With recent advancements in the automotive world and the introduction of autonomous vehicles, automotive security has become a real and important issue. As modern vehicles built with tens of Electronic Control Units (ECUs) connected to the in-vehicle networks, the Controller Area Network (CAN) has become a target of cyber attacks. Anomaly-based Intrusion Detection System (IDS) is considered as one effective approach to secure CAN and detect malicious attacks. Currently, there are two primary approaches used for intrusion detection: rule-based and machine learning-based. Rule-based approach is efficient but limited in the detection accuracy while machine learning-based detection has comparably higher detection accuracy but higher computation cost at the same time. In this paper, we propose a novel hybrid IDS that combines the benefits of both rule-based and machine learning-based approaches. More specifically, we use machine learning to achieve a high detection rate while keeping the low computational requirement by offsetting the detection with a rule-based component. Our experiments with CAN traces collected from four different vehicle models demonstrate the effectiveness and efficiency of the proposed hybrid IDS.

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS firstly leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9% more information is disclosed through de-anonymization and the de-anonymized ratio is 84%. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

Controller Area Network (CAN) is the de facto standard for in-vehicle networks. However, it is inherently vulnerable to various attacks due to the lack of security features. Intrusion detection systems (IDSs) are considered effective approaches to protect in-vehicle networks. IDSs based on advanced deep learning algorithms have been proposed to achieve higher detection accuracy. However, those systems generally involve high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory and energy costs, we propose a new IDS system using Binarized Neural Network (BNN). Compared to full-precision counterparts, BNNs can offer faster detection, smaller memory cost, and lower energy consumption. Moreover, BNNs can be further accelerated by leveraging Field-Programmable Grid Arrays (FPGAs) since BNNs cut down the hardware consumption. The proposed IDS is based on a BNN model that suits CAN traffic messages and takes advantage of sequential features of messages rather than each individual message. We also explore various design choices of BNN, including increasing network width and depth, to improve accuracy as BNNs typically sacrifice accuracy. The performance of our IDS is evaluated with four different real vehicle datasets. Experimental results show that the proposed IDS reduces the detection latency (3 times faster) on the same CPU platform while maintaining acceptable detection rates compared with full-precision models. We also examine the proposed IDS on multiple platforms, and our results show that using FPGA hardware reduces the detection latency dramatically (128 times faster) with lower power consumption compared to an embedded CPU device. Furthermore, we evaluate BNNs with different designs. Our experimental results demonstrate that wider or deeper models definitely improve accuracy at the cost of increased latency and model sizes to varying degrees. Applications are recommended to choose the appropriate model design they need depending on available resources they have.

Aiming at insufficient situation to express and operate assets in smart contracts, in this article we attempt to add a new asset model into smart contract language (such as SPESC) through combing method of asset's expressions and transactions in real-world contracts. Moreover, a translation mechanism can be set up to accomplish a conversion from the asset model to an executable contract program. On this basis, we propose a new language design toward asset-driven specific smart contracts, called TA-SPESC. This language complies with the structure of real-world contracts and supports a formal definition composed of four modules: Party, asset, term, and contract attribute. This asset model on it can be used to define various types of rights (including the right of ownership, use, possession, usufruct, and disposition of assets), as well as five asset operations (including asset registration, deposit, withdrawal, transfer, and cancellation) to effectively support asset transaction. More important, a series of generation rules are proposed to translate the TA-SPESC contract to an executable contract program. Moreover, taking house rental contract as an example, we provide a TA-SPESC instance and its specific description of translation process according to the generation rules, which supports a semiautomatic generation to executable programs. Finally, the Solidity codes derived from TA-SPESC contracts are run and tested, and the experiment and comparison results indicate that TA-SPESC contracts have high abstraction and low complexity, as well as versatility and convenience of asset transaction, which lead to more reliable software with less errors and fewer misunderstanding.

This article aims to establish a cryptographic solution to improve security and reliability of the National Institute of Standards and Technology’s attribute-based access control (ABAC) model. By breaking down the existing structure of attribute-based encryption, we propose a new cryptographic ABAC (C-ABAC) framework with dynamic policy authorization and real-time attribute credentials. Moreover, a practical C-ABAC construction is proposed to support provable policy decision making and verifiable attribute Tokens among multiple distributed authorities. In this construction, we develop a concrete approach of generating a cryptographic policy from access control markup language. We also prove that attribute Token has existential unforgeability under chosen-attribute and chosen-nonce attacks, and the cryptographic policy is existentially unforgeable under chosen-object attack. In addition, our C-ABAC construction provides semantic security against chosen-plaintext attack with Token and policy queries under the extended general Diffie–Hellman exponent assumption. Finally, we evaluate the performance of the C-ABAC system according to complexity analysis and experimental results. The results show that the C-ABAC system is reliable and easy to implement.

Malware is a burgeoning threat for smartphones and continuing advancing. Traditional defenses to malware, however, are not suitable for smartphones due to their resource intensive nature. This necessitates the design of novel mechanisms that can consider the specifics of the smartphone malware and smartphones themselves. In this paper, we introduce a lightweight permission enforcement approach-Tap-Wave-Rub (TWR)-for smartphone malware prevention. TWR is based on simple cyber-physical human interactions, i.e., human gestures, that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In particular, we present the design of two mechanisms: 1) acceleration-based phone tapping detection and 2) proximity-based finger tapping, rubbing, or hand waving detection. The first mechanism is geared for near field communication applications, which usually require the user to tap her phone with another device. The second mechanism involves very simple gestures, i.e., tapping or rubbing a finger near the top of phone's screen or waving a hand close to the phone, and broadly appeals to many applications (e.g., SMS). In addition, we present the TWR-enhanced Android permission model, the prototypes implementing the underlying gesture recognition mechanisms, and a variety of novel experiments to evaluate these mechanisms. Our results suggest the proposed approach could be very effective for malware detection/prevention, with quite low false positives and false negatives, while imposing little to no additional burden on the users.