Visible to the public Biblio

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2020-11-02
Qin, Maoyuan, Hu, Wei, Mu, Dejun, Tai, Yu.  2018.  Property Based Formal Security Verification for Hardware Trojan Detection. 2018 IEEE 3rd International Verification and Security Workshop (IVSW). :62—67.

The design of modern computer hardware heavily relies on third-party intellectual property (IP) cores, which may contain malicious hardware Trojans that could be exploited by an adversary to leak secret information or take control of the system. Existing hardware Trojan detection methods either require a golden reference design for comparison or extensive functional testing to identify suspicious signals. In this paper, we propose a new formal verification method to verify the security of hardware designs. The proposed solution formalizes fine grained gate level information flow model for proving security properties of hardware designs in the Coq theorem prover environment. Compare with existing register transfer level (RTL) information flow security models, our model only needs to translate a small number of logic primitives to their formal representations without the need of supporting the rich RTL HDL semantics or dealing with complex conditional branch or loop structures. As a result, a gate level information flow model can be created at much lower complexity while achieving significantly higher precision in modeling the security behavior of hardware designs. We use the AES-T1700 benchmark from Trust-HUB to demonstrate the effectiveness of our solution. Experimental results show that our method can detect and pinpoint the Trojan.

2020-03-30
Huang, Jinjing, Cheng, Shaoyin, Lou, Songhao, Jiang, Fan.  2019.  Image steganography using texture features and GANs. 2019 International Joint Conference on Neural Networks (IJCNN). :1–8.
As steganography is the main practice of hidden writing, many deep neural networks are proposed to conceal secret information into images, whose invisibility and security are unsatisfactory. In this paper, we present an encoder-decoder framework with an adversarial discriminator to conceal messages or images into natural images. The message is embedded into QR code first which significantly improves the fault-tolerance. Considering the mean squared error (MSE) is not conducive to perfectly learn the invisible perturbations of cover images, we introduce a texture-based loss that is helpful to hide information into the complex texture regions of an image, improving the invisibility of hidden information. In addition, we design a truncated layer to cope with stego image distortions caused by data type conversion and a moment layer to train our model with varisized images. Finally, our experiments demonstrate that the proposed model improves the security and visual quality of stego images.
2020-03-09
Lv, Jixian, Wang, Yi, Liu, Jinze.  2019.  A Security Problem in Cloud Auditing Protocols. 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). :43–46.
In 2013, subversion attack comes to publity again by Mikhail Bellare, who was inspired by PRISM. In this work, we implement this kind of attack on cloud auditing protocols. We show that through subversion attacks, the cloud server can recover the secret information stored by the data owner. Especially, First, we set a general frame of data auditing protocols. This model forms a basic security model of auditing protocols. Then we give a security model of attacker. Finally, we put forward some popular auditing protocols which can be subverted.
2020-03-02
Amrutiya, Varun, Jhamb, Siddhant, Priyadarshi, Pranjal, Bhatia, Ashutosh.  2019.  Trustless Two-Factor Authentication Using Smart Contracts in Blockchains. 2019 International Conference on Information Networking (ICOIN). :66–71.
Two-factor authentication (2FA) is widely prevalent in banking, emails and virtual private networks (VPN) connections or in accessing any secure web service. In 2FA, to get authenticated the users are expected to provide additional secret information along with the password. Typically, this secret information (tokens) is generated by a centralized trusted third party upon receiving an authentication request from users. Thus, this additional layer of security comes at the cost of inherently trusting the third party for their services. The security of such authentication systems is always under the threat of the trusted party is being compromised. In this paper, we propose a novel approach to make server authentication even more secure by building 2FA over the blockchain platform which is distributed in nature. The proposed solution does not require any trusted third party between claimant (user) and the verifier (server) for the authentication purpose. To demonstrate the idea of using blockchain technology for 2FA, we have added an extra layer of security component to the OpenSSH server a widely used application for Secure Shell (SSH) protocol.
Jiang, Qi, Zhang, Xin, Zhang, Ning, Tian, Youliang, Ma, Xindi, Ma, Jianfeng.  2019.  Two-Factor Authentication Protocol Using Physical Unclonable Function for IoV. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :195–200.
As an extension of Internet of Things (IoT) in transportation sector, the Internet of Vehicles (IoV) can greatly facilitate vehicle management and route planning. With ever-increasing penetration of IoV, the security and privacy of driving data should be guaranteed. Moreover, since vehicles are often left unattended with minimum human interventions, the onboard sensors are vulnerable to physical attacks. Therefore, the physically secure authentication and key agreement (AKA) protocol is urgently needed for IoV to implement access control and information protection. In this paper, physical unclonable function (PUF) is introduced in the AKA protocol to ensure that the system is secure even if the user devices or sensors are compromised. Specifically, PUF, as a hardware fingerprint generator, eliminates the storage of any secret information in user devices or vehicle sensors. By combining password with PUF, the user device cannot be used by someone else to be successfully authenticated as the user. By resorting to public key cryptography, the proposed protocol can provide anonymity and desynchronization resilience. Finally, the elaborate security analysis demonstrates that the proposed protocol is free from the influence of known attacks and can achieve expected security properties, and the performance evaluation indicates the efficiency of our protocol.
2019-02-22
Hu, D., Wang, L., Jiang, W., Zheng, S., Li, B..  2018.  A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks. IEEE Access. 6:38303-38314.

The security of image steganography is an important basis for evaluating steganography algorithms. Steganography has recently made great progress in the long-term confrontation with steganalysis. To improve the security of image steganography, steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithms. The concept of steganography without embedding (SWE), which does not need to modify the data of the carrier image, appeared to overcome the detection of machine-learning-based steganalysis algorithms. In this paper, we propose a novel image SWE method based on deep convolutional generative adversarial networks. We map the secret information into a noise vector and use the trained generator neural network model to generate the carrier image based on the noise vector. No modification or embedding operations are required during the process of image generation, and the information contained in the image can be extracted successfully by another neural network, called the extractor, after training. The experimental results show that this method has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.

2014-09-26
Kashyap, V., Wiedermann, B., Hardekopf, B..  2011.  Timing- and Termination-Sensitive Secure Information Flow: Exploring a New Approach. Security and Privacy (SP), 2011 IEEE Symposium on. :413-428.

Secure information flow guarantees the secrecy and integrity of data, preventing an attacker from learning secret information (secrecy) or injecting untrusted information (integrity). Covert channels can be used to subvert these security guarantees, for example, timing and termination channels can, either intentionally or inadvertently, violate these guarantees by modifying the timing or termination behavior of a program based on secret or untrusted data. Attacks using these covert channels have been published and are known to work in practiceâ as techniques to prevent non-covert channels are becoming increasingly practical, covert channels are likely to become even more attractive for attackers to exploit. The goal of this paper is to understand the subtleties of timing and termination-sensitive noninterference, explore the space of possible strategies for enforcing noninterference guarantees, and formalize the exact guarantees that these strategies can enforce. As a result of this effort we create a novel strategy that provides stronger security guarantees than existing work, and we clarify claims in existing work about what guarantees can be made.