An Insider Threat Activity in a Software Security Course

Software development teams face a critical threat to the security of their systems: insiders. A malicious insider is a person who violates an authorized level of access in a software system. Unfortunately, when creating software, developers do not typically account for insider threat. Students learning software development are unaware of the impacts of malicious actors and are far too often untrained in prevention methods against them. A few of the defensive mechanisms to protect against insider threats include eliminating system access once an employee leaves an organization, enforcing principle of least privilege, code reviews, and constant monitoring for suspicious activity. At the Department of Software Engineering at the Rochester Institute of Technology, we require a course titled Engineering of Secure Software and have created an activity designed to prepare students for the problem of insider threats. At the beginning of this activity, student teams are given the task of designing a moderately sized secure software system. The goal of this insider is to manipulate the team into creating a flawed system design that would allow attackers to perform malicious activities once the system has been created. When the insider is revealed at the conclusion of the project, students discuss countermeasures regarding the malicious actions the insiders were able to plan or complete, along with methods of prevention that may have been employed by the team to detect the malicious developer. In this paper, we describe the activity along with the results of a survey. We discuss the benefits and challenges of the activity with the goal of giving other instructors the tools they need to conduct this activity at their institution. While many institutions do not offer courses in computer security, this self-contained activity may be used in any computing course to enforce the importance of protecting against insider threats

Assessing Vulnerabilities of Biometric Readers Using an Applied Defeat Evaluation Methodology

Access control systems using biometric identification readers are becoming common within critical infrastructure and other high security applications. There is a perception that biometric, due to their ability to identify and validate the user, are more secure. However, biometric systems are vulnerable to many categories of attack vectors and there has been restricted research into such defeat vulnerabilities. This study expands on a past article (Brooks, 2009) that presented a defeat evaluation methodology applied to high-security biometric readers. The defeat methodology is represented, but applied to both fingerprint and back-of-hand biometric readers. Defeat evaluation included both physical and technical integrity testing, considering zero-effort to adversarial complex attacks. In addition, the evaluation considered the whole device and not just the biometric extraction and storage device. The study found a number of common vulnerabilities in the various types of biometric readers. Vulnerabilities included the ability to spoof optical readers with another person’s extracted print, use of inanimate objects to enrol and validate, defeat of live detection and the ability to by-pass the biometric reader. Optical sensors appeared the least secure, with capacitive the most secure. An awareness of the vulnerabilities and limitations of biometric readers need to be propagated, as such readers should not be considered high-security by default. As this study demonstrated, most of the readers had some inherent vulnerability that was not difficult to exploit, in particular, from an insider’s perspective

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