Qiang Zeng

Haotian Chi, Qiang Zeng, Xiaojiang Du et al.

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing out to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFirewall, a customizable data-flow control system to enhance the privacy of IoT platform users. PFirewall automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFirewall provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFirewall transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFirewall reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.

Fei Zuo, Qiang Zeng

By adding carefully crafted perturbations to input images, adversarial examples (AEs) can be generated to mislead neural-network-based image classifiers. $L_2$ adversarial perturbations by Carlini and Wagner (CW) are among the most effective but difficult-to-detect attacks. While many countermeasures against AEs have been proposed, detection of adaptive CW-$L_2$ AEs is still an open question. We find that, by randomly erasing some pixels in an $L_2$ AE and then restoring it with an inpainting technique, the AE, before and after the steps, tends to have different classification results, while a benign sample does not show this symptom. We thus propose a novel AE detection technique, Erase-and-Restore (E&R), that exploits the intriguing sensitivity of $L_2$ attacks. Experiments conducted on two popular image datasets, CIFAR-10 and ImageNet, show that the proposed technique is able to detect over 98% of $L_2$ AEs and has a very low false positive rate on benign images. The detection technique exhibits high transferability: a detection system trained using CW-$L_2$ AEs can accurately detect AEs generated using another $L_2$ attack method. More importantly, our approach demonstrates strong resilience to adaptive $L_2$ attacks, filling a critical gap in AE detection. Finally, we interpret the detection technique through both visualization and quantification.

Chenglong Fu, Qiang Zeng, Xiaojiang Du

The booming Internet of Things (IoT) market has drawn tremendous interest from cyber attackers. The centralized cloud-based IoT service architecture has serious limitations in terms of security, availability, and scalability, and is subject to single points of failure (SPOF). Recently, accommodating IoT services on blockchains has become a trend for better security, privacy, and reliability. However, blockchain's shortcomings of high cost, low throughput, and long latency make it unsuitable for IoT applications. In this paper, we take a retrospection of existing blockchain-based IoT solutions and propose a framework for efficient blockchain and IoT integration. Following the framework, we design a novel blockchain-assisted decentralized IoT remote accessing system, RS-IoT, which has the advantage of defending IoT devices against zero-day attacks without relying on any trusted third-party. By introducing incentives and penalties enforced by smart contracts, our work enables "an economic approach" to thwarting the majority of attackers who aim to achieve monetary gains. Our work presents an example of how blockchain can be used to ensure the fairness of service trading in a decentralized environment and punish misbehaviors objectively. We show the security of RS-IoT via detailed security analyses. Finally, we demonstrate its scalability, efficiency, and usability through a proof-of-concept implementation on the Ethereum testnet blockchain.

Haotian Chi, Qiang Zeng, Xiaojiang Du et al.

Emerging Internet of Thing (IoT) platforms provide a convenient solution for integrating heterogeneous IoT devices and deploying home automation applications. However, serious privacy threats arise as device data now flow out to the IoT platforms, which may be subject to various attacks. We observe two privacy-unfriendly practices in emerging home automation systems: first, the majority of data flowed to the platform are superfluous in the sense that they do not trigger any home automation; second, home owners currently have nearly zero control over their data. We present PFirewall, a customizable data-flow control system to enhance user privacy. PFirewall analyzes the automation apps to extract their semantics, which are automatically transformed into data-minimization policies; these policies only send minimized data flows to the platform for app execution, such that the ability of attackers to infer user privacy is significantly impaired. In addition, PFirewall provides capabilities and interfaces for users to define and enforce customizable policies based on individual privacy preferences. PFirewall adopts an elegant man-in-the-middle design, transparently executing data minimization and user-defined policies to process raw data flows and mediating the processed data between IoT devices and the platform (via the hub), without requiring modifications of the platform or IoT devices. We implement PFirewall to work with two popular platforms: SmartThings and openHAB, and set up two real-world testbeds to evaluate its performance. The evaluation results show that PFirewall is very effective: it reduces IoT data sent to the platform by 97% and enforces user defined policies successfully.

Rixin Xu, Qiang Zeng, Liehuang Zhu et al.

The combination of smart home platforms and automation apps introduces much convenience to smart home users. However, this also brings the potential for privacy leakage. If a smart home platform is permitted to collect all the events of a user day and night, then the platform will learn the behavior patterns of this user before long. In this paper, we investigate how IFTTT, one of the most popular smart home platforms, has the capability of monitoring the daily life of a user in a variety of ways that are hardly noticeable. Moreover, we propose multiple ideas for mitigating privacy leakages, which altogether forms a Filter-and-Fuzz (F&F) process: first, it filters out events unneeded by the IFTTT platform; then, it fuzzes the values and frequencies of the remaining events. We evaluate the F&F process, and the results show that the proposed solution makes IFTTT unable to recognize any of the user's behavior patterns.

Qiang Zeng, Jianhai Su, Chenglong Fu et al.

Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become a severe threat to the trustworthiness of machine learning. While AEs in the image domain have been well studied, audio AEs are less investigated. Recently, multiple techniques are proposed to generate audio AEs, which makes countermeasures against them an urgent task. Our experiments show that, given an AE, the transcription results by different Automatic Speech Recognition (ASR) systems differ significantly, as they use different architectures, parameters, and training datasets. Inspired by Multiversion Programming, we propose a novel audio AE detection approach, which utilizes multiple off-the-shelf ASR systems to determine whether an audio input is an AE. The evaluation shows that the detection achieves accuracies over 98.6%.

Kimberly Redmond, Lannan Luo, Qiang Zeng

Given a closed-source program, such as most of proprietary software and viruses, binary code analysis is indispensable for many tasks, such as code plagiarism detection and malware analysis. Today, source code is very often compiled for various architectures, making cross-architecture binary code analysis increasingly important. A binary, after being disassembled, is expressed in an assembly languages. Thus, recent work starts exploring Natural Language Processing (NLP) inspired binary code analysis. In NLP, words are usually represented in high-dimensional vectors (i.e., embeddings) to facilitate further processing, which is one of the most common and critical steps in many NLP tasks. We regard instructions as words in NLP-inspired binary code analysis, and aim to represent instructions as embeddings as well. To facilitate cross-architecture binary code analysis, our goal is that similar instructions, regardless of their architectures, have embeddings close to each other. To this end, we propose a joint learning approach to generating instruction embeddings that capture not only the semantics of instructions within an architecture, but also their semantic relationships across architectures. To the best of our knowledge, this is the first work on building cross-architecture instruction embedding model. As a showcase, we apply the model to resolving one of the most fundamental problems for binary code similarity comparison---semantics-based basic block comparison, and the solution outperforms the code statistics based approach. It demonstrates that it is promising to apply the model to other cross-architecture binary code analysis tasks.

Fei Zuo, Bokai Yang, Xiaopeng Li et al.

Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. In particular, L0 AEs are a category of widely discussed threats where adversaries are restricted in the number of pixels that they can corrupt. However, our observation is that, while L0 attacks modify as few pixels as possible, they tend to cause large-amplitude perturbations to the modified pixels. We consider this as an inherent limitation of L0 AEs, and thwart such attacks by both detecting and rectifying them. The main novelty of the proposed detector is that we convert the AE detection problem into a comparison problem by exploiting the inherent limitation of L0 attacks. More concretely, given an image I, it is pre-processed to obtain another image I' . A Siamese network, which is known to be effective in comparison, takes I and I' as the input pair to determine whether I is an AE. A trained Siamese network automatically and precisely captures the discrepancies between I and I' to detect L0 perturbations. In addition, we show that the pre-processing technique, inpainting, used for detection can also work as an effective defense, which has a high probability of removing the adversarial influence of L0 perturbations. Thus, our system, called AEPECKER, demonstrates not only high AE detection accuracies, but also a notable capability to correct the classification results.

Qiang Zeng, Golam Kayas, Emil Mohammed et al.

Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure, requiring intensive manual efforts. Worse, fresh patches tend to harm system dependability, hence deterring users from deploying them. We propose a heap patching system that simultaneously has the following prominent advantages: (1) generating patches without manual efforts; (2) installing patches without altering the code (so called code-less patching); (3) handling various heap vulnerability types; (4) imposing a very low overhead; and (5) no dependency on specific heap allocators. As a separate contribution, we propose targeted calling context encoding, which is a suite of algorithms for optimizing calling context encoding, an important technique with applications in many areas. The system properly combines heavyweight offline attack analysis with lightweight online defense generation, and provides a new countermeasure against heap attacks. The evaluation shows that the system is effective and efficient.

Haotian Chi, Qiang Zeng, Xiaojiang Du et al.

A number of Internet of Things (IoTs) platforms have emerged to enable various IoT apps developed by third-party developers to automate smart homes. Prior research mostly concerns the overprivilege problem in the permission model. Our work, however, reveals that even IoT apps that follow the principle of least privilege, when they interplay, can cause unique types of threats, named Cross-App Interference (CAI) threats. We describe and categorize the new threats, showing that unexpected automation, security and privacy issues may be caused by such threats, which cannot be handled by existing IoT security mechanisms. To address this problem, we present HOMEGUARD, a system for appified IoT platforms to detect and cope with CAI threats. A symbolic executor module is built to precisely extract the automation semantics from IoT apps. The semantics of different IoT apps are then considered collectively to evaluate their interplay and discover CAI threats systematically. A user interface is presented to users during IoT app installation, interpreting the discovered threats to help them make decisions. We evaluate HOMEGUARD via a proof-of-concept implementation on Samsung SmartThings and discover many threat instances among apps in the SmartThings public repository. The evaluation shows that it is precise, effective and efficient.

Chenglong Fu, Xiaojiang Du, Longfei Wu et al.

Implantable medical devices (IMDs), such as pacemakers, implanted cardiac defibrillators, and neurostimulators are medical devices implanted into patients' bodies for monitoring physiological signals and performing medical treatments. Many IMDs have built-in wireless communication modules to facilitate data collecting and device reprogramming by external programmers. The wireless communication brings significant conveniences for advanced applications such as real-time and remote monitoring but also introduces the risk of unauthorized wireless access. The absence of effective access control mechanisms exposes patients' life to cyber attacks. In this paper, we present a lightweight and universally applicable access control system for IMDs. By leveraging Physically Obfuscated Keys (POKs) as the hardware root of trust, provable security is achieved based on standard cryptographic primitives while attaining high energy efficiency. In addition, barrier-free IMD access under emergent situations is realized by utilizing the patient's biometrical information. We evaluate our proposed scheme through extensive security analysis and a prototype implementation, which demonstrates our work's superiority on security and energy efficiency.

Lannan Luo, Qiang Zeng, Chen Cao et al.

Android Framework is a layer of software that exists in every Android system managing resources of all Android apps. A vulnerability in Android Framework can lead to severe hacks, such as destroying user data and leaking private information. With tens of millions of Android devices unpatched due to Android fragmentation, vulnerabilities in Android Framework certainly attract attackers to exploit them. So far, enormous manual effort is needed to craft such exploits. To our knowledge, no research has been done on automatic generation of exploits that take advantage of Android Framework vulnerabilities. We make a first step towards this goal by applying symbolic execution of Android Framework to finding bugs and generating exploits. Several challenges have been raised by the task. (1) The information of an app flows to Android Framework in multiple intricate steps, making it difficult to identify symbolic inputs. (2) Android Framework has a complex initialization phase, which exacerbates the state space explosion problem. (3) A straightforward design that builds the symbolic executor as a layer inside the Android system will not work well: not only does the implementation have to ensure the compatibility with the Android system, but it needs to be maintained whenever Android gets updated. We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework. It fundamentally changes the state of the art in exploit generation on the Android system, and has been applied to constructing new techniques for finding vulnerabilities.