Binary code similarity analysis (BCSA) is widely used for diverse security applications such as plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.

One approach to assess the security of embedded IoT devices is applying dynamic analysis such as fuzz testing to their firmware in scale. To this end, existing approaches aim to provide an emulation environment that mimics the behavior of real hardware/peripherals. Nonetheless, in practice, such approaches can emulate only a small fraction of firmware images. For example, Firmadyne, a state-of-the-art tool, can only run 183 (16.28%) of 1,124 wireless router/IP-camera images that we collected from the top eight manufacturers. Such a low emulation success rate is caused by discrepancy in the real and emulated firmware execution environment.

A cellular network is a closed system, and each network operator has built a unique “walled garden” for their network by combining different operation policies, network configurations, and implementation optimizations. Unfortunately, some of these combinations can induce performance degradation due to misconfiguration or unnecessary procedures. To detect such degradation, a thorough understanding of even the minor details of the standards and operator-specific implementations is important. However, it is difficult to detect such problems, as the control plane is complicated by numerous procedures. This paper introduces a simple yet powerful method that diagnoses these problems by exploiting the operator-specific implementations of cellular networks. We develop a signaling collection and analysis tool that collects control plane messages from operators and finds problems through comparative analysis. The analysis process consists of three different control plane comparison procedures that can find such problems effectively. These individual procedures use a time threshold, control flow sequence, and signaling failure as the basis for comparison. To this end, we collect approximately 3.1 million control-plane messages from 13 major cellular operators worldwide. As a case study, we analyze the circuit-switched fallback technology that triggers generation crossover between third generation and long-term evolution technologies.

Recently, cellular operators have started migrating to IPv6 in response to the increasing demand for IP addresses. With the introduction of IPv6, cellular middleboxes, such as firewalls for preventing malicious traffic from the Internet and stateful NAT64 boxes for providing backward compatibility with legacy IPv4 services, have become crucial to maintain stability of cellular networks. This paper presents security problems of the currently deployed IPv6 middleboxes of five major operators. To this end, we first investigate several key features of the current IPv6 deployment that can harm the safety of a cellular network as well as its customers. These features combined with the currently deployed IPv6 middlebox allow an adversary to launch six different attacks. First, firewalls in IPv6 cellular networks fail to block incoming packets properly. Thus, an adversary could fingerprint cellular devices with scanning, and further, she could launch denial-of-service or over-billing attacks. Second, vulnerabilities in the stateful NAT64 box, a middlebox that maps an IPv6 address to an IPv4 address (and vice versa), allow an adversary to launch three different attacks: 1) NAT overflow attack that allows an adversary to overflow the NAT resources, 2) NAT wiping attack that removes active NAT mappings by exploiting the lack of TCP sequence number verification of firewalls, and 3) NAT bricking attack that targets services adopting IP-based blacklisting by preventing the shared external IPv4 address from accessing the service. We confirmed the feasibility of these attacks with an empirical analysis. We also propose effective countermeasures for each attack.

Long Term Evolution (LTE) is becoming the dominant cellular networking technology, shifting the cellular network away from its circuit-switched legacy towards a packet-switched network that resembles the Internet. To support voice calls over the LTE network, operators have introduced Voice-over-LTE (VoLTE), which dramatically changes how voice calls are handled, both from user equipment and infrastructure perspectives. We find that this dramatic shift opens up a number of new attack surfaces that have not been previously explored. To call attention to this matter, this paper presents a systematic security analysis.