User profiling is one of the most important research topics where organizations endeavour to establish profiles of user activities to detect or predict potential abnormal behaviours. Previous researches have mainly focused on detecting and identifying static activities through social media. A universal analysis based on streaming settings to monitor user activities continuously is missing. This paper proposes a framework for user profiling based on UDI platforms to address this issue. Our framework consists of three main steps: simulating realistic scenarios for user activities, proposing and extracting potential features, and applying machine learning models on simulated datasets. Our experimental results show that selected machine learning algorithms can distinguish most abnormal behaviours correctly. LODA, RRCF, and LSCP algorithms achieve the highest performance among all algorithms. Tree-based algorithms such as Isolation Forest acquire the best results when considering small datasets and speed. Furthermore, machine learning algorithms’ performance demonstrates the high quality of our simulated datasets.

Hierarchical Temporal Memory (HTM) is a continuously learning algorithm derived from neuroscience that models spatial and temporal streaming data. It was demonstrated that HTM produces a good performance in predicting unusual patterns or anomaly detection in univariate datasets. In this paper, we deploy the HTM algorithm for the anomaly detection problem in multivariate datasets, which are more common in practical scenarios. We first investigate the implementation of HTM using multi-encoders for multiple variables and analyze its performance in different parameter settings. Then, we introduce a new framework for ensemble learning by using single-encoder HTMs as weak learners. We carried out experiments on public datasets in different dimensions. Our experimental results show that our new approach outperforms the multi-encoder implementation of the HTM algorithm.

A multi signature scheme allows a group of signers to produce a joint signature on a common message, which is more compact than a collection of distinct signatures from all signers. Given this signature and the list of signers' public keys, a verifier is able to check if every signer in the group participated in signing. Recently, a multisignature scheme with public key aggregation has drawn a lot of attention due to their applications into the blockchain technology. Such multisignatures provide not only a compact signature, but also a compact aggregated public key, that is both the signature size and the public key size used to verify the correctness of the signature are independent from the number of signers. This is useful for a blockchain because of its duplication over a distributed network, and thus it is required to be as compact as possible. In this paper, we introduce a new multisignature scheme with such a feature. Our scheme is proven secure under the Decisional Diffie-Hellman assumption. In addition, in the presence of rogue key attacks, the security of our scheme is proven in the plain public key model.

Algebraic differential fault attack (ADFA) is an attack in which an attacker combines a differential fault attack and an algebraic technique to break a targeted cipher. In this paper, we present three attacks using three different algebraic techniques combined with a differential fault attack in the bit-flip fault model to break the SIMON block cipher. First, we introduce a new analytic method which is based on a differential trail between the correct and faulty ciphertexts. This method is able to recover the entire master key of any member of the SIMON family by injecting faults into a single round of the cipher. In our second attack, we present a simplified Grobner basis algorithm to solve the faulty system. We show that this method could totally break SIMON ciphers with only 3 to 5 faults injected. Our third attack combines a fault attack with a modern SAT solver. By guessing some key bits and with only a single fault injected at the round T - 6, where T is the number of rounds of a SIMON cipher, this combined attack could manage to recover a master key of the cipher. For the last two attacks, we perform experiments to demonstrate the effectiveness of our attacks. These experiments are implemented on personal computers and run in very reasonable timing.

The ubiquitous deployment of Internet of Things (IoT) devices enhances connectivity and communication, and benefits almost every aspect of our lives from manufacturing to retail to smart homes. However, low levels of security protection in these devices due to their limited resources open opportunities for malicious users. An IoT forensics system collecting, processing, analyzing and reporting evidence of attack is required to mitigate the IoT security issues. Although such system has been studied over the past decade and solutions such as cloud-based IoT forensic were proposed, limitation still exist. In this paper, leveraging on the blockchain technology, we propose a per-missioned blockchain-based IoT forensics framework to enhance the integrity, authenticity and non-repudiation properties for the collected evidence. We formally define the system architecture, provide framework details, and propose a cryptographic-based approach to mitigate identity privacy concern.

Since Edwards curves were introduced to elliptic curve cryptography by Bernstein and Lange in 2007, they have received a lot of attention due to their very fast group law operation. Pairing computation on such curves is slightly slower than on Weierstrass curves. However, in some pairing-based cryptosystems, they might require a number of scalar multiplications which is time-consuming operation and this can be advantageous to use Edwards in this scenario. In this paper, we present a variant of Miller’s algorithm for pairing computation on Edwards curves. Our approach is generic, it is able to compute both Weil and Tate pairings on pairing-friendly Edwards curves of any embedding degree. Our analysis shows that the new algorithm is faster than the previous algorithms for odd embedding degree and as fast as for even embedding degree. Hence, the new algorithm is suitable for computing optimal pairings and in situations where the denominators elimination technique is not possible.

In 1986, Victor Miller described an algorithm for computing the Weil pairing in his unpublished manuscript. This algorithm has then become the core of all pairing-based cryptosystems. Many improvements of the algorithm have been presented. Most of them involve a choice of elliptic curves of a special form to exploit a possible twist during Tate pairing computation. Other improvements involve a reduction of the number of iterations in the Miller's algorithm. For the generic case, Blake, Murty and Xu proposed three refinements to Miller's algorithm over Weierstrass curves. Though their refinements, which only reduce the total number of vertical lines in Miller's algorithm, did not give an efficient computation as other optimizations, they can be applied for computing both Weil and Tate pairings on all pairing-friendly elliptic curves. In this paper, we extend the Blake–Murty–Xu's method and show how to perform an elimination of all vertical lines in Miller's algorithm during computation of Weil/Tate pairings, on general elliptic curves. Experimental results show that our algorithm is faster by ∼25% in comparison with the original Miller's algorithm.

Timestamping is a cryptographic technique providing us with a proof-of-existence of a digital document at a given time. Combining both digital signature and provable time-stamping guarantees authentication, integrity and non-repudiation of electronic documents. In this paper, we introduce such a service, so called signtiming. Our scheme is based on an ID-based aggregate signature and is secure in the random oracle model.