Random Digit Representation of Integers

Modular exponentiation is core to today\u27s main stream public key cryptographic systems. In this article, we generalize the classical fractional $w$NAF method for modular exponentiation -- the classical method uses a digit set of the form $\{1,3,\dots,m\}$ which is extended here to any set of odd integers of the form $\{1,d_2,\dots, d_n\}$. We give a formula for the average density of non-zero terms in this new representation and discuss its asymptotic behavior when those digits are randomly chosen from a given set. We also propose a specific method for the precomputation phase of the exponentiation algorithm

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

An Improved Trace Driven Instruction Cache Timing Attack on RSA

The previous I-cache timing attacks on RSA which exploit the instruction path of a cipher were mostly proof-of-concept, and it is harder to put them into practice than D-cache timing attacks. We propose a new trace driven timing attack model based on spying on the whole I-cache. An improved analysis algorithm of the exponent using the characteristic of the size of the window is advanced, which could further reduce the search space of the bits of the key than the former and provide an error detection mechanism to detect some erroneous decisions of the operation sequence. We implemented an attack on RSA of OpenSSL under a practical environment, proving that the feasibility and effectiveness of I-Cache timing attack could be improved

An enhanced performance model for metamorphic computer virus classification and detectioN

Metamorphic computer virus employs various code mutation techniques to change its code to become new generations. These generations have similar behavior and functionality and yet, they could not be detected by most commercial antivirus because their solutions depend on a signature database and make use of string signature-based detection methods. However, the antivirus detection engine can be avoided by metamorphism techniques. The purpose of this study is to develop a performance model based on computer virus classification and detection. The model would also be able to examine portable executable files that would classify and detect metamorphic computer viruses. A Hidden Markov Model implemented on portable executable files was employed to classify and detect the metamorphic viruses. This proposed model that produce common virus statistical patterns was evaluated by comparing the results with previous related works and famous commercial antiviruses. This was done by investigating the metamorphic computer viruses and their features, and the existing classifications and detection methods. Specifically, this model was applied on binary format of portable executable files and it was able to classify if the files belonged to a virus family. Besides that, the performance of the model, practically implemented and tested, was also evaluated based on detection rate and overall accuracy. The findings indicated that the proposed model is able to classify and detect the metamorphic virus variants in portable executable file format with a high average of 99.7% detection rate. The implementation of the model is proven useful and applicable for antivirus programs

Using Random Digit Representation for Elliptic Curve Scalar Multiplication

Elliptic Curve Cryptography (ECC) was introduced independently by Miller and Koblitz in 1986. Compared to the integer factorization based Rivest-Shamir-Adleman (RSA) cryptosystem, ECC provides shorter key length with the same security level. Therefore, it has advantages in terms of storage requirements, communication bandwidth and computation time. The core and the most time-consuming operation of ECC is scalar multiplication, where the scalar is an integer of several hundred bits long. Many algorithms and methodologies have been proposed to speed up the scalar multiplication operation. For example, non-adjacent form (NAF), window-based NAF (wNAF), double bases form, multi-base non-adjacent form and so on. The random digit representation (RDR) scheme can represent any scalar using a set that contains random odd digits including the digit 1. The RDR scheme is efficient in terms of the average number of non-zeros and it also provides resistance to power analysis attacks. In this thesis, we propose a variant of the RDR scheme. The proposed variant, referred to as implementation-friendly recoding algorithm (IFRA), is advantageous over RDR in hardware implementation for two reasons. First, IFRA uses simple operations such as scan, match, and shift. Second, it requires no long adder to update the scalar. In this thesis we also investigate the average density of non-zero digits of IFRA. It is shown that the average density of the variant is close to the average density of RDR. Moreover, a hardware implementation of the variant scheme is presented using pre-computed values stored in one dual-port memory. A performance comparison for different recoding schemes is presented by demonstrating the run-time efficiency of IFRA compared to other recoding schemes. Finally, the IFRA is applied to scalar multiplication on ECC and we compare its computation time against those based on NAF, wNAF, and RDR

Research on performance enhancement for electromagnetic analysis and power analysis in cryptographic LSI

制度:新 ; 報告番号:甲3785号 ; 学位の種類:博士(工学) ; 授与年月日:2012/11/19 ; 早大学位記番号:新6161Waseda Universit

Passive IoT Device-Type Identification Using Few-Shot Learning

The ever-growing number and diversity of connected devices have contributed to rising network security challenges. Vulnerable and unauthorized devices may pose a significant security risk with severe consequences. Device-type identification is instrumental in reducing risk and thwarting cyberattacks that may be caused by vulnerable devices. At present, IoT device identification methods use traditional machine learning or deep learning techniques, which require a large amount of labeled data to generate the device fingerprints. Moreover, these techniques require building a new model whenever a new device is introduced. To address these limitations, we propose a few-shot learning-based approach on siamese neural networks to identify IoT device-type connected to a network by analyzing their network communications, which can be effective under conditions of insufficient labeled data and/or resources. We evaluate our method on data obtained from real-world IoT devices. The experimental results show the effectiveness of the proposed method even with a small amount of data samples. Besides, it indicates that our approach outperforms IoT Sentinel, the state-of-the-art approach for IoT fingerprinting, by a margin of 10% additional accuracy

A survey of timing channels and countermeasures

A timing channel is a communication channel that can transfer information to a receiver/decoder by modulating the timing behavior of an entity. Examples of this entity include the interpacket delays of a packet stream, the reordering packets in a packet stream, or the resource access time of a cryptographic module. Advances in the information and coding theory and the availability of high-performance computing systems interconnected by high-speed networks have spurred interest in and development of various types of timing channels. With the emergence of complex timing channels, novel detection and prevention techniques are also being developed to counter them. In this article, we provide a detailed survey of timing channels broadly categorized into network timing channel, in which communicating entities are connected by a network, and in-system timing channel, in which the communicating entities are within a computing system. This survey builds on the last comprehensive survey by Zander et al. [2007] and considers all three canonical applications of timing channels, namely, covert communication, timing side channel, and network flow watermarking. We survey the theoretical foundations, the implementation, and the various detection and prevention techniques that have been reported in literature. Based on the analysis of the current literature, we discuss potential future research directions both in the design and application of timing channels and their detection and prevention techniques