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Journal articles on the topic 'Insider threat'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Alsowail, Rakan A., and Taher Al-Shehari. "A Multi-Tiered Framework for Insider Threat Prevention." Electronics 10, no. 9 (2021): 1005. http://dx.doi.org/10.3390/electronics10091005.

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As technologies are rapidly evolving and becoming a crucial part of our lives, security and privacy issues have been increasing significantly. Public and private organizations have highly confidential data, such as bank accounts, military and business secrets, etc. Currently, the competition between organizations is significantly higher than before, which triggers sensitive organizations to spend an excessive volume of their budget to keep their assets secured from potential threats. Insider threats are more dangerous than external ones, as insiders have a legitimate access to their organizati

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Mohammad, Nur Ameera Natasha, Warusia Mohamed Yassin, Rabiah Ahmad, Aslinda Hassan, and Mohammed Nasser Ahmed Al Mhiqani. "An Insider Threat Categorization Framework for Automated Manufacturing Execution System." International Journal of Innovation in Enterprise System 3, no. 02 (2019): 31–41. http://dx.doi.org/10.25124/ijies.v3i02.38.

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Insider threats become one of the most dangerous threats in the cyber world as compared to outsider as the insiders have knowledge of assets. In addition, the threats itself considered in-visible and no one can predict what, when and how exactly the threat launched. Based on conducting literature, threat in Automated Manufacturing Execution Systems (AMESs) can be divided into three principle factors. Moreover, there is no standard framework to be referring which exist nowadays to categorize such factors in order to identify insider threats possible features. Therefore, from the conducted liter

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Palaparthy, Keerthana, Y. Manohar Reddy, Jatoth Victor Paul, and S. Raju. "Enhancing Insider Threat Detection through Integrated Behavioral, Signature and Anomaly based Detection Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39835.

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Insider threats present substantial risks to organizational security, as malicious actors exploit their authorized access to systems, networks, or data to perpetrate harmful activities. These threats encompass various forms, including data theft, sabotage, fraud, or espionage, leading to significant financial losses, reputational damage, or regulatory penalties. Traditional approaches to insider threat detection, such as anomaly-based, signature-based, and behavioral analysis methods, have inherent limitations, including high false positives, reliance on known patterns, and lack of contextual

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Anatoly, Adamovich Kornienko, and Aleksandrovich Polyanichko Mark. "Insider Detection Method in a Company." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 585–92. https://doi.org/10.35940/ijeat.A1452.129219.

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Managers often focus on external threats mainly due to the difficulties in evaluating the losses from the insider activities. The purpose of the study is to improve the efficient performance of an information security department and a company itself in counteracting insider threats by increasing the accuracy and rate of assessing the insider threat for each employee and ranking employees in accordance with the assessment of a summarized technical threat indicator. The authors morphologically analyze the features of insider activities in three sections and identify a promising area for combatin

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Mohammed, Nasser Al-Mhiqani, Ahmad Rabiah, Zainal Abidin Zaheera, Yassin Warusia, Hassan Aslinda, and Natasha Mohammad Ameera. "New insider threat detection method based on recurrent neural networks." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1474–79. https://doi.org/10.11591/ijeecs.v17.i3.pp1474-1479.

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Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In th

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Al-Mhiqani, Mohammed Nasser, Rabiah Ahmad, Z. Zainal Abidin, et al. "A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations." Applied Sciences 10, no. 15 (2020): 5208. http://dx.doi.org/10.3390/app10155208.

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Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern an

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Nasser Al-mhiqani, Mohammed, Rabiah Ahmad, Zaheera Zainal Abidin, Warusia Yassin, Aslinda Hassan, and Ameera Natasha Mohammad. "New insider threat detection method based on recurrent neural networks." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (2020): 1474. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1474-1479.

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<p>Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organizati

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Luqman Hakim. "Insider Threats: The Cybersecurity Analysis using OCTAVE Allegro which are combined with HAIS-Q." Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika 3, no. 1 (2025): 36–47. https://doi.org/10.61132/uranus.v3i1.649.

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There are various types of cybersecurity threat in the globe, one of which is an insider threat. Because the current vulnerability generates an insider threat, SMEs suffer. In this situation, the company suffers a loss of profit and trust. Because of the speed and intensity of cybersecurity, particularly internal threats, SMEs must conduct regular vulnerability assessments. Insider threats to cyber security are a major issue in today's environment. Insider threats come in a variety of flavours, one of which is the unintended insider threat, or UIsT. This type of threat is a real one, and it is

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9

Adikari, Swini. "Countering the Threat of Nuclear Terrorism Arising from Malicious Insiders." Global Affairs Review 1, no. 2 (2021): 1–18. http://dx.doi.org/10.51330/gar.0020214.

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Nuclear theft from malicious insiders is a significant threat to Pakistan’s nuclear weapons arsenal. Pakistan is a member of the Convention of the Physical Protection of Nuclear Material (CPPNM), which is an international agreement that adheres to the protection of nuclear materials and the recovery of stolen nuclear materials. However, this agreement does not specifically take into account the risk of security breaches arising from malicious insiders due to Pakistan’s rapidly growing nuclear arsenal. The purpose of this paper is to examine the heightened risk of insider threats in conjunction

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Kim, Park, Kim, Cho, and Kang. "Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms." Applied Sciences 9, no. 19 (2019): 4018. http://dx.doi.org/10.3390/app9194018.

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Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based

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Soleh, Musdi Muhammad. "A Study Case in NTMC POLRI: Reducing Data Breach Risk from Insider Threats by Using Risk IT Framework." ACMIT Proceedings 3, no. 1 (2019): 171–79. http://dx.doi.org/10.33555/acmit.v3i1.41.

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Risks is not only merely comes from external threats, it is also comes from inside - internal actor. Vormetric Insider Threat mentioned that in 800 surveyed enterprise companies, 89% vulnerable to insider attacks [1]. It mentioned that Data Breach issue the highest risk happened to the company caused by insider threats. This paper will analyse the insider attacks, Risk IT framework will be used toreduce to reduce and prevent these vulnerabilities in valuable assets.

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Kim, Jung Hwan, Chul Min Kim, and Man-Sung Yim. "An Investigation of Insider Threat Mitigation Based on EEG Signal Classification." Sensors 20, no. 21 (2020): 6365. http://dx.doi.org/10.3390/s20216365.

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This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted fr

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Yilmaz, Erhan, and Ozgu Can. "Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13341–46. http://dx.doi.org/10.48084/etasr.6911.

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Insider threats pose a significant risk to organizations, necessitating robust detection mechanisms to safeguard against potential damage. Traditional methods struggle to detect insider threats operating within authorized access. Therefore, the use of Artificial Intelligence (AI) techniques is essential. This study aimed to provide valuable insights for insider threat research by synthesizing advanced AI methodologies that offer promising avenues to enhance organizational cybersecurity defenses. For this purpose, this paper explores the intersection of AI and insider threat detection by acknow

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Hershkowitz, Martin. "The “Insider” Threat." Journal of Police Crisis Negotiations 7, no. 1 (2007): 103–11. http://dx.doi.org/10.1300/j173v07n01_06.

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Nostro, Nicola, Andrea Ceccarelli, Andrea Bondavalli, and Francesco Brancati. "Insider Threat Assessment." ACM SIGOPS Operating Systems Review 48, no. 2 (2014): 3–12. http://dx.doi.org/10.1145/2694737.2694740.

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Saxena, Neetesh, Emma Hayes, Elisa Bertino, Patrick Ojo, Kim-Kwang Raymond Choo, and Pete Burnap. "Impact and Key Challenges of Insider Threats on Organizations and Critical Businesses." Electronics 9, no. 9 (2020): 1460. http://dx.doi.org/10.3390/electronics9091460.

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The insider threat has consistently been identified as a key threat to organizations and governments. Understanding the nature of insider threats and the related threat landscape can help in forming mitigation strategies, including non-technical means. In this paper, we survey and highlight challenges associated with the identification and detection of insider threats in both public and private sector organizations, especially those part of a nation’s critical infrastructure. We explore the utility of the cyber kill chain to understand insider threats, as well as understanding the underpinning

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Hu, Teng, Bangzhou Xin, Xiaolei Liu, Ting Chen, Kangyi Ding, and Xiaosong Zhang. "Tracking the Insider Attacker: A Blockchain Traceability System for Insider Threats." Sensors 20, no. 18 (2020): 5297. http://dx.doi.org/10.3390/s20185297.

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The insider threats have always been one of the most severe challenges to cybersecurity. It can lead to the destruction of the organisation’s internal network system and information leakage, which seriously threaten the confidentiality, integrity and availability of data. To make matters worse, since the attacker has authorized access to the internal network, they can launch the attack from the inside and erase their attack trace, which makes it challenging to track and forensics. A blockchain traceability system for insider threats is proposed in this paper to mitigate the issue. First, this

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Jason Slaughter, Carole E. Chaski, and Kellep Charles. "Emotion analysis based on belief of targeted individual supporting insider threat detection." International Journal of Science and Research Archive 11, no. 2 (2024): 226–37. http://dx.doi.org/10.30574/ijsra.2024.11.2.0393.

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Unintentional Insider Threat is the concept that an insider threat event may occur unintentionally versus maliciously. Individuals who believe they are being targeted may be at increased risk of being insider threats. Based on a previous survey titled A Survey of Unintentional Medical Insider Threat Category, it was found that both medical and psychological problems may lead to feeling targeted. It was further found that Insider Threat programs should be updated to include trained personnel in both medicine and psychology in addition to cybersecurity to address the risk.

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Bhandari, Durga, and Kumar Pudashine. "Insider Threat Detection using LSTM." Journal of Science and Technology 3, no. 1 (2023): 57–65. http://dx.doi.org/10.3126/jost.v3i1.69066.

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Security threats have been the major challenge for any organization. This has even been more threatening since in present days most of the organizational data are in digital form and digital data are easy to access and alter if not properly secured. While most of the threats considered are external threats like Viruses, Worms, DOS, DDOS, hacking etc. Internal threats also cannot be ignored. Many frauds, especially for organizations that perform financial transactions, are done by misusing the internal access to the data. Internal threats happen from the users who have some privileged access to

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Wang, Jiarong, Qianran Sun, and Caiqiu Zhou. "Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events." Applied Sciences 13, no. 24 (2023): 13021. http://dx.doi.org/10.3390/app132413021.

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With the continuous advancement of enterprise digitization, insider threats have become one of the primary cybersecurity concerns for organizations. Therefore, it is of great significance to develop an effective insider threat detection mechanism to ensure the security of enterprises. Most methods rely on artificial feature engineering and input the extracted user behavior features into a clustering-based unsupervised machine learning model for insider threat detection. However, feature extraction is independent of clustering-based unsupervised machine learning. As a result, user behavior feat

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Marshet Tamirat. "Current Detection Methods for Insider Threats and Social Engineering Attacks: Enhancements and Analysis Using Deep Learning." Journal of Electrical Systems 20, no. 11s (2024): 2838–53. https://doi.org/10.52783/jes.7966.

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Despite advancements in technology, insider threats and social engineering attacks continue to pose significant challenges. Current threat detection methods often fail to eﬀectively identifies insider threats, leaving organizations vulnerable. This systematic review thoroughly examines and evaluates existing detection methods for insider threats and social engineering attacks, performs comparative gap analyses, assesses detection eﬀectiveness, identifies inherent challenges, and proposes conceptual system architecture. A primary challenge is distinguishing between normal and malicious insider

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Al-Shehari, Taher, and Rakan A. Alsowail. "An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques." Entropy 23, no. 10 (2021): 1258. http://dx.doi.org/10.3390/e23101258.

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Insider threats are malicious acts that can be carried out by an authorized employee within an organization. Insider threats represent a major cybersecurity challenge for private and public organizations, as an insider attack can cause extensive damage to organization assets much more than external attacks. Most existing approaches in the field of insider threat focused on detecting general insider attack scenarios. However, insider attacks can be carried out in different ways, and the most dangerous one is a data leakage attack that can be executed by a malicious insider before his/her leavin

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Clarke, Karla, Yair Levy, Laurie Dringus, and Shonda Brown. "How workplace satisfaction affects insider threat detection as a vital variable for the mitigation of malicious cyber insiders." Online Journal of Applied Knowledge Management 7, no. 1 (2019): 40–52. http://dx.doi.org/10.36965/ojakm.2019.7(1)40-52.

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Insider threat mitigation is a growing challenge within organizations. The development of a novel alert visualization dashboard for the identification of potentially malicious cyber insider threats was identified as necessary to alleviate this challenge. This research developed a cyber insider threat dashboard visualization prototype for detecting potentially malicious cyber insider activities QUICK.v™. This study utilized Subject Matter Experts (SMEs) by applying the Delphi Method to identify the most critical cyber visualization variables and ranking. This paper contains the detailed results

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Dr.Vijayalakshmi Chintamaneni, Dr.M.SreeRamu, and Shaik Abubakar Siddiq. "Insider Threats in the Age of Cyber Threat Intelligence: Behavioral Indicators and Detection Strategies." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 132–43. https://doi.org/10.46647/ijetms.2025.v09si01.022.

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Insider threats remain one of the most challenging aspects of cyber security, as they oftenbypasstraditional perimeter defenses. This paper explores how cyber threat intelligence (CTI) canenhance insider threat detection through behavioral analytics, anomaly detection, and machinelearning-based profiling. We investigate real-world insider threat incidents across the financial sector,critical infrastructure, and corporate environments to identify key indicators of malicious activity. Byintegrating AI-driven risk scoring models with CTI frameworks, we propose a predictive approachthat improves e

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Zhu, Taiming, Yuanbo Guo, Ankang Ju, Jun Ma, and Xuan Wang. "An Insider Threat Detection Method Based on Business Process Mining." International Journal of Business Data Communications and Networking 13, no. 2 (2017): 83–98. http://dx.doi.org/10.4018/ijbdcn.2017070107.

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Current intrusion detection systems are mostly for detecting external attacks, but the “Prism Door” and other similar events indicate that internal staff may bring greater harm to organizations in information security. Traditional insider threat detection methods only consider the audit records of personal behavior and failed to combine it with business activities, which may miss the insider threat happened during a business process. The authors consider operators' behavior and correctness and performance of the business activities, propose a business process mining based insider threat detect

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Qin, Heng, and Jin Hui Zhao. "Insider Threat Detection with Behavior-Based Attestation." Applied Mechanics and Materials 568-570 (June 2014): 1370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1370.

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Insiders, who have the lawful authority in network information system, formed a huge threat to security by abuse and misuse of authority. It has become one of huge challenge to the security of information system. Against the features of more subtle and more difficult to find, this paper study how to perceive the trusted behavior of insiders with behavior-based attestation. Taking into account the impact of various uncertainties in monitoring and perception process, dynamic awareness model of insider threat is presented based on subjective logic. In order to find the insider threats, monitoring

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Fyffe, George. "Addressing the insider threat." Network Security 2008, no. 3 (2008): 11–14. http://dx.doi.org/10.1016/s1353-4858(08)70031-x.

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Pfleeger, Shari Lawrence, and Salvatore J. Stolfo. "Addressing the Insider Threat." IEEE Security & Privacy Magazine 7, no. 6 (2009): 10–13. http://dx.doi.org/10.1109/msp.2009.146.

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WATANABE, Kazumi. "Insider cyber-threat study:." Proceedings of the Annual Convention of the Japanese Psychological Association 74 (September 20, 2010): 3PM145. http://dx.doi.org/10.4992/pacjpa.74.0_3pm145.

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SREEKUMAR, SHREYA, SARANG C, ALKA SAJEEVAN P, VARNA O V, and ASWATHI V. "Insider Threat Detection Methodologies." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43389.

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Insider threats, originating from individuals with legitimate access to sensitive systems and data, represent a significant cybersecurity challenge, unlike external attacks, insider threats are harder to detect, as they often exploit legitimate credentials to bypass conventional security measures. These threats can result in severe consequences such as data breaches, financial losses, and system disruptions. Traditional detection methods, such as rule-based approaches and classical ma- chine learning models, struggle to identify evolving and sophisticated insider behaviors due to their relianc

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Shevchenko, Svitlana, Yuliia Zhdanovа, Pavlo Skladannyi, and Sofia Boiko. "INSIDERS AND INSIDER INFORMATION: ESSENCE, THREATS, ACTIVITIES AND LEGAL RESPONSIBILITY." Cybersecurity: Education, Science, Technique 3, no. 15 (2022): 175–85. http://dx.doi.org/10.28925/2663-4023.2022.15.175185.

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The constant development of information technologies, the growing role at the present stage of human potential create new internal threats to the information security of enterprises. The article investigates and analyzes the problems of information security associated with internal violators of companies and their insider activity. Economic reports and analytical materials allowed to determine the relevance and importance of this work. Based on scientific literature, a review of various approaches to the definition of "insider" and "insider information" was carried out. The main key indicators

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Ruba, Ruba, and Hanan AlShaher. "Insider Threat Detection: Exploring User Event Behavior Analytics and Machine Learning in Security Reviews." Journal of Cybersecurity and Information Management 13, no. 2 (2024): 171–81. http://dx.doi.org/10.54216/jcim.130213.

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With the exponential increase in technology use, insider threats are also growing in scale and importance, becoming one of the biggest challenges for government and corporate information security. Recent research shows that insider threats are more costly than external threats, making it critical for organizations to protect their information security. Effective insider threat detection requires the use of the latest models and technologies. Although a large number of insider threats have been discovered, the field is still limited by many issues, such as data imbalance, false positives, and a

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Munro, Victor. "Applying Insider Risk Mitigation: Contemporary Issues." Journal of Intelligence, Conflict, and Warfare 6, no. 2 (2023): 84–89. http://dx.doi.org/10.21810/jicw.v6i2.6251.

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On July 20, 2023, Victor Munro, Executive Director of the Insider Risk Management Centre of Excellence, presented on the contemporary issues surrounding the field of insider risk management. The presentation focused on three significant areas: 1) the nexus between whistleblower protections and insider threat management; 2) the balance of mitigating threats without compromising; and 3) the post-COVID impact on threat vectors and role of artificial intelligence in threat mitigation and organizational culture. The presentation was followed by a question-and-answer period with questions from the a

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Zatonatskiy, D., V. Marhasova, and N. Korogod. "INSIDER THREAT MANAGEMENT AS AN ELEMENT OF THE CORPORATE ECONOMIC SECURITY." Financial and credit activity: problems of theory and practice 1, no. 36 (2021): 149–58. http://dx.doi.org/10.18371/fcaptp.v1i36.227690.

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This paper considers the insider threats in the companies from different sectors and various methods of their assessment. The problem of information leakage is becoming increasingly important for companies in all areas of economic activity. The problem of insider threats is becoming increasingly important, as the company may incur losses not only due to the leakage of information about its inventions, but also through lawsuits in case of theft of personal information of the customers, contractors and more. This means that in order to gain access to the international markets, Ukrainian companie

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Burrell, Darrell Norman, Calvin Nobles, Austin Cusak, Marwan Omar, and Lemie Gillesania. "CYBERCRIME AND THE NATURE OF INSIDER THREAT COMPLEXITIES IN HEALTHCARE AND BIOTECHNOLOGY ENGINEERING ORGANIZATIONS." JOURNAL OF CRIME AND CRIMINAL BEHAVIOR 2, no. 2 (2022): 131–44. http://dx.doi.org/10.47509/jccb.2022.v02i02.04.

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This article explores the nature of cybersecurity professionals being insider threats to their own organization, as well as the general increase in harder-to-detect threats coming from an ever-widening acceptance of third-party insiders, which organizations, biotechnology engineering, and other healthcare organizations rely on. After examining the current and emerging literature on how individuals are motivated to engage in problematic workplace behaviors as a means of gaining their specific goal or need, the paper articulates malicious cybersecurity insider threat indicators, then provides be

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Researcher. "THE ROLE OF AI IN DETECTING INSIDER THREATS IN HEALTHCARE ORGANIZATIONS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 239–48. https://doi.org/10.5281/zenodo.13960643.

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This article explores the critical role of Artificial Intelligence (AI) in detecting and preventing insider threats within healthcare organizations. It examines the growing challenges of data security in the healthcare sector, highlighting the significant financial and reputational risks posed by insider threats. The article discusses various types of insider threats, including malicious actors, negligent employees, and compromised credentials. It then delves into how AI enhances insider threat detection through behavioral analytics, machine learning, natural language processing, and predictiv

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Trivedi, Aayush. "Research Paper on Cybersecurity and Insider Threat Detection: The Role of User Behavior Analytics (UBA) in Modern Defense Strategies." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 455–66. https://doi.org/10.22214/ijraset.2025.66298.

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Insider threats have emerged as one of the most pressing challenges in modern cybersecurity. These threats, which originate from within an organization, pose a unique risk due to the trusted access that insiders—such as employees, contractors, and business partners—have to sensitive systems and data. Detecting and preventing insider threats is particularly challenging because traditional security measures, designed to guard against external attacks, are often insufficient to identify malicious or negligent behavior from trusted individuals. This research paper delves into the complexities of i

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Alsowail, Rakan A., and Taher Al-Shehari. "Techniques and countermeasures for preventing insider threats." PeerJ Computer Science 8 (April 1, 2022): e938. http://dx.doi.org/10.7717/peerj-cs.938.

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With the wide use of technologies nowadays, various security issues have emerged. Public and private sectors are both spending a large portion of their budget to protect the confidentiality, integrity, and availability of their data from possible attacks. Among these attacks are insider attacks which are more serious than external attacks, as insiders are authorized users who have legitimate access to sensitive assets of an organization. As a result, several studies exist in the literature aimed to develop techniques and tools to detect and prevent various types of insider threats. This articl

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Ogunbodede, Olajide O., Olumide S. Adewale, Boniface K. Alese, and Oluyomi K. Akinyokun. "Game Theory for Insider Threat Detection and Mitigation: A Review." International Journal of Advances in Engineering and Management 6, no. 10 (2024): 104–13. https://doi.org/10.35629/5252-0610104113.

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: Malicious insiders pose serious threat to organizations, institutions, and governments’ critical infrastructure because of their access to confidential data, systems, and resources. This threat, often referred to as the insider threat, is most of the time regarded as a human factor problemoften driven by motivation, opportunity, and capability. As long as the insider problem is treated as a technology problem, the natural recourse for detection and mitigation is to the use of technical and procedural controls. Such an approach is not advisable when confronting strategic human adversaries, si

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Donald, L. Buresh Ph.D. J.D. LL.M. "Can Extremism Be Thought of As an Insider Threat?" International Journal of Social Science and Human Research 04, no. 10 (2021): 2650–57. https://doi.org/10.47191/ijsshr/v4-i10-04.

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This paper discusses extremism and insider threats. The issue of concern is whether a person who holds extremist beliefs will at some point become an insider threat. The identification of an insider threat is highlighted, with the understanding that extremism can occur on the far-right and far-left. The paper observes one feature of extremism is that extremists can become mainstream, mainly when the political climate changes. Several existing laws involving extremism are outlined, understanding that there is no one-size-fits-all explanation for extremism

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Singh Lodhi, Manvendra, and Rahul Kaul. "Detecting Unknown Insider Threat Scenarios." International Journal on Computational Science & Applications 6, no. 5/6 (2016): 15–21. http://dx.doi.org/10.5121/ijcsa.2016.6602.

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McKenna, Brian. "Danger of the insider threat." Infosecurity 4, no. 2 (2007): 5. http://dx.doi.org/10.1016/s1754-4548(07)70025-6.

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Bulpett, Ben. "Safeguarding against the insider threat." Network Security 2020, no. 6 (2020): 14–17. http://dx.doi.org/10.1016/s1353-4858(20)30068-4.

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Greitzer, Frank L., Andrew P. Moore, Dawn M. Cappelli, Dee H. Andrews, Lynn A. Carroll, and Thomas D. Hull. "Combating the Insider Cyber Threat." IEEE Security & Privacy Magazine 6, no. 1 (2008): 61–64. http://dx.doi.org/10.1109/msp.2008.8.

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Archuleta, Edmund G., and Jack Moyer. "Guarding Against the Insider Threat." Journal - American Water Works Association 101, no. 5 (2009): 38–44. http://dx.doi.org/10.1002/j.1551-8833.2009.tb09891.x.

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Gaiduk, K. A., and A. Y. Iskhakov. "Using machine learning techniques for insider threat detection." Herald of the Siberian State University of Telecommunications and Informatics 16, no. 4 (2022): 80–95. http://dx.doi.org/10.55648/1998-6920-2022-16-4-80-95.

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This paper presents an analysis of algorithms and approaches used to solve the problem of identifying insider threats using machine learning techniques. Internal threat detection in the context of this research is reduced to the task of detecting anomalies in the audit logs of access subjects' actions. The paper formalizes the main directions of insider threats detection and presents popular machine learning algorithms. The paper raises the problem of objective evaluation of research and development in the subject area. Based on the analysis recommendations for the implementation of internal t

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Nicolaou, Andreas, Stavros Shiaeles, and Nick Savage. "Mitigating Insider Threats Using Bio-Inspired Models." Applied Sciences 10, no. 15 (2020): 5046. http://dx.doi.org/10.3390/app10155046.

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Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from insiders is increasing. Many researchers are approaching this issue with various methods in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this paper, we approach the insider threat problem and attempt to

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Gayathri, R. G., Atul Sajjanhar, and Yong Xiang. "Image-Based Feature Representation for Insider Threat Classification." Applied Sciences 10, no. 14 (2020): 4945. http://dx.doi.org/10.3390/app10144945.

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Cybersecurity attacks can arise from internal and external sources. The attacks perpetrated by internal sources are also referred to as insider threats. These are a cause of serious concern to organizations because of the significant damage that can be inflicted by malicious insiders. In this paper, we propose an approach for insider threat classification which is motivated by the effectiveness of pre-trained deep convolutional neural networks (DCNNs) for image classification. In the proposed approach, we extract features from usage patterns of insiders and represent these features as images.

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Ewuola, Ayomipo. "Risk-Based Approach to Corporate Investigations and Insider Threat Mitigation in Multinational Organizations." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 2 (2023): 811–21. https://doi.org/10.54660/.ijmrge.2023.4.2.811-821.

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Insider threats represent a significant and evolving risk to multinational organizations, challenging traditional security and investigative paradigms due to the complexity of diverse legal, cultural, and operational environments. This explores a risk-based approach to corporate investigations and insider threat mitigation, grounded in extensive investigative leadership experience across multiple regions. The risk-based methodology emphasizes the identification, prioritization, and management of insider threats by aligning investigative efforts with the organization's strategic risk appetite a

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Itai, Yaya, and Emmanuel Onwubiko. "Combating insider fraud in Financial Institutions/impact." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 14 (January 17, 2019): 3351–58. http://dx.doi.org/10.24297/ijmit.v14i0.8010.

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The fear of fraud is constant. Unfortunately, now more than ever before, fraud is being committed by employees on the inside, the very people who are supposed to be supporting and protecting an organization. Even though the financial industry is one of the most regulated, financial institution are still getting with the highest rate of internal fraud. Insider threat has always existed within each Financial Institution. In the recent years, insider threat has become a more prominent issue because of the emerging trends in the workplace. This change to a more flexible and productive workplace en

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