The 2017 homograph browser attack mitigation survey

Since their inception, International Domain Names (IDN) have allowed for non-Latin characters to be entered into domain names. This feature has led to attackers forging malicious domains which appear identical to the Latin counterpart. This is achieved through using non-Latin characters which appear identical to their Latin counterpart. This attack is referred to as a Homograph attack. This research continues the work of Hannay and Bolan (2009), and Hannay and Baatard (2012), which assessed the mitigation methods incorporated by web browsers in mitigating IDN homograph attacks. Since these works, time IDN mitigation algorithms have been altered, such as the one used in Mozilla Firefox (Gerv, 2017). This study evaluates browser homograph attack mitigation strategies in browsers released post-2011. In this study, we find a high level of effective multi-script mitigation across the browser families surveyed. Notable exceptions to this include a single version of Firefox in which the mitigation features were not present and ongoing omission of mitigation against single script attacks

Assessment of Internationalised Domain Name Homograph Attack Mitigation

With the advent of internationalised domains the threat posed by non-english character sets has eventuated. Whilst this phenomenon remains well known in the development and internet industry the actual implementations of popular applications have been tested to determine their resilience to homograph based attack. The research found that most provided features that overcome such attacks, but there remain a few notable exceptions. Should an attacker take advantage of such oversights a victim would likely not be able to spot a fraudulent site or email and thus provide a perfect platform for subsequent attack

The 2011 IDN Homograph Attack Mitigation Survey

The advent of internationalized domain names (IDNs) has introduced a new threat, with the non-English character sets allowing for visual mimicry of domain names. Whilst this potential for this form of attack has been well recognized, many applications such as Internet browsers and e-mail clients have been slow to adopt successful mitigation strategies and countermeasures. This research examines those strategies and countermeasures, identifying areas of weakness that allow for homograph attacks. As well as examining the presentation of IDNs in e-mail clients and Internet browser URL bars, this year’s study examines the presentation of IDNs in browser-based security certificates and requests for locational data access

ミャンマー語テキストの形式手法による音節分割、正規化と辞書順排列

国立大学法人長岡技術科学大

Teaching Tip: Hook, Line, and Sinker – The Development of a Phishing Exercise to Enhance Cybersecurity Awareness

In this paper, we describe the development of an in-class exercise designed to teach students how to craft social engineering attacks. Specifically, we focus on the development of phishing emails. Providing an opportunity to craft offensive attacks not only helps prepare students for a career in penetration testing but can also enhance their ability to detect and defend against similar methods. First, we discuss the relevant background. Second, we outline the requirements necessary to implement the exercise. Third, we describe how we implemented the exercise. Finally, we discuss our results and share student feedback

The Proceedings of 15th Australian Information Security Management Conference, 5-6 December, 2017, Edith Cowan University, Perth, Australia

Conference Foreword The annual Security Congress, run by the Security Research Institute at Edith Cowan University, includes the Australian Information Security and Management Conference. Now in its fifteenth year, the conference remains popular for its diverse content and mixture of technical research and discussion papers. The area of information security and management continues to be varied, as is reflected by the wide variety of subject matter covered by the papers this year. The papers cover topics from vulnerabilities in “Internet of Things” protocols through to improvements in biometric identification algorithms and surveillance camera weaknesses. The conference has drawn interest and papers from within Australia and internationally. All submitted papers were subject to a double blind peer review process. Twenty two papers were submitted from Australia and overseas, of which eighteen were accepted for final presentation and publication. We wish to thank the reviewers for kindly volunteering their time and expertise in support of this event. We would also like to thank the conference committee who have organised yet another successful congress. Events such as this are impossible without the tireless efforts of such people in reviewing and editing the conference papers, and assisting with the planning, organisation and execution of the conference. To our sponsors, also a vote of thanks for both the financial and moral support provided to the conference. Finally, thank you to the administrative and technical staff, and students of the ECU Security Research Institute for their contributions to the running of the conference

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