Visible to the public Biblio

Filters: Author is Li, M.  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
G
Guo, C., Chen, X., Jie, Y., Zhangjie, F., Li, M., Feng, B..  2017.  Dynamic Multi-phrase Ranked Search over Encrypted Data with Symmetric Searchable Encryption. IEEE Transactions on Services Computing. PP:1–1.

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.

H
Huo, T., Wang, W., Zhao, P., Li, Y., Wang, T., Li, M..  2020.  TEADS: A Defense-Aware Framework for Synthesizing Transient Execution Attacks. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :320—327.

Since 2018, a broad class of microarchitectural attacks called transient execution attacks (e.g., Spectre and Meltdown) have been disclosed. By abusing speculative execution mechanisms in modern CPUs, these attacks enable adversaries to leak secrets across security boundaries. A transient execution attack typically evolves through multiple stages, termed the attack chain. We find that current transient execution attacks usually rely on static attack chains, resulting in that any blockage in an attack chain may cause the failure of the entire attack. In this paper, we propose a novel defense-aware framework, called TEADS, for synthesizing transient execution attacks dynamically. The main idea of TEADS is that: each attacking stage in a transient execution attack chain can be implemented in several ways, and the implementations used in different attacking stages can be combined together under certain constraints. By constructing an attacking graph representing combination relationships between the implementations and testing available paths in the attacking graph dynamically, we can finally synthesize transient execution attacks which can bypass the imposed defense techniques. Our contributions include: (1) proposing an automated defense-aware framework for synthesizing transient execution attacks, even though possible combinations of defense strategies are enabled; (2) presenting an attacking graph extension algorithm to detect potential attack chains dynamically; (3) implementing TEADS and testing it on several modern CPUs with different protection settings. Experimental results show that TEADS can bypass the defenses equipped, improving the adaptability and durability of transient execution attacks.

J
Jiang, J., Yin, Q., Shi, Z., Li, M..  2018.  Comprehensive Behavior Profiling Model for Malware Classification. 2018 IEEE Symposium on Computers and Communications (ISCC). :00129-00135.

In view of the great threat posed by malware and the rapid growing trend about malware variants, it is necessary to determine the category of new samples accurately for further analysis and taking appropriate countermeasures. The network behavior based classification methods have become more popular now. However, the behavior profiling models they used usually only depict partial network behavior of samples or require specific traffic selection in advance, which may lead to adverse effects on categorizing advanced malware with complex activities. In this paper, to overcome the shortages of traditional models, we raise a comprehensive behavior model for profiling the behavior of malware network activities. And we also propose a corresponding malware classification method which can extract and compare the major behavior of samples. The experimental and comparison results not only demonstrate our method can categorize samples accurately in both criteria, but also prove the advantage of our profiling model to two other approaches in accuracy performance, especially under scenario based criteria.

Jin, H., Wang, T., Zhang, M., Li, M., Wang, Y., Snoussi, H..  2020.  Neural Style Transfer for Picture with Gradient Gram Matrix Description. 2020 39th Chinese Control Conference (CCC). :7026–7030.
Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.
L
Li, M., Chang, H., Xiang, Y., An, D..  2020.  A Novel Anti-Collusion Audio Fingerprinting Scheme Based on Fourier Coefficients Reversing. IEEE Signal Processing Letters. 27:1794—1798.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

Li, M., Zhang, Y., Sun, Y., Wang, W., Tsang, I. W., Lin, X..  2020.  I/O Efficient Approximate Nearest Neighbour Search based on Learned Functions. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :289–300.
Approximate nearest neighbour search (ANNS) in high dimensional space is a fundamental problem in many applications, such as multimedia database, computer vision and information retrieval. Among many solutions, data-sensitive hashing-based methods are effective to this problem, yet few of them are designed for external storage scenarios and hence do not optimized for I/O efficiency during the query processing. In this paper, we introduce a novel data-sensitive indexing and query processing framework for ANNS with an emphasis on optimizing the I/O efficiency, especially, the sequential I/Os. The proposed index consists of several lists of point IDs, ordered by values that are obtained by learned hashing (i.e., mapping) functions on each corresponding data point. The functions are learned from the data and approximately preserve the order in the high-dimensional space. We consider two instantiations of the functions (linear and non-linear), both learned from the data with novel objective functions. We also develop an I/O efficient ANNS framework based on the index. Comprehensive experiments on six benchmark datasets show that our proposed methods with learned index structure perform much better than the state-of-the-art external memory-based ANNS methods in terms of I/O efficiency and accuracy.
Li, M., Wang, F., Gupta, S..  2020.  Data-driven fault model development for superconducting logic. 2020 IEEE International Test Conference (ITC). :1—5.

Superconducting technology is being seriously explored for certain applications. We propose a new clean-slate method to derive fault models from large numbers of simulation results. For this technology, our method identifies completely new fault models – overflow, pulse-escape, and pattern-sensitive – in addition to the well-known stuck-at faults.

Li, W., Li, M., Ma, Y., Yang, Q..  2018.  PMU-extended Hardware ROP Attack Detection. 2018 12th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). :183–187.

Return Oriented Programming is one of the major challenges for software security nowadays. It can bypass Data Execution Prevention (DEP) mechanism by chaining short instruction sequences from existing code together to induce arbitrary code execution. Existing defenses are usually trade-offs between practicality, security, and performance. In this paper, we propose PMUe, a low-cost hardware ROP detection approach that detects ROP attack based on three inherent properties of ROP. It is transparent to user applications and can be regarded as a small extension to existing Performance Monitoring Unit in commodity processors. Our evaluation demonstrates that PMUe can effectively detect ROP attack with negligible performance overhead.

Li, W., Ma, Y., Yang, Q., Li, M..  2018.  Hardware-Based Adversary-Controlled States Tracking. 2018 IEEE 4th International Conference on Computer and Communications (ICCC). :1366–1370.

Return Oriented Programming is one of the most important software security challenges nowadays. It exploits memory vulnerabilities to control the state of the program and hijacks its control flow. Existing defenses usually focus on how to protect the control flow or face the challenge of how to maintain the taint markings for memory data. In this paper, we directly focus on the adversary-controlled states, simplify the classic dynamic taint analysis method to only track registers and propose Hardware-based Adversary-controlled States Tracking (HAST). HAST dynamically tracks registers that may be controlled by the adversary to detect ROP attack. It is transparent to user application and makes few modifications to existing hardware. Our evaluation demonstrates that HAST will introduce almost no performance overhead and can effectively detect ROP attacks without false positives on the tested common Linux applications.

Liu, Y., Yuan, X., Li, M., Zhang, W., Zhao, Q., Zhong, J., Cao, Y., Li, Y., Chen, L., Li, H. et al..  2018.  High Speed Device-Independent Quantum Random Number Generation without Detection Loophole. 2018 Conference on Lasers and Electro-Optics (CLEO). :1–2.

We report a an experimental study of device-independent quantum random number generation based on an detection-loophole free Bell test with entangled photons. After considering statistical fluctuations and applying an 80 Gb × 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than 10-5.

Lu, L., Yu, J., Chen, Y., Liu, H., Zhu, Y., Liu, Y., Li, M..  2018.  LipPass: Lip Reading-based User Authentication on Smartphones Leveraging Acoustic Signals. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1466–1474.

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.

S
Shu, F., Li, M., Chen, S., Wang, X., Li, F..  2017.  Research on Network Security Protection System Based on Dynamic Modeling. 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1602–1605.
A dynamic modeling method for network security vulnerabilities which is composed of the design of safety evaluation model, the design of risk model of intrusion event and the design of vulnerability risk model. The model based on identification of vulnerabilities values through dynamic forms can improve the tightness between vulnerability scanning system, intrusion prevention system and security configuration verification system. Based on this model, the network protection system which is most suitable for users can be formed, and the protection capability of the network protection system can be improved.
Shu, F., Li, M., Chen, S., Wang, X., Li, F..  2017.  Research on Network Security Protection System Based on Dynamic Modeling. 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1602–1605.
A dynamic modeling method for network security vulnerabilities which is composed of the design of safety evaluation model, the design of risk model of intrusion event and the design of vulnerability risk model. The model based on identification of vulnerabilities values through dynamic forms can improve the tightness between vulnerability scanning system, intrusion prevention system and security configuration verification system. Based on this model, the network protection system which is most suitable for users can be formed, and the protection capability of the network protection system can be improved.
Sun, M., Li, M., Gerdes, R..  2018.  Truth-Aware Optimal Decision-Making Framework with Driver Preferences for V2V Communications. 2018 IEEE Conference on Communications and Network Security (CNS). :1-9.

In Vehicle-to-Vehicle (V2V) communications, malicious actors may spread false information to undermine the safety and efficiency of the vehicular traffic stream. Thus, vehicles must determine how to respond to the contents of messages which maybe false even though they are authenticated in the sense that receivers can verify contents were not tampered with and originated from a verifiable transmitter. Existing solutions to find appropriate actions are inadequate since they separately address trust and decision, require the honest majority (more honest ones than malicious), and do not incorporate driver preferences in the decision-making process. In this work, we propose a novel trust-aware decision-making framework without requiring an honest majority. It securely determines the likelihood of reported road events despite the presence of false data, and consequently provides the optimal decision for the vehicles. The basic idea of our framework is to leverage the implied effect of the road event to verify the consistency between each vehicle's reported data and actual behavior, and determine the data trustworthiness and event belief by integrating the Bayes' rule and Dempster Shafer Theory. The resulting belief serves as inputs to a utility maximization framework focusing on both safety and efficiency. This framework considers the two basic necessities of the Intelligent Transportation System and also incorporates drivers' preferences to decide the optimal action. Simulation results show the robustness of our framework under the multiple-vehicle attack, and different balances between safety and efficiency can be achieved via selecting appropriate human preference factors based on the driver's risk-taking willingness.

Y
Yu, L., Chen, L., Dong, J., Li, M., Liu, L., Zhao, B., Zhang, C..  2020.  Detecting Malicious Web Requests Using an Enhanced TextCNN. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :768–777.
This paper proposes an approach that combines a deep learning-based method and a traditional machine learning-based method to efficiently detect malicious requests Web servers received. The first few layers of Convolutional Neural Network for Text Classification (TextCNN) are used to automatically extract powerful semantic features and in the meantime transferable statistical features are defined to boost the detection ability, specifically Web request parameter tampering. The semantic features from TextCNN and transferable statistical features from artificially-designing are grouped together to be fed into Support Vector Machine (SVM), replacing the last layer of TextCNN for classification. To facilitate the understanding of abstract features in form of numerical data in vectors extracted by TextCNN, this paper designs trace-back functions that map max-pooling outputs back to words in Web requests. After investigating the current available datasets for Web attack detection, HTTP Dataset CSIC 2010 is selected to test and verify the proposed approach. Compared with other deep learning models, the experimental results demonstrate that the approach proposed in this paper is competitive with the state-of-the-art.