The effect of cyber-attacks on stock returns

A widely debated issue in recent years is cybercrime. Breaches in the security of accessibility, integrity and confidentiality of information involve potentially high explicit and implicit costs for firms. This paper investigates the impact of information security breaches on stock returns. Using event-study methodology, the study provides empirical evidence on the effect of announcements of cyber-attacks on the market value of firms from 1995 to 2015. Results show that substantial negative market returns occur following announcements of cyber-attacks. Financial entities often suffer greater negative effects than other companies and non-confidential cyber-attacks are the most dangerous, especially for the financial sector. Overall findings seem to show a link between cybercrime and insider trading

Stopping Insiders before They Attack: Understanding Motivations and Drivers

Insider attacks are able to evade traditional security controls because the perpetrators of the attack often have legitimate access to protected systems and data. Massive logging of user online activity data (e.g. file access or transfer, use of data storage devices, email records) is collected and analyzed to detect insider attacks (e.g. data theft, fraud, policy violation, etc.). Such techniques are fraught with drawbacks and limitations: 1) the proverbial “needle in a haystack problem,” where very little useful information is found in massive data sets, especially where the incidence of malicious insider activities is very small compared to that of legitimate actors; 2) employee privacy issues may exist about the company monitoring employee behavior; and 3) these techniques are largely wanting in their accuracy, leading to notably high false positive rates. Perhaps the most salient limitation of these techniques is that the analyses are post-hoc, and by the time the activity is detected, the insider has already engaged in data theft or exfiltration, the impact of which may not be reversible. This paper discusses the concept of using probes for detection of threats, wherein user intentions to engage in insider attacks can be gauged by sending carefully designed probes that rouse malicious users into acting. In this research, we seek a broad understanding of the scope and relevance of such probes. There are various motivations for users to steal data, including financial gain, patriotic fervor, and disgruntlement with work. In the present experiment, we created simulated conditions to reflect common insider motivations by providing subjects with imagined scenarios, then asking them to take the perspective of insiders in those scenarios, and explicate their actions through a series of structured questions that mimic our probes. The results show the effect of different scenarios in motivating the users, and the effectiveness of different probes in eliciting their actions

A multiple-perspective approach for insider-threat risk prediction in cyber-security

Currently governments and research communities are concentrating on insider threat matters more than ever, the main reason for this is that the effect of a malicious insider threat is greater than before. Moreover, leaks and the selling of the mass data have become easier, with the use of the dark web. Malicious insiders can leak confidential data while remaining anonymous. Our approach describes the information gained by looking into insider security threats from the multiple perspective concepts that is based on an integrated three-dimensional approach. The three dimensions are human issue, technology factor, and organisation aspect that forms one risk prediction solution. In the first part of this thesis, we give an overview of the various basic characteristics of insider cyber-security threats. We also consider current approaches and controls of mitigating the level of such threats by broadly classifying them in two categories: a) technical mitigation approaches, and b) non-technical mitigation approaches. We review case studies of insider crimes to understand how authorised users could harm their organisations by dividing these cases into seven groups based on insider threat categories as follows: a) insider IT sabotage, b) insider IT fraud, c) insider theft of intellectual property, d) insider social engineering, e) unintentional insider threat incident, f) insider in cloud computing, and g) insider national security. In the second part of this thesis, we present a novel approach to predict malicious insider threats before the breach takes place. A prediction model was first developed based on the outcomes of the research literature which highlighted main prediction factors with the insider indicator variables. Then Bayesian network statistical methods were used to implement and test the proposed model by using dummy data. A survey was conducted to collect real data from a single organisation. Then a risk level and prediction for each authorised user within the organisation were analysed and measured. Dynamic Bayesian network model was also proposed in this thesis to predict insider threats for a period of time, based on data collected and analysed on different time scales by adding time series factors to the previous model. Results of the verification test comparing the output of 61 cases from the education sector prediction model show a good consistence. The correlation was generally around R-squared =0.87 which indicates an acceptable fit in this area of research. From the result we expected that the approach will be a useful tool for security experts. It provides organisations with an insider threat risk assessment to each authorised user and also organisations can discover their weakness area that needs attention in dealing with insider threat. Moreover, we expect the model to be useful to the researcher's community as the basis for understanding and future research

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 (inclusiVe InSider Threat 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

How often is Employee Anger an Insider Risk I? Detecting and Measuring Negative Sentiment versus Insider Risk in Digital Communications

This research introduced two new scales for the identification and measurement of negative sentiment and insider risk in communications in order to examine the unexplored relationship between these two constructs. The inter-rater reliability and criterion validity of the Scale of Negativity in Texts (SNIT) and the Scale of Insider Risk in Digital Communications (SIRDC) were established with a random sample of email from the Enron archive and criterion measures from established insiders, disgruntled employees, suicidal, depressed, angry, anxious, and other sampled groups. In addition, the sensitivity of the scales to changes over time as the risk of digital attack increased and transitioned to a physical attack was also examined in an actual case study. Inter-rater reliability for the SNIT was extremely high across groups while the SIRDC produced lower, but acceptable levels of agreement. Both measures also significantly distinguished the criterion groups from the overall Enron sample. The scales were then used to measure the frequency of negative sentiment and insider risk indicators in the random Enron sample and the relationship between the two constructs. While low levels of negative sentiment were found in 20% of the sample, moderate and high levels of negative sentiment were extremely rare, occurring in less than 1% of communications. Less than 4% of the sampled emails displayed indicators of insider risk on the SIRDC. Emails containing high levels of insider risk comprised less than one percent or the sample. Of the emails containing negative sentiment in the sample, only 16.3%, also displayed indicators of insider risk. The odds of a communication containing insider risk increased with the level of negative sentiment and only low levels of insider risk were found at low levels of negative sentiment. All of the emails found to contain insider risk indicators on the SIRDC also displayed some level of negative sentiment. The implications of these findings for insider risk detection were then examined

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