Threat Modelling for Active Directory

This paper analyses the security threats that can arise against an Active Directory server when it is included in a Web application. The approach is based on the STRIDE classification methodology. The paper also provides outline descriptions of countermeasures that can be deployed to protect against the different threats and vulnerabilities identified here

An Evasion Attack against ML-based Phishing URL Detectors

Background: Over the year, Machine Learning Phishing URL classification (MLPU) systems have gained tremendous popularity to detect phishing URLs proactively. Despite this vogue, the security vulnerabilities of MLPUs remain mostly unknown. Aim: To address this concern, we conduct a study to understand the test time security vulnerabilities of the state-of-the-art MLPU systems, aiming at providing guidelines for the future development of these systems. Method: In this paper, we propose an evasion attack framework against MLPU systems. To achieve this, we first develop an algorithm to generate adversarial phishing URLs. We then reproduce 41 MLPU systems and record their baseline performance. Finally, we simulate an evasion attack to evaluate these MLPU systems against our generated adversarial URLs. Results: In comparison to previous works, our attack is: (i) effective as it evades all the models with an average success rate of 66% and 85% for famous (such as Netflix, Google) and less popular phishing targets (e.g., Wish, JBHIFI, Officeworks) respectively; (ii) realistic as it requires only 23ms to produce a new adversarial URL variant that is available for registration with a median cost of only $11.99/year. We also found that popular online services such as Google SafeBrowsing and VirusTotal are unable to detect these URLs. (iii) We find that Adversarial training (successful defence against evasion attack) does not significantly improve the robustness of these systems as it decreases the success rate of our attack by only 6% on average for all the models. (iv) Further, we identify the security vulnerabilities of the considered MLPU systems. Our findings lead to promising directions for future research. Conclusion: Our study not only illustrate vulnerabilities in MLPU systems but also highlights implications for future study towards assessing and improving these systems.Comment: Draft for ACM TOP

Resilient Risk based Adaptive Authentication and Authorization (RAD-AA) Framework

In recent cyber attacks, credential theft has emerged as one of the primary vectors of gaining entry into the system. Once attacker(s) have a foothold in the system, they use various techniques including token manipulation to elevate the privileges and access protected resources. This makes authentication and token based authorization a critical component for a secure and resilient cyber system. In this paper we discuss the design considerations for such a secure and resilient authentication and authorization framework capable of self-adapting based on the risk scores and trust profiles. We compare this design with the existing standards such as OAuth 2.0, OpenID Connect and SAML 2.0. We then study popular threat models such as STRIDE and PASTA and summarize the resilience of the proposed architecture against common and relevant threat vectors. We call this framework as Resilient Risk based Adaptive Authentication and Authorization (RAD-AA). The proposed framework excessively increases the cost for an adversary to launch and sustain any cyber attack and provides much-needed strength to critical infrastructure. We also discuss the machine learning (ML) approach for the adaptive engine to accurately classify transactions and arrive at risk scores

Using Capec Attack Patterns For Developing Abuse Cases

To engineer secure software, it is imperative to understand attackers’ perspectives and approaches. This information has been captured by attack patterns. The Common Attack Patterns Enumeration Classification (CAPEC) repository hosts over 450 attack patterns that contain information about how attacks have been launched against software. Researches have indicated that attack patterns can be utilized for developing secure software; however, there exists no systematic methodology to address this concern. This research proposes a methodology for utilizing CAPEC attack patterns for developing abuse cases at the requirements stage of the secure software development lifecycle (SDLC). In previous research, a tool for retrieving attack patterns (TrAP) was developed to retrieve CAPEC attack patterns according to Microsoft STRIDE threat categories. This tool also features a search function using keywords. The proposed methodology starts with a set of initial abuse cases developed through brainstorming. Microsoft SDL threat modelling tool is then used to identify and rank possible security threats in the system. The SDL tool generates a series of questions for each threat and these questions are used to extract keywords that serve as input to the TrAP tool to retrieve attack patterns relevant to the abuse cases. Keywords can also be system prerequisites or any technology being implemented in the system. From the list of retrieved attack patterns, the most relevant attack patterns are selected and used to extend the initial abuse cases. New abuse cases can also be discovered through this process

An investigation into the usability and acceptability of multi-channel authentication to online banking users in Oman

Authentication mechanisms provide the cornerstone for security for many distributed systems, especially for increasingly popular online applications. For decades, widely used, traditional authentication methods included passwords and PINs that are now inadequate to protect online users and organizations from ever more sophisticated attacks. This study proposes an improvement to traditional authentication mechanisms. The solution introduced here includes a one-time-password (OTP) and incorporates the concept of multiple levels and multiple channels – features that are much more successful than traditional authentication mechanisms in protecting users' online accounts from being compromised. This research study reviews and evaluates current authentication classes and mechanisms and proposes an authentication mechanism that uses a variety of techniques, including multiple channels, to resist attacks more effectively than most commonly used mechanisms. Three aspects of the mechanism were evaluated: 1. The security of multi-channel authentication (MCA) was evaluated in theoretical terms, using a widely accepted methodology. 2. The usability was evaluated by carrying out a user study. 3. Finally, the acceptability thereof was evaluated by asking the participants in study (2) specific questions which aligned with the technology acceptance model (TAM). The study’s analysis of the data, gathered from online questionnaires and application log tables, showed that most participants found the MCA mechanism superior to other available authentication mechanisms and clearly supported the proposed MCA mechanism and the benefits that it provides. The research presents guidelines on how to implement the proposed mechanism, provides a detailed analysis of its effectiveness in protecting users' online accounts against specific, commonly deployed attacks, and reports on its usability and acceptability. It represents a significant step forward in the evolution of authentication mechanisms meeting the security needs of online users while maintaining usability