Cryptography-based Solutions Research Articles

A Systematic Literature Review on Authentication and Threat Challenges on RFID Based NFC Applications

The Internet of Things (IoT) concept is tremendously applied in our current daily lives. The IoT involves Radio Frequency Identification (RFID) as a part of the infrastructure that helps with the data gathering from different types of sensors. In general, security worries have increased significantly as these types of technologies have become more common. For this reason, manifold realizations and studies have been carried out to address this matter. In this work, we tried to provide a thorough analysis of the cryptography-based solutions for RFID cards (MIFARE cards as a case study) by performing a Systematic Literature Review (SLR) to deliver the up-to-date trends and outlooks on this topic.

Security in wireless body area networks via anonymous authentication: Comprehensive literature review, scheme classification, and future challenges

Authentication is a security practice that seeks to validate a user identity to various entities, yet doing so across a wireless network poses inherent risks. Wireless Body Area Networks (WBANs) are a unique form of network that contains e-healthcare patient data, thus WBANs require a high level of privacy and security. There are several anonymous WBAN authentication systems in the literature, but this is the first paper to conduct a complete review of all the various schemes, then tabulate and compare the results. Doing so reveals a number of research avenues that need either greater depth, or systems development. This article presents a thorough taxonomy and survey of the anonymous authentication methods, illustrating the security services and vulnerabilities, and then the properties of an ideal anonymous authentication scheme. The various schemes reviewed are also classified by their main encryption algorithm, such as bilinear parings-based schemes, elliptic curve cryptography-based solutions, lattice-based methods, and XOR-based approaches. The main authentication capabilities, cryptographic features, security advantages, evaluation metrics, and shortcomings are all specified and discussed. A thorough comparison of anonymous authentication methods is presented to demonstrate each scheme's resistance to a variety of security threats. The study concludes that very few schemes have addressed the group authentication problem, which is very important in healthcare systems. Also, in the multi-factor authentication context, which provides a higher level of security, only 11% of the schemes used three factors, and 1% of them utilized four factors. Besides, DDoS attacks, as a growing concern in all computing environments, are handled by a narrow number of schemes. At last, a set of recommendations for further research based on identified literature gaps.

L-SecNet: Towards Secure and Lightweight Deep Neural Network Inference

The advances in machine learning technology has promoted its great potential for deep neural network (DNN) inference powered applications of Internet of Things (IoT), such as facial verification cameras and speech recognition assistants. The current deployment of these applications also raises serious privacy concerns, especially when sensitive individual information is accessed easily by various IoT devices. Fortunately, the cryptography-based solutions are able to execute secure inference without infringing the user’s raw data and the model owner’s proprietary model. However, existing works suffer from impractically high latency and low accuracy, stemming primarily from the evaluations of the non-linear layers in DNN. In this paper, we propose L-SecNet, a lightweight secure neural network inference system that provides efficient inference services without sacrificing accuracy and privacy. Specifically, to reduce latency caused by comparison operations in non-linear layers, we subtly combine additive secret sharing and multiplicative secret sharing to design a lightweight secure comparison protocol. Further, we approximate the commonly used and time-consuming activation functions (including Sigmoid and Tanh functions) with the non-linear sin function instead of the linear polynomial approximation functions in the existing works. In order to maintain low running latency while meeting the requirement of high accuracy, a secure and lightweight protocol for a sin function is proposed. Our theoretical analysis and empirical experiments evaluate the security and efficiency of the L-SecNet system. Compared with the state-of-the-art works, L-SecNet saves up to about 80 times bandwidth and about 53 times runtime.

ARPVP: Attack Resilient Position-Based VANET Protocol Using Ant Colony Optimization

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

Secure Transmission Using Linearly Distributed Virtual Antenna Array With Element Position Perturbations

Physical-layer security (PLS) is a promising alternative or complement to cryptography-based solutions, because security key management and distribution required in such schemes are challenging in future wireless technologies with a distributed nature (e.g., Internet-of-Things (IoT) and massive machine-type communication). Motivated by this fact, in this paper, we investigate a PLS algorithm using linear virtual antenna array (VAA) constructed by multiple single-antenna nodes in mobile ad hoc networks such as unmanned aerial vehicles (UAVs) and mobile robots. Digital cooperative beamforming (CB) has been extensively explored such as cooperative null-steering or jamming. However, in spite of their effectiveness, the digital CB-based schemes incur prohibitive overhead and complexity, which creates difficulty in networks with limited hardware (e.g., IoT). To overcome this issue, we consider an analog CB-based PLS with linearly distributed VAA, which can be implemented in a fully distributed manner. In the proposed scheme, the received signal at the intended receiver is co-phased, while other receivers capture noise-like signals through randomized radiation patterns using the linear VAA. Assuming Rician channels, we derive secrecy rate of the proposed algorithm in a closed-from expression for an arbitrary K-factor, which is not available in previous work on the analog CB-based PLS. Furthermore, we show that the random location perturbations of the VAA elements following Gaussian distributions can enhance the secrecy performance by resolving the angle-of-departure ambiguity issue in linear arrays.

IC-MADS: IoT Enabled Cross Layer Man-in-Middle Attack Detection System for Smart Healthcare Application

Smart healthcare is one of the core applications of the Internet of Things (IoT) technology in which the body sensors capture the patient's periodic data and transmit seamlessly over the wireless communications to remote monitoring stations. Due to the open nature of wireless communications, privacy-preserving and security are the biggest concern for IoT enabled healthcare systems. Several trust-based and cryptography-based solutions proposed to achieve the privacy-preserving and data security, however, such layered solutions failed to address the cross-layer attacks such as Man-in-Middle Attack (MIMA). Thus we believe that cross-layer attack detection methods achieve effective protection against such attacks. This paper proposed the novel lightweight cross-layer trust computation algorithm for MIMA attacker detection called IC-MADS. The IC-MADS consists of two main contributions such as energy-efficient clustering and cross-layer attack detection. The clustering algorithm proposed for optimal Cluster Head (CH) selection using the probability computation and evaluation approach. Each sensor node probability is computed using the parameters like residual energy, distance to Base Station, and node degree parameters. The node with a higher probability value becomes the CH. This solves the energy imbalance and load balancing problems. The cross-layer trust evaluation approach introduced node evaluation to detect the MIMA attackers in the network. The trust computation of each node is performed by using the sensor nodes parameters across the different layers such as network, physical, and MAC layers. The aggregate trust value of each sensor node is then compared with the threshold value to check whether the node is an attacker. The simulation results prove that IC-MADS achieves better protection against MIMA attacks with minimum overhead and energy consumption.

Pay as You Go: A Generic Crypto Tolling Architecture

The imminent pervasive adoption of vehicular communication, either localized (ETSI ITS G5, IEEE WAVE or cellular D2D), long range (5G) or hybrid, will foster a richer service ecosystem for vehicular applications. The appearance of new cryptography-based solutions envisaging digital identity and currency exchange are set to stem new approaches for existing and future challenges. This article presents a novel tolling architecture that harnesses the availability of 5G C-V2X connectivity for open road tolling using smartphones, IOTA as the digital currency, and Hyperledger Indy for identity validation. An experimental feasibility analysis is used to validate the proposed architecture for secure, private, and convenient electronic toll payment.

An Attack-Resistant Trust Inference Model for Securing Routing in Vehicular Ad Hoc Networks

The problem of routing security in vehicular ad hoc networks has become a major concern for researchers. Compared with cryptography-based solutions, trust-based solutions are considered to be more acceptable as a promising approach, which mainly define two operations: trust computing and security application. In this paper we first study trust properties and construct a novel trust inference model, where two trust attributes named subjective trust and recommendation trust, are selected to quantify the trust level of a specific vehicle. The SCGM(1,1)-weighted Markov prediction algorithm based on fluctuation recognition is utilized to calculate the subjective trust accurately, meanwhile a new evaluation method of recommendation credibility based on the feedback mechanism is introduced for the calculation of recommendation trust. We subsequently exploit the advantages of mesh structure and design a light-weight trust-aware multicast routing protocol (i.e., LTMRP), which can establish secure and reliable communication paths by selecting trusted relay vehicles. To further improve the routing efficiency, two mechanisms including forwarding nodes reuse mechanism and trust-aware route handoff mechanism are proposed. Simulation verification and comparison of the relevant trust models and trust-based protocols are accomplished by three simulation platforms (i.e., Netlogo, SUMO, and NS-2).The convincing results show that our trust model obtains strong resistibility to multiple malicious attacks. Moreover, examined in four routing metrics, the experimental results indicate that LTMRP performs better than the other relevant protocols.

Preserving privacy in speaker and speech characterisation

Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via speaker characterisation. In promoting harmonised research, the article also outlines common, empirical evaluation metrics for the assessment of privacy-preserving technologies for speech data.

Homomorphic Subspace MAC Scheme for Secure Network Coding

Existing symmetric cryptography-based solutions against pollution attacks for network coding systems suffer various drawbacks, such as highly complicated key distribution and vulnerable security against collusion. This letter presents a novel homomorphic subspace message authentication code (MAC) scheme that can thwart pollution attacks in an efficient way. The basic idea is to exploit the combination of the symmetric cryptography and linear subspace properties of network coding. The proposed scheme can tolerate the compromise of up to r−1 intermediate nodes when r source keys are used. Compared to previous MAC solutions, less secret keys are needed for the source and only one secret key is distributed to each intermediate node.

Homomorphic Subspace MAC Scheme for Secure Network Coding

Existing symmetric cryptography-based solutions against pollution attacks for network coding systems suffer various drawbacks, such as highly complicated key distribution and vulnerable security against collusion. This letter presents a novel homomorphic subspace message authentication code (MAC) scheme that can thwart pollution attacks in an efficient way. The basic idea is to exploit the combination of the symmetric cryptography and linear subspace properties of network coding. The proposed scheme can tolerate the compromise of up to r−1 intermediate nodes when r source keys are used. Compared to previous MAC solutions, less secret keys are needed for the source and only one secret key is distributed to each intermediate node.

Autonomous security for autonomous systems

The Internet's interdomain routing protocol, BGP, supports a complex network of Autonomous Systems which is vulnerable to a number of potentially crippling attacks. Several promising cryptography-based solutions have been proposed, but their adoption has been hindered by the need for community consensus, cooperation in a public key infrastructure (PKI), and a common security protocol. Rather than force centralized control in a distributed network, this paper examines distributed security methods that are amenable to incremental deployment. Typically, such methods are less comprehensive and not provably secure. The paper describes a distributed anomaly detection and response system that provides comparable security to cryptographic methods and has a more plausible adoption path. Specifically, the paper makes the following contributions: (1) it describes pretty good BGP (PGBGP), whose security is comparable (but not identical) to secure origin BGP; (2) it gives theoretical proofs on the effectiveness of PGBGP; (3) it reports simulation experiments on a snapshot of the Internet topology annotated with the business relationships between neighboring networks; (4) it quantifies the impact that known exploits could have on the Internet; and (5) it determines the minimum number of ASes that would have to adopt a distributed security solution to provide global protection against these exploits. Taken together these results explore the boundary between what can be achieved with provably secure centralized security mechanisms for BGP and more distributed approaches that respect the autonomous nature of the Internet.

Resolving the Micropayment Problem

Cryptography-based alternatives are the most appropriate long-term solution to the micropayment problem, especially compared to the business solution of restructuring transaction fees. We acknowledge that the business solution is easier to deploy in the short term. Cryptography-based solutions face tremendous deployment challenges: they need more market testing, they significantly change payment-ecosystem processes, their legal implications are unclear, and they require new kinds of devices and processes for merchants. Nonetheless, the long-term payoff of investing in such sound solutions are be tremendous