Insider Threat Mitigation Research Articles

Building an Impenetrable Vault: Advanced Cybersecurity Strategies for Database Servers

Organizations are relying more on database servers as storage for important information. This makes it imperative for strong measures against cyber-attack to safeguard sensitive data. The study focusses on some advanced ways of protecting database servers from cyber-crime and these are: SQL injection prevention, role-based access control, data encryption techniques, and insider threat mitigation. It also looks into the latest developments in the area such as machine learning based abnormality detection, Zero Trust architecture, and multi-factor authentication. Such practices make it possible to establish a multi-layered defensive mechanism that enhances data protection by considering the outside as well as inside dangers. Besides compliance with regulations, maintaining visibility and patch management is vital so as to remain ahead of changing cyberspace dangers. Therefore, this research presents an array of recommendations to address the entire security landscape for resilient database platforms against sophisticated assaults. The conclusions point out that there is need to amalgamate these practices towards ensuring safety in terms of risks related with contemporary database server setups. Key Words: Database Security, SQL Injection, Role-Based Access Control, Data Encryption, Insider Threat, Zero Trust Architecture.

Trustworthiness evaluation of workers in critical facilities using electroencephalography-based acquaintance test

This study emphasized the critical importance of prioritizing workers' trustworthiness in safeguarding critical facilities from the potential harm caused by insiders' betrayal. By noting the limitations of existing physical protection systems in critical facilities, this study showed that effectively characterizing and detecting malicious insider activity can be possible by observing immediate brain physiological reactions to specific stimuli. Based on the finding, a novel approach using electroencephalography (EEG)-based acquaintance tests was introduced to objectively assess ’a level of suspicion between colleagues by measuring their brain responses when he/she is exposed to familiar and unfamiliar stimuli. If a person claims falsely about one's acquaintance, the model assumes he/she has malicious or suspicious intent by violating the reporting obligation. The experiment was designed with a relatively short time of monitoring of less than 2 min and with a simple EEG headband device called MUSE. The experiment is to analyze whether the model could provide reliable prediction of disguised acquaintance avoiding complex preparation process. Averaged N170 peak analysis indicated that MUSE provided adequate signal characteristics to classify acquaintance. Also, a machine learning-based subject-wise classification model showed adequate capability to differentiate the EEG signals of acquaintances from unknowns. The final prediction combined multiple single-trial classification results, correctly detected the participant's acquaintance about 94.1 % of the cases, with similar performance when strangers were presented. The results indicated the possibility of using biosignal to enhance security culture and mitigate insider threats in critical facilities, by providing an indication of behavior that disregards security policies and procedures.

Insider threat mitigation: Systematic literature review

The increasing prevalence of cybercrime necessitates the implementation of robust security measures. The majority of these attacks are initiated by authorized users who possess knowledge of the system vulnerabilities. Thus, insider attack prevention (ITP) strategies need to be explored to ensure the security of organizations. This review comprehensively examines the existing literature on ITP methods, focusing on recent developments and their implementation in various corporations. This review categorizes and classifies different types of insider attacks and their corresponding prevention and detection techniques. This paper also introduces a multi-tiered activity monitoring model that integrates network, system, and physical security measures to provide comprehensive defense against insider threats. This review also provides a detailed taxonomy that classifies insider threats based on insider type, access level, and targeted security objectives. Finally, future directions for ITP are explored, highlighting the open issues and challenges that need to be addressed.

Time Aspect of Insider Threat Mitigation

Time Aspect of Insider Threat Mitigation

Counterintelligence Risks in Crew Management and Recruitment: The Role of Profiling and Screening in Shipping Companies

This paper examines the critical role of profiling screening in countring security threats within the maritime industry, focusing on crew management and recruitment processes.In light of the industry’s susceptibility to espionage, terrorism, and sabotage, effectivecounterintelligence measures are imperative. By scrutinizing the vulnerabilities and best practices associated with profiling screening, shipping companies can fortify their security defenses, mitigate insider threats, and ensure the safety of their assets and personnel.

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

This study investigates the effectiveness of Role-Based Access Control (RBAC) systems in mitigating insider threats to database security within various organizational environments. Insider threats represent a significant challenge for database security, necessitating robust and adaptive security measures. By delineating access based on users' roles within an organization, RBAC emerges as a critical tool against such threats. Employing a quantitative research methodology, this work gathered data through a survey targeting professionals directly involved in the security and management of organizational databases across technology, finance, healthcare, and government industries. The study utilized Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate the measurement model and analyze the relationships between RBAC effectiveness, implementation challenges, RBAC enhancements, and their collective impact on insider threat reduction. Findings indicate that RBAC effectively reduces unauthorized access and data breaches, significantly mitigating insider threats. However, implementation challenges such as role definition complexity and adapting to dynamic access needs emerge as notable obstacles. Enhancements in RBAC, mainly through integrating technologies like machine learning and dynamic access controls, are identified as critical mediators that enhance RBAC's effectiveness. The study concludes that while RBAC is a vital tool for database security, its success depends on continuous improvement and customization to specific organizational contexts. It recommends developing continuous enhancement programs for RBAC systems, specialized training for administrators, and the customization of RBAC strategies to meet unique organizational and industry needs. These measures are crucial for optimizing RBAC's effectiveness against insider threats.

E-Watcher: insider threat monitoring and detection for enhanced security

AbstractInsider threats refer to harmful actions carried out by authorized users within an organization, posing the most damaging risks. The increasing number of these threats has revealed the inadequacy of traditional methods for detecting and mitigating insider threats. These existing approaches lack the ability to analyze activity-related information in detail, resulting in delayed detection of malicious intent. Additionally, current methods lack advancements in addressing noisy datasets or unknown scenarios, leading to under-fitting or over-fitting of the models. To address these, our paper presents a hybrid insider threat detection framework. We not only enhance prediction accuracy by incorporating a layer of statistical criteria on top of machine learning-based classification but also present optimal parameters to address over/under-fitting of models. We evaluate the performance of our framework using a real-life threat test dataset (CERT r4.2) and compare it to existing methods on the same dataset (Glasser and Lindauer 2013). Our initial evaluation demonstrates that our proposed framework achieves an accuracy of 98.48% in detecting insider threats, surpassing the performance of most of the existing methods. Additionally, our framework effectively handles potential bias and data imbalance issues that can arise in real-life scenarios.

Mitigating Insider Threat’s IP Spoofing through Enhanced Dynamic Cluster Algorithm (EDPU Based HCF)

Insider Threat has always been a major problem to computer security due to unauthorized system misuse by users in an organization. Understanding the concept and the inherent adverse consequences of the insider threat can assist in postulating mitigating approaches and techniques to the menace. Insider intrusion, from researches, experiences and literature have proved to be more expensive and destructive more than external attacks due the comprehensive understanding of the internal operations of the organization by the perpetrator. Many researchers have explored into the unhealthy nature of insider activity with the aim of eliminating the threat, thereby identifying the various categories as theft of intellectual property, fraud, sabotage, espionage. This work tends to address the menace by studying models for detecting, reducing and eliminating the threat through IP Spoofing in order to propose a better model for the intrusion. Certain experimental research through analysis of network data measurement has shown that HCF (Hop Count Filtering) can discover and discard almost 90% of spoofed IP packets but an improvement on this experiment called DPU (Dynamic Path Update) Based Hop Count Filtering has proved to identify and discard more than 90%. This was carried out in Linux Kernel environment to substantiate the effectiveness of its measurements. However, enhancing enhancing the performance of the DPU-based HCF by reducing the packet size of packets at the point of entry in order to decrease the network traffic, and to permanently discard 100% spoofed packets is the research direction of this work

Combating Insider Threat in the Open-World Environments: Identification, Monitoring, and Data Augmentation

Recent years have witnessed a dramatic increase in a class of security threats known as "insider threats". These threats occur when individuals with authorized access to an organization's network engage in harmful activities, potentially leading to the disclosure of vital information or adversely affecting the organization's systems (e.g., financial loss, system crashes, and national security challenges). Distinct from other types of terror attacks, combating insider threats exhibits several unique challenges, including (1) rarity, (2) non-separability, (3) label scarcity, (4) dynamics, and (5) heterogeneity, making themselves extremely difficult to identify and mitigate. We target the challenging problem of combating insider threats in open-world environments by leveraging a variety of data sources (e.g., internal system logs, employee networks, human trafficking, and smuggling networks). To effectively combat these intricate threats, we introduce an interactive learning mechanism that is composed of three mutually beneficial learning modules: insider identification, insider monitoring, and data augmentation. Each module plays a crucial role in enhancing our ability to detect and mitigate insider threats, thereby contributing to a more secure and resilient organizational environment.

Leveraging Artificial Intelligence in Enhancing Identity and Access Management (IAM) for Court IT Systems

As judicial systems worldwide embrace digital transformation, the need to integrate advanced technologies into court operations has become more urgent than ever. This shift has revolutionized case management, stakeholder communication, and record-keeping, significantly enhancing the efficiency, transparency, and accessibility of judicial processes. However, with these advancements comes the critical need to protect sensitive data, such as personal details, case evidence, and legal documents, from unauthorized access and cyber threats. This white paper explores the pivotal role of Identity and Access Management (IAM) in securing court IT systems [1]. IAM, as a gatekeeper, not only plays a crucial role in protecting sensitive data but also provides a sense of reassurance about the security of court IT systems. It ensures that only authorized individuals interact with the data and provides a comprehensive audit trail to mitigate insider threats. By offering precise, role-based access control and monitoring user activity, IAM solutions help courts protect confidential information, uphold the integrity of judicial processes, and maintain public trust.

Blockchain based general data protection regulation compliant data breach detection system

Context Data breaches caused by insiders are on the rise, both in terms of frequency and financial impact on organizations. Insider threat originates from within the targeted organization and users with authorized access to an organization’s network, applications, or databases commit insider attacks. Motivation Insider attacks are difficult to detect because an attacker with administrator capabilities can change logs and login records to destroy the evidence of the attack. Moreover, when such a harmful insider attack goes undetected for months, it can do a lot of damage. Such data breaches may significantly impact the affected data owner’s life. Developing a system for rapidly detecting data breaches is still critical and challenging. General Data Protection Regulation (GDPR) has defined the procedures and policies to mitigate the problems of data protection. Therefore, under the GDPR implementation, the data controller must notify the data protection authority when a data breach has occurred. Problem Statement Existing data breach detection mechanisms rely on a reliable third party. Because of the presence of a third party, such systems are not trustworthy, transparent, secure, immutable, and GDPR-compliant. Contributions To overcome these issues, this study proposed a GDPR-compliant data breach detection system by leveraging the benefits of blockchain technology. Smart contracts are written in Solidity and deployed on a local Ethereum test network to implement the solution. The proposed system can generate alert notifications against every data breach. Results We tested and deployed our proposed system, and the findings indicate that it can accomplish the insider threat mitigation objective. Furthermore, the GDPR compliance analysis of our system was also evaluated to make sure that it complies with the GDPR principles (such as right to be forgotten, access control, conditions for consent, and breach notifications). The conducted analysis has confirmed that the proposed system offers capabilities to comply with the GDPR from an application standpoint.

Insider threat mitigation through human intelligence and counterintelligence: A case study in the shipping industry

This paper comprehensively examines the multifaceted motivations behind insider threats within organizations, elucidating driving forces such as financial gain, revenge, personal aspirations, ideological beliefs, coercion, and negligence. Understanding this spectrum is fundamental for crafting effective Counterintelligence strategies. The study delves into behavioral indicators crucial for identifying potential threats, emphasizing the significance of recognizing warning signs like unusual data access, unsanctioned software usage, escalated privilege requests, poor performance, disagreement with policies, and more. Furthermore, the role of Human Intelligence (HUMINT) in Counterintelligence (CI) and insider threat detection is explored, highlighting its qualitative contribution to understanding human behavior. Plus, through a hypothetical case study in the Shipping industry, the paper illustrates the direct application of HUMINT principles in fortifying security against insider threats, considering the unique challenges of this dynamic sector. The case study strategically employs employee interviews, psychological assessments, social network analysis, and trust-building initiatives to proactively identify and mitigate potential threats, in an industry reliant on seamless global supply chain operations.

Addressing the gap in information security: an HR-centric and AI-driven framework for mitigating insider threats

PurposeDespite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.Design/methodology/approachThe paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.FindingsThe paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.Originality/valueThe paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

Mitigating Insider Threat: A Neural Network Approach for Enhanced Security

Mitigating Insider Threat: A Neural Network Approach for Enhanced Security

VISTA: An inclusive insider threat taxonomy, with mitigation strategies

Insiders have the potential to do a great deal of damage, given their legitimate access to organisational assets and the trust they enjoy. Organisations can only mitigate insider threats if they understand what the different kinds of insider threats are, and what tailored measures can be used to mitigate the threat posed by each of them. Here, we derive VISTA (inclusiVeInSiderThreat tAxonomy) based on an extensive literature review and a survey with C-suite executives to ensure that the VISTA taxonomy is not only scientifically grounded, but also meets the needs of organisations and their executives. To this end, we map each VISTA category of insider threat to tailored mitigations that can be deployed to reduce the threat.

An improved model for comparing different endpoint detection and response tools for mitigating insider threat

An improved model for comparing different endpoint detection and response tools for mitigating insider threat

Insider Threat Mitigation in High Security Facilities

The biggest challenge for the security in high security facilities is the insider threat, humans as the weakest link of the system. The insider is an invisible enemy of the security, because it has unique capabilities. Although perfect security cannot exist, the aim of the present study – besides showing the threat represented by insider offenders – is to introduce the measures for risk mitigation.

Why and how unpredictability is implemented in aviation security – A first qualitative study

In the past, aviation security regulations have mostly been reactive, responding to terrorist attacks by adding more stringent measures. In combination with the standardization of security control processes, this has resulted in a more predictable system that makes it easier to plan and execute acts of unlawful interference. The implementation of unpredictability, that is, variation of security controls, as a proactive approach could be beneficial for addressing risks coming from outside (terrorist attacks) and inside the system (insider threats). By conducting semi-structured interviews with security experts, this study explored why and how unpredictability is applied at airports. Results show that European airport stakeholders apply unpredictability measures for many reasons: To complement the security system, defeat the opponent, and improve human factor aspects of the security system. Unpredictability is applied at various locations, by different controlling authorities, to different target groups and application forms; nevertheless, the deployment is not evaluated systematically. Results also show how the variation of security controls can contribute to mitigating insider threats, for example, by reducing insider knowledge. Future research should focus on the evaluation of the deterrent effect of unpredictability to further give suggestions on how unpredictable measures should be realized to proactively address upcoming risks.

Insider-threat detection: Lessons from deploying the CITD tool in three multinational organisations

Insider threat is a persistent concern for organisations and business alike that has attracted the interest of the research community, resulting in numerous behavioural models and tools to tackle it. However, the effectiveness of detection of these tools has scarcely been demonstrated in real environments. In order to fill this gap, we collaborated with three multinational commercial organisations who trialled our anomaly detection system, and worked with us to understand performance constraints for insider threat detection deployment and innate weaknesses in their operational contexts. During a period longer than a year, we were provided access to real data in their premises and interacted with their cybersecurity analysts to understand their systems, validate the results and identify best practices for mitigating insider threat. In this paper, we provide details on the architecture used in our tool, the methodology followed to validate its performance and we elaborate on our experiences in implementing the tool in the three corporate environments. We present the results obtained from deploying the detection system in real network infrastructure over a period of six months, the lessons learned, issues experienced, and potential limitations.

Lessons Learned from a Comparative Analysis of Counterintelligence and Insider Threat Mitigation Case Studies in Nuclear Facilities

Lessons Learned from a Comparative Analysis of Counterintelligence and Insider Threat Mitigation Case Studies in Nuclear Facilities