Biblio

Having high safety and effective computational property, the elliptic curve cryptosystem is very suitable for embedded mobile environment with resource constraints. Power attack is a powerful cipher attack method, it uses leaking information of cipher-chip in its operation process to attack chip cryptographic algorithms. In view of the situation that the power attack on the elliptic curve cryptosystem mainly concentrates on scalar multiplication operation an improved algorithm FWNAF based on RWNAF is proposed. This algorithm utilizes the fragments window technology further improves the utilization ratio of the storage resource and reduces the “jitter phenomenon” in system computing performance caused by the sharp change in system resources.

In this paper, our aim is to detect illegal spectrum access behaviors. Firstly, we detect whether the channel is busy, and then if it is busy, recognizing whether there are illegal users. To get closer to the actual situation, we consider a more general scenario where multiple users are authorized to work on the same channel under certain interference control strategies, and build it as a ternary hypothesis test model using the generalized multi-hypothesis Neyman-Pearson criterion. Considering the various potential combination of multiple authorized users, the spectrum detection process utilizes a two-step detector. We adopt the Generalized Likelihood Ratio Test (GLRT) and the Rao test to detect illegal spectrum access behaviors. What is more, the Wald test is proposed which has a compromise between computational complexity and performance. The relevant formulas of the three detection schemes are derived. Finally, comprehensive and in-depth simulations are provided to verify the effectiveness of the proposed detection scheme that it has the best detection performance under different authorized sample numbers and different performance constraints. Besides, we illustrate the probability of detection of illegal behaviors under different parameters of illegal behaviors and different sets of AUs' states under the Wald test.

As recently studied, network-on-chip (NoC) suffers growing threats from hardware trojans (HTs), leading to performance degradation or information leakage when it provides communication service in many/multi-core systems. Therefore, defense techniques against NoC HTs experience rapid development in recent years. However, to the best of our knowledge, there are few standard benchmarks developed for the defense techniques evaluation. To address this issue, in this paper, we design a suite of benchmarks which involves multiple NoCs with different HTs, so that researchers can compare various HT defense methods fairly by making use of them. We first briefly introduce the features of target NoC and its infected modules in our benchmarks, and then, detail the design of our NoC HTs in a one-by-one manner. Finally, we evaluate our benchmarks through extensive simulations and report the circuit cost of NoC HTs in terms of area and power consumption, as well as their effects on NoC performance. Besides, comprehensive experiments, including functional testing and side channel analysis are performed to assess the stealthiness of our HTs.

Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods.

As the adaptive steganography selects edge and texture area for loading, the theoretical analysis is limited by modeling difficulty. This paper introduces a novel method to study pixel-value difference (PVD) embedding scheme. First, the difference histogram values of cover image are used as parameters, and a variance formula for PVD stego noise is obtained. The accuracy of this formula has been verified through analysis with standard pictures. Second, the stego noise is divided into six kinds of pixel regions, and the regional noise variances are utilized to compare the security between PVD and least significant bit matching (LSBM) steganography. A mathematical conclusion is presented that, with the embedding capacity less than 2.75 bits per pixel, PVD is always not safer than LSBM under the same embedding rate, regardless of region selection. Finally, 10000 image samples are used to observe the validity of mathematical conclusion. For most images and regions, the data are also shown to be consistent with the prior judgment. Meanwhile, the cases of exception are analyzed seriously, and are found to be caused by randomness of pixel selection and abandoned blocks in PVD scheme. In summary, the unity of theory and practice completely indicates the effectiveness of our new method.

The RFID technology has attracted considerable attention in recent years, and brings convenience to supply chain management. In this paper, we concentrate on designing path-checking protocols to check the valid paths in supply chains. By entering a valid path, the check reader can distinguish whether the tags have gone through the path or not. Based on modified schnorr signature scheme, we provide a path-checking method to achieve multi-signatures and final verification. In the end, we conduct security and privacy analysis to the scheme.

In previous multi-authority key-policy attribute-based Encryption (KP-ABE) schemes, either a super power central authority (CA) exists, or multiple attribute authorities (AAs) must collaborate in initializing the system. In addition, those schemes are proved security in the selective model. In this paper, we propose a new fully secure decentralized KP-ABE scheme, where no CA exists and there is no cooperation between any AAs. To become an AA, a participant needs to create and publish its public parameters. All the user's private keys will be linked with his unique global identifier (GID). The proposed scheme supports any monotonic access structure which can be expressed by a linear secret sharing scheme (LSSS). We prove the full security of our scheme in the standard model. Our scheme is also secure against at most F-1 AAs corruption, where F is the number of AAs in the system. The efficiency of our scheme is almost as well as that of the underlying fully secure single-authority KP-ABE system.