No abstract

IoT repackaging refers to an attack devoted to tampering with a legitimate firmware package by modifying its content (e.g., injecting some malicious code) and re-distributing it in the wild. In such a scenario, the firmware delivery and update processes play a central role in ensuring firmware integrity. Unfortunately, most of the existing solutions lack proper integrity verification, leaving firmware exposed to repackaging attacks, such as the one reported in [1]. If this is not the case, they still require an external trust anchor (e.g., a signing certificate), which could limit their adoption in resource-constrained environments.To mitigate such a problem, in this paper, we introduce PATRIOT, a novel self-protecting scheme for IoT that allows the injection of integrity checks, called anti-tampering (AT) controls, directly into the firmware. The AT controls enable the runtime detection of repackaging attempts without the need for external trust anchors or computationally expensive systems. Also, we have implemented this scheme into PATRIOTIC, a prototype to automatically protect C/C++ IoT firmware. The evaluation phase of 33 real-world firmware samples demonstrated the feasibility of the proposed methodology and its robustness against practical repackaging attacks without altering the firmware behavior or severe performance issues.

No abstract

<p>From a little research experiment to an essential component of military arsenals, malicious software has constantly been growing and evolving for more than three decades. On the other hand, from a negligible market share, the Android operating system is nowadays the most widely used mobile operating system, becoming a desirable target for large-scale malware distribution. While scientific literature has followed this trend, one aspect has been understudied: the role of native code in malicious Android apps. Android apps are written in high-level languages, but thanks to the Java Native Interface (JNI), Android also supports calling native (C/C++) library functions. While allowing native code in Android apps has a strong positive impact from a performance perspective, it dramatically complicates its analysis because bytecode and native code need different abstractions and analysis algorithms, and they thus pose different challenges and limitations. Consequently, these difficulties are often (ab)used to hide malicious payloads. In this work, we propose a novel methodology to reverse engineering Android apps focusing on suspicious patterns related to native components, i.e., surreptitious code that requires further inspection. We implemented a static analysis tool based on such methodology, which can bridge the “Java” and the native worlds and perform an in-depth analysis of tag code blocks responsible for suspicious behavior. These tags benefit the human facing the reverse engineering task: they clearly indicate which part of the code to focus on to find malicious code. Then, we performed a longitudinal analysis of Android malware over the past ten years and compared the recent malicious samples with actual top apps on the Google Play Store. Our work depicts typical behaviors of modern malware, its evolution, and how it abuses the native layer to complicate the analysis, especially with dynamic code loading and novel anti-analysis techniques. Finally, we show a use case for our suspicious tags: we trained and tested a machine learning algorithm for a binary classification task. Even if suspicious does not imply malicious, our classifier obtained a remarkable F1-score of 0.97, showing that our methodology can be helpful to both humans and machines.</p>

<p>From a little research experiment to an essential component of military arsenals, malicious software has constantly been growing and evolving for more than three decades. On the other hand, from a negligible market share, the Android operating system is nowadays the most widely used mobile operating system, becoming a desirable target for large-scale malware distribution. While scientific literature has followed this trend, one aspect has been understudied: the role of native code in malicious Android apps. Android apps are written in high-level languages, but thanks to the Java Native Interface (JNI), Android also supports calling native (C/C++) library functions. While allowing native code in Android apps has a strong positive impact from a performance perspective, it dramatically complicates its analysis because bytecode and native code need different abstractions and analysis algorithms, and they thus pose different challenges and limitations. Consequently, these difficulties are often (ab)used to hide malicious payloads. In this work, we propose a novel methodology to reverse engineering Android apps focusing on suspicious patterns related to native components, i.e., surreptitious code that requires further inspection. We implemented a static analysis tool based on such methodology, which can bridge the “Java” and the native worlds and perform an in-depth analysis of tag code blocks responsible for suspicious behavior. These tags benefit the human facing the reverse engineering task: they clearly indicate which part of the code to focus on to find malicious code. Then, we performed a longitudinal analysis of Android malware over the past ten years and compared the recent malicious samples with actual top apps on the Google Play Store. Our work depicts typical behaviors of modern malware, its evolution, and how it abuses the native layer to complicate the analysis, especially with dynamic code loading and novel anti-analysis techniques. Finally, we show a use case for our suspicious tags: we trained and tested a machine learning algorithm for a binary classification task. Even if suspicious does not imply malicious, our classifier obtained a remarkable F1-score of 0.97, showing that our methodology can be helpful to both humans and machines.</p>