Abstract. KeeLoq remote keyless entry systems are widely used for access control purposes such as garage openers or car door systems. We present the first successful differential power analysis attacks on numerous commercially available products employing KeeLoq code hopping. Our new techniques combine side-channel cryptanalysis with specific properties of the KeeLoq algorithm. They allow for efficiently revealing both the secret key of a remote transmitter and the manufacturer key stored in a receiver. As a result, a remote control can be cloned from only ten power traces, allowing for a practical key recovery in few minutes. After extracting the manufacturer key once, with similar techniques, we demonstrate how to recover the secret key of a remote control and replicate it from a distance, just by eavesdropping on at most two messages. This key-cloning without physical access to the device has serious realworld security implications, as the technically challenging part can be outsourced to specialists. Finally, we mount a denial of service attack on a KeeLoq access control system. All proposed attacks have been verified on several commercial KeeLoq products. MotivationThe KeeLoq block cipher is widely used for security relevant applications, e.g., remote keyless entry (RKE) systems for car or building access, and passive radio frequency identification (RFID) transponders for car immobilizers [13]. In the course of the last year, the KeeLoq algorithm has moved into the focus of the international cryptographic research community. Shortly after the first cryptanalysis of the cipher [1], more analytical attacks were proposed [4,5], revealing Amir Moradi performed most of the work described in this contribution as a visiting researcher at Ruhr University Bochum.

In this paper we describe two differential fault attack techniques against Advanced Encryption Standard (AES). We propose two models for fault occurrence; we could find all 128 bits of key using one of them and only 6 faulty ciphertexts. We need approximately 1500 faulty ciphertexts to discover the key with the other fault model. Union of these models covers all faults that can occur in the 9th round of encryption algorithm of AES-128 cryptosystem. One of main advantage of proposed fault models is that any fault in the AES encryption from start (AddRoundKey with the main key before the first round) to MixColumns function of 9th round can be modeled with one of our fault models. These models cover all states, so generated differences caused by diverse plaintexts or ciphertexts can be supposed as faults and modeled with our models. It establishes a novel technique to cryptanalysis AES without side channel information. The major difference between these methods and previous ones is on the assumption of fault models. Our proposed fault models use very common and general assumption for locations and values of occurred faults.

No abstract

Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems, a hybrid face recognition system is proposed which benefits from the superiority of both convolutional neural network (CNN) and support vector machine (SVM). To this end, first a genetic algorithm is employed to find the optimum structure of CNN. Then, the performance of the system is improved by replacing the last layer of CNN with an ensemble of SVMs. Finally, using concepts of error correction, decision is made. The potential of CNN as a trainable feature extractor provides a flexible recognition system that can recognise faces with variations in pose and illumination. Simulation results show that the system achieves good recognition rate and is robust against variations in terms of facial expressions, occlusion, noise, and illuminations.

No abstract

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our simulation results on both synthetic and real-world datasets show that our strategies achieve a desired accuracy in inferring the underlying structure, while spending a small budget on communication. Index Terms-Structure learning, Chow-Liu algorithm, Gaussian Graphical Model.!

To evaluate the performance of the distributed medium access control layer of the emerging ultrawideband and 60-GHz millimeter wave (mmWave) wireless personal area networks based on ECMA-368 and ECMA-387 standards, the first step is to determine the beacon period length (BPL) of the superframe in a given network. In this paper, we provide an analytical model for the probability mass function (PMF) of the BPL as a function of the network dimensions, number of beaconing devices, antenna beamwidth, and the transmission range of the devices. To enable devices with steerable directional antennas in the ECMA-387 standard to have simultaneous communications with neighbors in their different antenna sectors, we propose an improvement to the standard for which we computed the PMF of the BPL in its worst case. The effect of beacon period (BP) contraction on the PMF is also considered and modeled. The proposed model for all cases is evaluated by simulating different scenarios in the network and the results show that on average, the model for the average BPL has an error of 1.2 and 2.5 percent in the current definition of the standard and in the proposed modification, respectively, without BP contraction and 0.9 and 1.5 percent, respectively, with BP contraction.

No abstract