A Worst Practices Guide to Insider Threats: Lessons from Past Mistakes

Insider threats are perhaps the most serious challenges that nuclear security systems face. All of the cases of theft of nuclear materials where the circumstances of the theft are known were perpetrated either by insiders or with the help of insiders; given that the other cases involve bulk material stolen covertly without anyone being aware the material was missing, there is every reason to believe that they were perpetrated by insiders as well. Similarly, disgruntled workers from inside nuclear facilities have perpetrated many of the known incidents of nuclear sabotage. The most recent example of which we are aware is the apparent insider sabotage of a diesel generator at the San Onofre nuclear plant in the United States in 2012; the most spectacular was an incident three decades ago in which an insider placed explosives directly on the steel pressure vessel head of a nuclear reactor and then detonated them.While many such incidents, including the two just mentioned, appear to have been intended to send a message to management, not to spread radioactivity, they highlight the immense dangers that could arise from insiders with more malevolent intent. As it turns out, insiders perpetrate a large fraction of thefts from heavily guarded non-nuclear facilities as well. Yet organizations often find it difficult to understandand protect against insider threats. Why is this the case?Part of the answer is that there are deep organizational and cognitive biases that lead managers to downplay the threats insiders pose to their nuclear facilities and operations. But another part of the answer is that those managing nuclear security often have limited information about incidents that have happened in other countries or in other industries, and the lessons that might be learned from them.The IAEA and the World Institute for Nuclear Security (WINS) produce"best practices" guides as a way of disseminating ideas and procedures that have been identified as leading to improved security. Both have produced guides on protecting against insider threats.5 But sometimes mistakes are even moreinstructive than successes.Here, we are presenting a kind of "worst practices" guide of serious mistakes made in the past regarding insider threats. While each situation is unique, and serious insider problems are relatively rare, the incidents we describe reflect issues that exist in many contexts and that every nuclear security manager should consider. Common organizational practices -- such as prioritizing production over security, failure to share information across subunits, inadequate rules or inappropriate waiving of rules, exaggerated faith in group loyalty, and excessive focus on external threats -- can be seen in many past failures to protect against insider threats

A review of behavioural research on data security

Protection of confidential information or data from being leaked to the public is a growing concern among organisations and individuals. This paper presents the results of the search for literature on behavioural and security aspects of data protection. The topics covered by this review include a summary of the changes brought about by the EU GDPR (General Data Protection Regulation). It covers human and behavioural aspects of data protection, security and data breach or loss (threats), IT architectures to protect data (prevention), managing data breaches (mitigation), risk assessment and data protection audits. A distinction is made between threats and prevention from within an organisation and from the outside

Detection Of Insider Attacks In Block Chain Network Using The Trusted Two Way Intrusion Detection System

For data privacy, system reliability, and security, Blockchain technologies have become more popular in recent years. Despite its usefulness, the blockchain is vulnerable to cyber assaults; for example, in January 2019 a 51% attack on Ethereum Classic successfully exposed flaws in the platform's security. From a statistical point of view, attacks represent a highly unusual occurrence that deviates significantly from the norm. Blockchain attack detection may benefit from Deep Learning, a field of study whose aim is to discover insights, patterns, and anomalies within massive data repositories. In this work, we define an trusted two way intrusion detection system based on a Hierarchical weighed fuzzy algorithm and self-organized stacked network (SOSN) deep learning model, that is trained exploiting aggregate information extracted by monitoring blockchain activities. Here initially the smart contract handles the node authentication. The purpose of authenticating the node is to ensure that only specific nodes can submit and retrieve the information. We implement Hierarchical weighed fuzzy algorithm to evaluate the trust ability of the transaction nodes. Then the transaction verification step ensures that all malicious transactions or activities on the submitted transaction by self-organized stacked network deep learning model. The whole experimentation was carried out under matlab environment. Extensive experimental results confirm that our suggested detection method has better performance over important indicators such as Precision, Recall, F-Score, overhead

Development and Validation of a Proof-of-Concept Prototype for Analytics-based Malicious Cybersecurity Insider Threat in a Real-Time Identification System

Insider threat has continued to be one of the most difficult cybersecurity threat vectors detectable by contemporary technologies. Most organizations apply standard technology-based practices to detect unusual network activity. While there have been significant advances in intrusion detection systems (IDS) as well as security incident and event management solutions (SIEM), these technologies fail to take into consideration the human aspects of personality and emotion in computer use and network activity, since insider threats are human-initiated. External influencers impact how an end-user interacts with both colleagues and organizational resources. Taking into consideration external influencers, such as personality, changes in organizational polices and structure, along with unusual technical activity analysis, would be an improvement over contemporary detection tools used for identifying at-risk employees. This would allow upper management or other organizational units to intervene before a malicious cybersecurity insider threat event occurs, or mitigate it quickly, once initiated. The main goal of this research study was to design, develop, and validate a proof-of-concept prototype for a malicious cybersecurity insider threat alerting system that will assist in the rapid detection and prediction of human-centric precursors to malicious cybersecurity insider threat activity. Disgruntled employees or end-users wishing to cause harm to the organization may do so by abusing the trust given to them in their access to available network and organizational resources. Reports on malicious insider threat actions indicated that insider threat attacks make up roughly 23% of all cybercrime incidents, resulting in $2.9 trillion in employee fraud losses globally. The damage and negative impact that insider threats cause was reported to be higher than that of outsider or other types of cybercrime incidents. Consequently, this study utilized weighted indicators to measure and correlate simulated user activity to possible precursors to malicious cybersecurity insider threat attacks. This study consisted of a mixed method approach utilizing an expert panel, developmental research, and quantitative data analysis using the developed tool on simulated data set. To assure validity and reliability of the indicators, a panel of subject matter experts (SMEs) reviewed the indicators and indicator categorizations that were collected from prior literature following the Delphi technique. The SMEs’ responses were incorporated into the development of a proof-of-concept prototype. Once the proof-of-concept prototype was completed and fully tested, an empirical simulation research study was conducted utilizing simulated user activity within a 16-month time frame. The results of the empirical simulation study were analyzed and presented. Recommendations resulting from the study also be provided

Root causes, ongoing difficulties, proactive prevention techniques, and emerging trends of enterprise data breaches

A data breach in the modern digital era is the unintentional or intentional disclosure of private data to uninvited parties. Businesses now consider data to be a crucial asset, and any breach of this data can have dire repercussions, including harming a company's brand and resulting in losses. Enterprises now place a high premium on detecting and preventing data loss due to the growing amount of data and the increasing frequency of data breaches. Even with a great deal of research, protecting sensitive data is still a difficult task. This review attempts to highlight interesting prospects and offer insightful information to those who are interested in learning about the risks that businesses face from data leaks, current occurrences, state-of-the-art methods for detection and prevention, new difficulties, and possible solutions.Comment: 17 pages, 9 figure

An Insider Threat Categorization Framework for Automated Manufacturing Execution System

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 literature a standard theoretical categorization of insider threats framework for AMESs has been proposed. Hence, three principle factors, i.e. Human, Systems and Machine have considered as major categorization of insider threats. Consequently, the possible features for each factor identified based on previous researcher recommendations. Therefore, via identifying possible features and categorize it into principle factors or groups, a standard framework could be derived. These frameworks will contribute more benefit specifically in the manufacturing field as a reference to mitigate an insider threat.   Keywords—automated manufacturing execution systems insider threats, factors and features, insider threat categorization framework

Impact and key challenges of insider threats on organizations and critical businesses

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 human behavior and psychological factors. The existing defense techniques are discussed and critically analyzed, and improvements are suggested, in line with the current state-of-the-art cyber security requirements. Finally, open problems related to the insider threat are identified and future research directions are discussed

A Privacy-Preserving, Context-Aware, Insider Threat prevention and prediction model (PPCAITPP)

The insider threat problem is extremely challenging to address, as it is committed by insiders who are trusted and authorized to access the information resources of the organization. The problem is further complicated by the multifaceted nature of insiders, as human beings have various motivations and fluctuating behaviours. Additionally, typical monitoring systems may violate the privacy of insiders. Consequently, there is a need to consider a comprehensive approach to mitigate insider threats. This research presents a novel insider threat prevention and prediction model, combining several approaches, techniques and tools from the fields of computer science and criminology. The model is a Privacy- Preserving, Context-Aware, Insider Threat Prevention and Prediction model (PPCAITPP). The model is predicated on the Fraud Diamond (a theory from Criminology) which assumes there must be four elements present in order for a criminal to commit maleficence. The basic elements are pressure (i.e. motive), opportunity, ability (i.e. capability) and rationalization. According to the Fraud Diamond, malicious employees need to have a motive, opportunity and the capability to commit fraud. Additionally, criminals tend to rationalize their malicious actions in order for them to ease their cognitive dissonance towards maleficence. In order to mitigate the insider threat comprehensively, there is a need to consider all the elements of the Fraud Diamond because insider threat crime is also related to elements of the Fraud Diamond similar to crimes committed within the physical landscape. The model intends to act within context, which implies that when the model offers predictions about threats, it also reacts to prevent the threat from becoming a future threat instantaneously. To collect information about insiders for the purposes of prediction, there is a need to collect current information, as the motives and behaviours of humans are transient. Context-aware systems are used in the model to collect current information about insiders related to motive and ability as well as to determine whether insiders exploit any opportunity to commit a crime (i.e. entrapment). Furthermore, they are used to neutralize any rationalizations the insider may have via neutralization mitigation, thus preventing the insider from committing a future crime. However, the model collects private information and involves entrapment that will be deemed unethical. A model that does not preserve the privacy of insiders may cause them to feel they are not trusted, which in turn may affect their productivity in the workplace negatively. Hence, this thesis argues that an insider prediction model must be privacy-preserving in order to prevent further cybercrime. The model is not intended to be punitive but rather a strategy to prevent current insiders from being tempted to commit a crime in future. The model involves four major components: context awareness, opportunity facilitation, neutralization mitigation and privacy preservation. The model implements a context analyser to collect information related to an insider who may be motivated to commit a crime and his or her ability to implement an attack plan. The context analyser only collects meta-data such as search behaviour, file access, logins, use of keystrokes and linguistic features, excluding the content to preserve the privacy of insiders. The model also employs keystroke and linguistic features based on typing patterns to collect information about any change in an insider’s emotional and stress levels. This is indirectly related to the motivation to commit a cybercrime. Research demonstrates that most of the insiders who have committed a crime have experienced a negative emotion/pressure resulting from dissatisfaction with employment measures such as terminations, transfers without their consent or denial of a wage increase. However, there may also be personal problems such as a divorce. The typing pattern analyser and other resource usage behaviours aid in identifying an insider who may be motivated to commit a cybercrime based on his or her stress levels and emotions as well as the change in resource usage behaviour. The model does not identify the motive itself, but rather identifies those individuals who may be motivated to commit a crime by reviewing their computer-based actions. The model also assesses the capability of insiders to commit a planned attack based on their usage of computer applications and measuring their sophistication in terms of the range of knowledge, depth of knowledge and skill as well as assessing the number of systems errors and warnings generated while using the applications. The model will facilitate an opportunity to commit a crime by using honeypots to determine whether a motivated and capable insider will exploit any opportunity in the organization involving a criminal act. Based on the insider’s reaction to the opportunity presented via a honeypot, the model will deploy an implementation strategy based on neutralization mitigation. Neutralization mitigation is the process of nullifying the rationalizations that the insider may have had for committing the crime. All information about insiders will be anonymized to remove any identifiers for the purpose of preserving the privacy of insiders. The model also intends to identify any new behaviour that may result during the course of implementation. This research contributes to existing scientific knowledge in the insider threat domain and can be used as a point of departure for future researchers in the area. Organizations could use the model as a framework to design and develop a comprehensive security solution for insider threat problems. The model concept can also be integrated into existing information security systems that address the insider threat problemInformation ScienceD. Phil. (Information Systems