CAPTCHAs are widely used today as a reliable method to set up a Turing test to discern between humans and computers. With the improvements in AI technology, many AI hard problems could now be solved with new techniques, for example, better Optical Character Recognition models. This work highlights the possibility of using adversarial defences techniques such as Spatial smoothing and JPEG compression to defeat image classification CAPTCHAs.

With the indisputable prevalence of PDFs, several studies into PDF malware and their evasive variants have been conducted to test the robustness of ML-based PDF classifier frameworks, Hidost and Mimicus. As heavily documented, the fundamental difference between them is that Hidost investigates the logical structure of PDFs, while Mimicus detects malicious indicators through their structural features. However, there exists techniques to mutate such features such that malicious PDFs are able to bypass these classifiers. In this work, we investigated three known attacks: Mimicry, Mimicry+, and Reverse Mimicry to compare how effective they are in evading classifiers in Hidost and Mimicus. The results shows that Mimicry and Mimicry+ are effective in bypassing models in Mimicus but not in Hidost, while Reverse Mimicy is effective against both models in Mimicus and Hidost.

In this paper we discuss a technique to safeguard specific airspace from intruding drones with the help of surveillance drones. The idea is to use multiple surveillance drones to patrol through the area looking for suspicious flying objects. The surveillance drones are trained to identify permissible drones in the area and hostile drones using image recognition algorithms. Once a hostile drone is detected the surveillance drones surround it making it difficult to maneuver. In the meantime, our automated drone attack framework launches cyber-attacks against the hostile drone to bring it down.

Drones are usually associated with the military but in recent times, they are also used for public and commercial interests such as transporting of goods, communications, agriculture, disaster mitigation and environment preservation. However, like any system, drones have vulnerabilities that can be exploited which can jeopardise a drone's operation and may lead to loss of lives, property and money. Thus drones deployed must be carefully evaluated and selected. Pen-testing is a way to assess the vulnerabilities of drones but it may require multiple commands, files or scripts. In this work, we propose a tool to allow easy pen-testing and assessment of drones. Vulnerability assessment of the DJI Mavic 2 Pro is discussed extensively as well. Future work includes addressing the vulnerabilities of other drones and expanding the tool to conduct pen-testing on other drones.

Obfuscation techniques have been conventionally used for legitimate applications, including preventing application reverse engineering, tampering and protecting intellectual property. A malware author could also leverage these benign techniques to hide their malicious intents and evade anti-malware detection. As variants of known malware have been regularly found on the Google Play Store, transformed malware attacks are a real problem that security solutions today need to address. It has been proven that mainstream security tools installed on smartphones are mainly signature-based; our work focuses on evaluating the efficiency of a composite of obfuscation techniques in evading anti-malware detection. We further verified the trend of transformed malware in evading detection, with a larger and more updated database of known malware. This is also the first work to-date that presents the instability of some anti-malware tools (AMTs) against obfuscated malware. This work also proved that current mainstream AMTs do not build up resilience against obfuscation methods, but instead try to update the signature database on created variants.

Android applications are vulnerable to reverse engineering which could result in tampering and repackaging of applications. Even though there are many off the shelf obfuscation tools that hardens Android applications, they are limited to basic obfuscation techniques. Obfuscation techniques that transform the code segments drastically are difficult to implement on Android because of the Android runtime verifier which validates the loaded code. In this paper, we introduce a novel obfuscation technique, Android Encryption based Obfuscation (AEON), which can encrypt code segments and perform runtime decryption during execution. The encrypted code is running outside of the normal Android virtual machine, in an embeddable Java source interpreter and thereby circumventing the scrutiny of Android runtime verifier. Our obfuscation technique works well with Android source code and Dalvik bytecode.