-level security goals can be difficult, due to the low-level nature and complexity of the policy language, and the multiple policy violation patterns. In this paper, we propose a visualization-based policy analysis framework that enables system administrators to visually query and visualize SELinux security policies and to easily identify the policy violations. We propose and formalize both a semantic substrate and adjacency matrix visualization techniques for policy visualization. Furthermore, we propose a visual query language for expressing policy queries in a visual form. Our framework is targeted towards enabling the average administrator by providing an intuitive cognitive sense about the policy, policy queries and policy violations. We also describe our implementation of a visualization-based policy analysis tool that provides the functionalities discussed in our framework.

The emergence of new capabilities such as virtualization and elastic (private or public) cloud computing infrastructures has made it possible to deploy multiple applications, on demand, on the same cloud infrastructure. A major challenge to achieve this possibility, however, is that modern applications are typically distributed, structured systems that include not only computational and storage entities, but also policy entities (e.g., load balancers, firewalls, intrusion prevention boxes). Deploying applications on a cloud infrastructure without the policy entities may introduce substantial policy violations and/or security holes. In this paper, we present PACE: the first systematic framework for Policy-Aware Application Cloud Embedding. We precisely define the policy-aware, cloud application embedding problem, study its complexity and introduce simple, efficient, online primal-dual algorithms to embed applications in cloud data centers. We conduct evaluations using data from a real, large campus network and a realistic data center topology to evaluate the feasibility and performance of PACE. We show that deployment in a cloud without considering in-network policies may lead to a large number of policy violations (e.g., using tree routing as a way to enforce in-network policies may observe up to 91% policy violations). We also show that our embedding algorithms are very efficient by comparing with a good online fractional embedding algorithm.

Wrongful firings in violation of public Policy have received inadequate attention from managers and researchers. Such firings involve ethical issues, including employees for refusing to engage in wrongdoing, reporting organizational wrongdoing, or exercising a legal right or duty. We develop a model of the wrongful firing in violation of public policy process, based on our qualitative analysis of 63 legal cases. The model specifies that these firings arise when managers confuse an organizational problem with the employee who raises the issue. Management either fires the employee immediately or labels the employee a “deviant,” often harassing or pressuring the employee to conform and then firing the employee when he or she does not comply. We outline ways researchers can test and extend our model, and we offer specific recommendations for managers interested in preventing firings in violation of public policy

The paper by Djurayeva Aziza Isakovna (ORCID ID: 0009-0006-5630-7177, University of Information Technologies and Management, Republic of Uzbekistan) and Urinova Nilufar Turaqulovna (ORCID ID: 0009-0004-6956-1743, University of Information Technologies and Management, Republic of Uzbekistan) has been removed from open access due to payment FRAUD and a serious violation of editorial ethics and publication policies.

Editorial

Objective: In this study, we examined how smoke/tobacco-free campuses are enforced at 4-year public universities in the United States. Methods: During September of 2016, smoke-free and tobacco-free policies of 4- year, public universities were located via the American Nonsmokers’ Rights Foundation and the College Navigator websites. Policies were categorized based upon those who were responsible for initially confronting policy violators, those who were responsible for receiving reports of policy violations, methods for reporting a violation, and the types of sanctions for violators. Results: Of 167 policies, most (62%) relied on the entire campus community (students, faculty, staff) to provide initial policy enforcement. The majority (59%) did not mention who would receive violation reports, only 15% included a detailed method for reporting a violation, and over one-third did not mention any sanctions for students, employees, or visitors. Conclusion: This study highlights the need for more specific and robust policy enforcement at smoke/tobacco-free universities.

Insiders’ negligence or abuse is regarded as a leading cause of information security breaches in organizations. As most of the extant studies have largely examined insider threats at a high level of abstraction, the role of situational moral reasoning for information security policy (ISP) violations in specific situations has received little attention. To advance this line of research, this paper opens up a potentially fruitful path for IS researchers by applying situational action theory (SAT) to contextually examine why employees violate ISPs in particular situations. We consider the violations of password security policy, internet use policy, and confidential data security policy, and examine specific violation intents ranging from altruistic to malicious. The results support most of the assertions derived from SAT. We found situational moral beliefs to be the predominant driver for ISP violations across three situations in an organizational setting. However, the moderation effect of moral beliefs was only significant in situations involving sharing passwords and selling confidential data. Sanction certainty and sanction severity were also found to have different effects across situations. We conclude by presenting implications for IS security practitioners and suggestions for future research.

This study aims to examine the role of Information Systems (SI) in improving the quality of audits of local government financial statements. Audit of local government financial statements is a critical process in evaluating public finances and ensuring the reliability of financial information and compliance with applicable regulations. In recent years, the use of SI has become an important factor in improving audit quality by strengthening the efficiency, effectiveness, and accuracy of the audit process. A review of the literature reveals that the use of SI in auditing local government financial statements provides significant benefits. First, automation of the audit process using SI replaces error-prone manual tasks, thereby increasing audit efficiency. Second, SI facilitates efficient data collection and analysis, allowing auditors to access more accurate and comprehensive financial information. Third, SI strengthens internal supervision through real-time monitoring of financial activities and early detection of risks or policy violations. Finally, SI also provides protection against the security and integrity of sensitive data in auditing practices. However, the use of SI in auditing local government financial statements is also faced with challenges. These challenges include limited resources, complexity of system integration, security of sensitive data, and the need for increased auditor competence in understanding and using SI effectively. Through this research, it is hoped that a deeper understanding of the role of SI in improving the quality of audits of local government financial statements can be obtained. The results of this study can provide practical guidance and recommendations for auditors and local governments in making optimal use of SI, as well as overcoming challenges that may arise. Thus, it is expected that the practice of auditing local government financial statements can be more effective, accurate, and transparent, as well as provide significant benefits in public financial management.

Cybersecurity threats are evolving rapidly, necessitating intelligent defense systems that adapt to user-specific behavior and environmental context. This paper presents SecureMind-Net, a novel context-aware adaptive agent framework that integrates neuro-symbolic reasoning with federated learning for personalized cybersecurity threat mitigation. Unlike traditional static or rule-based systems, SecureMind-Net continuously profiles user behavior and system interactions using GRU-based context encoders and semantic reasoning ontologies to dynamically detect threats. A Transformer-based encoder extracts latent threat signatures, while an OWL-based symbolic module provides explainable reasoning over contextual metadata and policy violations. The hybrid neuro-symbolic threat detection achieves superior accuracy by fusing neural and symbolic predictions through a confidence-weighted decision gate. Personalized risk mitigation is performed using Proximal Policy Optimization (PPO), which adapts mitigation strategies like quarantine, alerting, or process isolation to user-specific risk profiles. Federated learning ensures decentralized, privacy-preserving model updates using secure aggregation and differential privacy, eliminating the need to share raw data across clients. Experimental results on the CICIDS2017 and TON_IoT datasets show that SecureMind-Net achieves an overall detection accuracy of 97.6%, outperforming conventional models including CNN (89.7%), Bi-LSTM (91.5%), and Transformer-only models (93.6%). The system also demonstrates low false-positive rates (1.8%) and fast inference times (28.3 ms), making it suitable for real-time deployment. The framework's ability to learn personalized behavior profiles significantly reduces alert fatigue and improves precision across diverse user roles. SecureMind-Net represents a significant advancement in intelligent, adaptive, and privacy-preserving cybersecurity systems.

A Network Intrusion Detection System is a system which detects intrusive, malicious activities or policy violations in a host or hosts network. The ability to access balanced and diversified data to train the system is very important for any detection system. Intrusion data rarely have these characteristics, since samples of network traffic are strongly biased to normal traffic, being difficult to access traffic associated with intrusion events. Therefore, it is important to have a method to synthesize intrusion data with a probabilistic and behavioral structure similar to the original one. In this work, we provide such a method. Intrusion data have continuous and categorical features, with a strongly unbalanced distribution of intrusion labels. That is the reason why we generate synthetic samples conditioned to the distribution of labels. That is, from a particular set of labels, we generate training samples associated with that set of labels, replicating the probabilistic structure of the original data that comes from those labels. We use a generative model based on a customized variational autoencoder, using the labels of the intrusion class as an additional input to the network. This modification provides an advantage, as we can readily generate new data using only the labels, without having to rely on training samples as canonical representatives for each label, which makes the generation process more reliable, less complex and faster. We show that the synthetic data are similar to the real data, and that the new synthesized data can be used to improve the performance scores of common machine learning classifiers.