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Zhang, J., Ji, X., Xu, W., Chen, Y.-C., Tang, Y., Qu, G..  2020.  MagView: A Distributed Magnetic Covert Channel via Video Encoding and Decoding. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :357—366.

Air-gapped networks achieve security by using the physical isolation to keep the computers and network from the Internet. However, magnetic covert channels based on CPU utilization have been proposed to help secret data to escape the Faraday-cage and the air-gap. Despite the success of such cover channels, they suffer from the high risk of being detected by the transmitter computer and the challenge of installing malware into such a computer. In this paper, we propose MagView, a distributed magnetic cover channel, where sensitive information is embedded in other data such as video and can be transmitted over the air-gapped internal network. When any computer uses the data such as playing the video, the sensitive information will leak through the magnetic covert channel. The "separation" of information embedding and leaking, combined with the fact that the covert channel can be created on any computer, overcomes these limitations. We demonstrate that CPU utilization for video decoding can be effectively controlled by changing the video frame type and reducing the quantization parameter without video quality degradation. We prototype MagView and achieve up to 8.9 bps throughput with BER as low as 0.0057. Experiments under different environment are conducted to show the robustness of MagView. Limitations and possible countermeasures are also discussed.

Cao, Y., Tang, Y..  2019.  Development of Real-Time Style Transfer for Video System. 2019 3rd International Conference on Circuits, System and Simulation (ICCSS). :183—187.

Re-drawing the image as a certain artistic style is considered to be a complicated task for computer machine. On the contrary, human can easily master the method to compose and describe the style between different images. In the past, many researchers studying on the deep neural networks had found an appropriate representation of the artistic style using perceptual loss and style reconstruction loss. In the previous works, Gatys et al. proposed an artificial system based on convolutional neural networks that creates artistic images of high perceptual quality. Whereas in terms of running speed, it was relatively time-consuming, thus it cannot apply to video style transfer. Recently, a feed-forward CNN approach has shown the potential of fast style transformation, which is an end-to-end system without hundreds of iteration while transferring. We combined the benefits of both approaches, optimized the feed-forward network and defined time loss function to make it possible to implement the style transfer on video in real time. In contrast to the past method, our method runs in real time with higher resolution while creating competitive visually pleasing and temporally consistent experimental results.

Zhang, C., Xu, C., Xu, J., Tang, Y., Choi, B..  2019.  GEMˆ2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain. 2019 IEEE 35th International Conference on Data Engineering (ICDE). :842–853.
Blockchain technology has attracted much attention due to the great success of the cryptocurrencies. Owing to its immutability property and consensus protocol, blockchain offers a new solution for trusted storage and computation services. To scale up the services, prior research has suggested a hybrid storage architecture, where only small meta-data are stored onchain and the raw data are outsourced to off-chain storage. To protect data integrity, a cryptographic proof can be constructed online for queries over the data stored in the system. However, the previous schemes only support simple key-value queries. In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. The key challenge lies in how to design an authenticated data structure (ADS) that can be efficiently maintained by the blockchain, in which a unique gas cost model is employed. By analyzing the performance of the existing techniques, we propose a novel ADS, called GEM2-tree, which is not only gas-efficient but also effective in supporting authenticated queries. To further reduce the ADS maintenance cost without sacrificing much the query performance, we also propose an optimized structure, GEM2*-tree, by designing a two-level index structure. Theoretical analysis and empirical evaluation validate the performance of the proposed ADSs.
Khatchadourian, R., Tang, Y., Bagherzadeh, M., Ahmed, S..  2019.  Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). :619-630.

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ?642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

Liu, D., Li, Y., Tang, Y., Wang, B., Xie, W..  2018.  VMPBL: Identifying Vulnerable Functions Based on Machine Learning Combining Patched Information and Binary Comparison Technique by LCS. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :800-807.

Nowadays, most vendors apply the same open source code to their products, which is dangerous. In addition, when manufacturers release patches, they generally hide the exact location of the vulnerabilities. So, identifying vulnerabilities in binaries is crucial. However, just searching source program has a lower identifying accuracy of vulnerability, which requires operators further to differentiate searched results. Under this context, we propose VMPBL to enhance identifying the accuracy of vulnerability with the help of patch files. VMPBL, compared with other proposed schemes, uses patched functions according to its vulnerable functions in patch file to further distinguish results. We establish a prototype of VMPBL, which can effectively identify vulnerable function types and get rid of safe functions from results. Firstly, we get the potential vulnerable-patched functions by binary comparison technique based on K-Trace algorithm. Then we combine the functions with vulnerability and patch knowledge database to classify these function pairs and identify the possible vulnerable functions and the vulnerability types. Finally, we test some programs containing real-world CWE vulnerabilities, and one of the experimental results about CWE415 shows that the results returned from only searching source program are about twice as much as the results from VMPBL. We can see that using VMPBL can significantly reduce the false positive rate of discovering vulnerabilities compared with analyzing source files alone.

Lu, X., Wan, X., Xiao, L., Tang, Y., Zhuang, W..  2018.  Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals. 2018 IEEE International Conference on Communications (ICC). :1-6.
Edge computing for mobile devices in vehicular ad hoc networks (VANETs) has to address rogue edge attacks, in which a rogue edge node claims to be the serving edge in the vehicle to steal user secrets and help launch other attacks such as man-in-the-middle attacks. Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units (OBUs) and the large-scale network infrastructure with roadside units (RSUs). In this paper, we propose a physical (PHY)- layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control (MAC) address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. We adopt a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model. Simulation results show that the Q-learning based detection scheme can significantly reduce the detection error rate and increase the utility compared with existing schemes.
Xu, H., Hu, L., Liu, P., Xiao, Y., Wang, W., Dayal, J., Wang, Q., Tang, Y..  2018.  Oases: An Online Scalable Spam Detection System for Social Networks. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :98–105.
Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.
Zhang, F., Dong, X., Zhao, X., Wang, Y., Qureshi, S., Zhang, Y., Lou, X., Tang, Y..  2018.  Theoretical Round Modification Fault Analysis on AEGIS-128 with Algebraic Techniques. 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). :335-343.
This paper proposed an advanced round modification fault analysis (RMFA) at the theoretical level on AEGIS-128, which is one of seven finalists in CAESAR competition. First, we clarify our assumptions and simplifications on the attack model, focusing on the encryption security. Then, we emphasize the difficulty of applying vanilla RMFA to AEGIS-128 in the practical case. Finally we demonstrate our advanced fault analysis on AEGIS-128 using machine-solver based algebraic techniques. Our enhancement can be used to conquer the practical scenario which is difficult for vanilla RMFA. Simulation results show that when the fault is injected to the initialization phase and the number of rounds is reduced to one, two samples of injections can extract the whole 128 key bits within less than two hours. This work can also be extended to other versions such as AEGIS-256.
Shen, G., Tang, Y., Li, S., Chen, J., Yang, B..  2017.  A General Framework of Hardware Trojan Detection: Two-Level Temperature Difference Based Thermal Map Analysis. 2017 11th IEEE International Conference on Anti-Counterfeiting, Security, and Identification (ASID). :172–178.

With the globalization of integrated circuit design and manufacturing, Hardware Trojan have posed serious threats to the security of commercial chips. In this paper, we propose the framework of two-level temperature difference based thermal map analysis detection method. In our proposed method, thermal maps of an operating chip during a period are captured, and they are differentiated with the thermal maps of a golden model. Then every pixel's differential temperature of differential thermal maps is extracted and compared with other pixel's. To mitigate the Gaussian white noise and to differentiate the information of Hardware Trojan from the information of normal circuits, Kalman filter algorithm is involved. In our experiment, FPGAs configured with equivalent circuits are utilized to simulate the real chips to validate our proposed approach. The experimental result reveals that our proposed framework can detect Hardware Trojan whose power proportion magnitude is 10''3.

Fan, Z., Wu, H., Xu, J., Tang, Y..  2017.  An Optimization Algorithm for Spatial Information Network Self-Healing Based on Software Defined Network. 2017 12th International Conference on Computer Science and Education (ICCSE). :369–374.

Spatial information network is an important part of the integrated space-terrestrial information network, its bearer services are becoming increasingly complex, and real-time requirements are also rising. Due to the structural vulnerability of the spatial information network and the dynamics of the network, this poses a serious challenge to how to ensure reliable and stable data transmission. The structural vulnerability of the spatial information network and the dynamics of the network brings a serious challenge of ensuring reliable and stable data transmission. Software Defined Networking (SDN), as a new network architecture, not only can quickly adapt to new business, but also make network reconfiguration more intelligent. In this paper, SDN is used to design the spatial information network architecture. An optimization algorithm for network self-healing based on SDN is proposed to solve the failure of switching node. With the guarantee of Quality of Service (QoS) requirement, the link is updated with the least link to realize the fast network reconfiguration and recovery. The simulation results show that the algorithm proposed in this paper can effectively reduce the delay caused by fault recovery.