Muhammad Taqi Raza

Md Ruman Islam, Raja Hasnain Anwar, Spyridon Mastorakis et al.

Modern applications are end-to-end encrypted to prevent data from being read or secretly modified. 5G tech nology provides ubiquitous access to these applications without compromising the application-specific performance and latency goals. In this paper, we empirically demonstrate that 5G radio communication becomes the side channel to precisely infer the user's applications in real-time. The key idea lies in observing the 5G physical and MAC layer interactions over time that reveal the application's behavior. The MAC layer receives the data from the application and requests the network to assign the radio resource blocks. The network assigns the radio resources as per application requirements, such as priority, Quality of Service (QoS) needs, amount of data to be transmitted, and buffer size. The adversary can passively observe the radio resources to fingerprint the applications. We empirically demonstrate this attack by considering four different categories of applications: online shopping, voice/video conferencing, video streaming, and Over-The-Top (OTT) media platforms. Finally, we have also demonstrated that an attacker can differentiate various types of applications in real-time within each category.

Mohammadreza Ebrahimi, Ning Zhang, James Hu et al.

Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day) attacks. Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis. However, these detectors are susceptible to malware variants with slight perturbations, known as adversarial examples. Generating effective adversarial examples is useful to reveal the vulnerabilities of such systems. Current methods for launching such attacks require accessing either the specifications of the targeted anti-malware model, the confidence score of the anti-malware response, or dynamic malware analysis, which are either unrealistic or expensive. We propose MalRNN, a novel deep learning-based approach to automatically generate evasive malware variants without any of these restrictions. Our approach features an adversarial example generation process, which learns a language model via a generative sequence-to-sequence recurrent neural network to augment malware binaries. MalRNN effectively evades three recent deep learning-based malware detectors and outperforms current benchmark methods. Findings from applying our MalRNN on a real dataset with eight malware categories are discussed.

Guan-Hua Tu, Yuanjie Li, Chunyi Peng et al.

Mobile Internet is becoming the norm. With more personalized mobile devices in hand, many services choose to offer alternative, usually more convenient, approaches to authenticating and delivering the content between mobile users and service providers. One main option is to use SMS (i.e., short messaging service). Such carrier-grade text service has been widely used to assist versatile mobile services, including social networking, banking, to name a few. Though the text service can be spoofed via certain Internet text service providers which cooperated with carriers, such attacks haven well studied and defended by industry due to the efforts of research community. However, as cellular network technology advances to the latest IP-based 4G LTE, we find that these mobile services are somehow exposed to new threats raised by this change, particularly on 4G LTE Text service (via brand-new distributed Mobile-Initiated Spoofed SMS attack which is not available in legacy 2G/3G systems). The reason is that messaging service over LTE shifts from the circuit-switched (CS) design to the packet-switched (PS) paradigm as 4G LTE supports PS only. Due to this change, 4G LTE Text Service becomes open to access. However, its shields to messaging integrity and user authentication are not in place. As a consequence, such weaknesses can be exploited to launch attacks (e.g., hijack Facebook accounts) against a targeted individual, a large scale of mobile users and even service providers, from mobile devices. Current defenses for Internet-Initiated Spoofed SMS attacks cannot defend the unprecedented attack. Our study shows that 53 of 64 mobile services over 27 industries are vulnerable to at least one threat. We validate these proof-of-concept attacks in one major US carrier which supports more than 100 million users. We finally propose quick fixes and discuss security insights and lessons we have learnt.