The Internet of Things (IoT) is constituted of devices that are exponentially growing in number and in complexity. They use numerous customized firmware and hardware, without taking into consideration security issues, which make them a target for cybercriminals, especially malware authors.We will present a novel approach of using side channel information to identify the kinds of threats that are targeting the device. Using our approach, a malware analyst is able to obtain precise knowledge about malware type and identity, even in the presence of obfuscation techniques which may prevent static or symbolic binary analysis. We recorded 100,000 measurement traces from an IoT device infected by various in-the-wild malware samples and realistic benign activity. Our method does not require any modification on the target device. Thus, it can be deployed independently from the resources available without any overhead. Moreover, our approach has the advantage that it can hardly be detected and evaded by the malware authors. In our experiments, we were able to predict three generic malware types (and one benign class) with an accuracy of 99.82%. Even more, our results show that we are able to classify altered malware samples with unseen obfuscation techniques during the training phase, and to determine what kind of obfuscations were applied to the binary, which makes our approach particularly useful for malware analysts.
CCS CONCEPTS• Security and privacy → Malware and its mitigation.
With macOS increasing popularity, the number, and variety of macOS malware are rising as well. Yet, very few tools exist for dynamic analysis of macOS malware. In this paper, we propose a macOS malware analysis framework called Mac-A-Mal. We develop a kernel extension to monitor malware behavior and mitigate several anti-evasion techniques used in the wild. Our framework exploits the macOS features of XPC service invocation that typically escape traditional mechanisms for detection of children processes. Performance benchmarks show that our system is comparable with professional tools and able to withstand VM detection. By using Mac-A-Mal, we discovered 71 unknown adware samples (8 of them using valid distribution certificates), 2 keyloggers, and 1 previously unseen trojan involved in the APT32 OceanLotus.
In this poster we present a novel approach of using side channel information to identify the kinds of malware threats that are targeting IoT devices. Although in the presence of obfuscation techniques that can prevent static or symbolic binary analysis, a malware researcher may obtain detailed information about malware type and identification using our method. It operates by leveraging side channel by electromagnetism rather than software-layer malware analysis. By capturing 96,000 measurement traces from an IoT system infected with different malware samples, we can obtain this information without altering the actual hardware. As a result, it can be implemented without any overhead, regardless of the resources available. Furthermore, our method has the advantage of non-trivial for malware writers to avoid. By collecting EM traces and later analyzing them for patterns, we were able to extract information from IoT devices. We were able to distinguish malware families based on side-channel knowledge without being able to see what exact hardware was involved. We were able to predict three generic malware forms (and one benign class) with a 99.89% percent accuracy in our tests. Even more, our results show that we are able to classify altered malware samples with unseen obfuscation techniques during the training phase, and to determine what kind of obfuscations were applied to the binary, which makes our approach particularly useful for malware analysts.
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