Biblio

Biometric matching involves storing and processing sensitive user information. Maintaining the privacy of this data is thus a major challenge, and homomorphic encryption offers a possible solution. We propose a privacy-preserving biometrics-based authentication protocol based on fully homomorphic en-cryption, where the biometric sample for a user is gathered by a local device but matched against a biometric template by a remote server operating solely on encrypted data. The design ensures that 1) the user's sensitive biometric data remains private, and 2) the user and client device are securely authenticated to the server. A proof-of-concept implementation building on the TFHE library is also presented, which includes the underlying basic operations needed to execute the biometric matching. Performance results from the implementation show how complex it is to make FHE practical in this context, but it appears that, with implementation optimisations and improvements, the protocol could be used for real-world applications.

As a decoy for hackers, honeypots have been proved to be a very valuable tool for collecting real data. However, due to closed source and vendor-specific firmware, there are significant limitations in cost for researchers to design an easy-to-use and high-interaction honeypot for industrial control systems (ICSs). To solve this problem, it’s necessary to find a cost-effective solution. In this paper, we propose a novel honeypot architecture termed HoneyVP to support a semi-virtual and semi-physical honeypot design and implementation to enable high cost performance. Specially, we first analyze cyber-attacks on ICS devices in view of different interaction levels. Then, in order to deal with these attacks, our HoneyVP architecture clearly defines three basic independent and cooperative components, namely, the virtual component, the physical component, and the coordinator. Finally, a local-remote cooperative ICS honeypot system is implemented to validate its feasibility and effectiveness. Our experimental results show the advantages of using the proposed architecture compared with the previous honeypot solutions. HoneyVP provides a cost-effective solution for ICS security researchers, making ICS honeypots more attractive and making it possible to capture physical interactions.

New technologies from day to day are submitted with many vulnerabilities that can make data exploitation. Nowadays, IoT is a target for Cybercrime attacks as it is one of the popular platforms in the century. This research address the IoT security problem by carried a medium-interaction honeypot. Honeypot is one of the solutions that can be done because it is a system feed for the introduction of attacks and fraudulent devices. This research has created a medium interaction honeypot using Cowrie, which is used to maintain the Internet of Things device from malware attacks or even attack patterns and collect information about the attacker's machine. From the result analysis, the honeypot can record all trials and attack activities, with CPU loads averagely below 6,3%.

What do we need to do to protect our personal information? IoT devices such as smartphones, smart watches, and home appliances are widespread. Encryption is required not only to prevent eavesdropping on communications but also to prevent information leakage from cloud services due to unauthorized access. Therefore, attention is being paid to fully homomorphic encryption (FHE) that allows addition and multiplication between ciphertexts. However, FHE with this convenient function has a drawback that the encryption requires huge volume of calculation and the ciphertext is large. Therefore, if FHE is used on a device with limited computational resources such as an IoT device, the load on the IoT device will be too heavy. In this research, we propose a system that can safely and effectively utilize data without imposing a load on IoT devices. In this system, somewhat homomorphic encryption (SHE), which is a lightweight cryptosystem compared with FHE, is combined with FHE. The results of the experiment confirmed that the load on the IoT device can be reduced to approximately 1/1400 compared to load of the system from previous research.

Recently deep learning has demonstrated much success within the fields of image and natural language processing, facial recognition, and computer vision. The success is attributed to large, accessible databases and deep learning's ability to learn highly accurate models. Thus, deep learning is being investigated as a viable end-to-end approach to digital communications design. This work investigates the use of adversarial deep learning to ensure that a radio can communicate covertly, via Direct Sequence Spread Spectrum (DSSS), with another while a third (the adversary) is actively attempting to detect, intercept and exploit their communications. The adversary's ability to detect and exploit the DSSS signals is hindered by: (i) generating a set of spreading codes that are balanced and result in low side lobes as well as (ii) actively adapting the encoding scheme. Lastly, DSSS communications performance is assessed using energy constrained devices to accurately portray IoT and IoBT device limitations.

The Industrial Internet of Things is expected to attract significant investments for Industry 4.0. In this new environment, the blockchain has immediate potential in industrial applications, providing unchanging, traceable and auditable access control. However, recent work and present in blockchain literature are based on a cloud infrastructure that requires significant investments. Furthermore, due to the placement and distance of the cloud infrastructure to industrial control devices, such approaches present a communication latency that can compromise the strict deadlines for accessing and communicating with this device. In this context, this article presents a blockchain-based access control architecture, which is deployed directly to edge devices positioned close to devices that need access control. Performance assessments of the proposed approach were carried out in practice in an industrial mining environment. The results of this assessment demonstrate the feasibility of the proposal and its performance compared to cloud-based approaches.

Internal attacks are one of the biggest cybersecurity issues to companies and businesses. Despite the implemented perimeter security systems, the risk of adversely affecting the security and privacy of the organization’s information remains very high. Actually, the detection of such a threat is known to be a very complicated problem, presenting many challenges to the research community. In this paper, we investigate the effectiveness and usefulness of using Autoencoder and Variational Autoencoder deep learning algorithms to automatically defend against insider threats, without human intervention. The performance evaluation of the proposed models is done on the public CERT dataset (CERT r4.2) that contains both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a higher detection accuracy and a reasonable false positive rate.

Currently, more and more malicious insiders are making threats, and the detection of insider threats is becoming more challenging. The malicious insider often uses legitimate access privileges and mimic normal behaviors to evade detection, which is difficult to be detected via using traditional defensive solutions. In this paper, we propose DeepMIT, a malicious insider threat detection framework, which utilizes Recurrent Neural Network (RNN) to model user behaviors as time sequences and predict the probabilities of anomalies. This framework allows DeepMIT to continue learning, and the detections are made in real time, that is, the anomaly alerts are output as rapidly as data input. Also, our framework conducts further insight of the anomaly scores and provides the contributions to the scores and, thus, significantly helps the operators to understand anomaly scores and take further steps quickly(e.g. Block insider's activity). In addition, DeepMIT utilizes user-attributes (e.g. the personality of the user, the role of the user) as categorical features to identify the user's truly typical behavior, which help detect malicious insiders who mimic normal behaviors. Extensive experimental evaluations over a public insider threat dataset CERT (version 6.2) have demonstrated that DeepMIT has outperformed other existing malicious insider threat solutions.

In this work, a new human-computer assistive technology gadget designed for people with impairments is evaluated. The developed human-in-the-loop interface device has an embedded assistance controller and can replace the traditional mouse, gamepad and keyboard, enabling human-computer hands-free full access. This work is concerned with the assistive device performance characterization aspects. Based on the experiments carried out, the human-computer performance improvement with the embedded controller is analysed in detail. Results show that adding the human-in-the-loop assistance controller improves human-computer hands-free skills, which is an innovative contribution for the replacement of computer interfaces that depend on the human hands.

With the rapid development of the wireless network and smart mobile equipment, many lawbreakers employ mobile devices to destroy and steal important information and property from other persons. In order to fighting the criminal act efficiently, the public security organ need to collect the evidences from the crime tools and submit to the court. In the meantime, with development of internal storage technology, the law enforcement officials collect lots of information from the smart mobile equipment, for the sake of handling the huge amounts of data, we propose a framework that combine distributed clustering methods to analyze data sets, this model will split massive data into smaller pieces and use clustering method to analyze each smaller one on disparate machines to solve the problem of large amount of data, thus forensics investigation work will be more effectively.

In this paper, we focus on Information-Centric Delay-Tolerant Network (ICDTN), which incorporates the communication paradigm of Information-Centric Networking (ICN) into Delay-Tolerant Networking (DTN). Conventional ICNs adopt a naming scheme that names the content with the content identifier. However, a past study proposed an alternative naming scheme that describes the name of content with the content descriptor. We believe that, in ICDTN, it is more suitable to utilize the approach using the content descriptor. In this paper, we therefore propose keyword-based ICDTN that resolves content requests and deliveries contents based on keywords, i.e., content descriptor, in the request and response messages.

The impending threat of large-scale quantum computers to classical RSA and ECC-based public-key cryptographic schemes prompted NIST to initiate a global level standardization process for post-quantum cryptography. This process which started in 2017 with 69 submissions is currently in its third and final round with seven main candidates and eight alternate candidates, out of which seven (7) out of the fifteen (15) candidates are schemes based on hard problems over structured lattices, known as lattice-based cryptographic schemes. Among the various parameters such as theoretical post-quantum (PQ) security guarantees, implementation cost and performance, resistance against physical attacks such as Side-Channel Analysis (SCA) and Fault Injection Analysis (FIA) has also emerged as an important criterion for standardization in the final round [1]. This is especially relevant for adoption of PQC in embedded devices, which are most likely used in environments where an attacker can have unimpeded physical access to the device.

Recently, most of the processes are being computerized, due to the development of information and communication technology. In proportion to this, cyber-attacks are also increasing, and state-sponsored cyber-attacks are becoming a great threat to the country. These attacks are often composed of stages and proceed step-by-step, so for defense, it is necessary to predict the next action and perform appropriate mitigation. To this end, the paper proposes a mitigation-based performance evaluation method. We developed the new true positive which can have a value between 0 and 1 according to the mitigation. The experiment result and case studies show that the proposed method can effectively measure forecasting results under cyber security defense system.

This paper represents the experimental implementation of an encryption-based visible light communication system for indoor communication over 14m, two single LED transmitters as the data source, and four receivers considered as data receivers for performance evaluation.

A grouping-proof protocol aims to generate an evidence that two or more RFID (Radio Frequency Identification) tags in a group are coexistent, which has been widely deployed in practical scenarios, such as healthcare, supply-chain management, and so on. However, existing grouping-proof protocols have many issues in security and efficiency, either incompatible with EPCglobal Class-1 Generation-2 (C1G2) standard, or vulnerable to different attacks. In this paper, we propose a lightweight grouping-proof protocol which only utilizes bitwise operations (AND, XOR) and 128-bit pseudorandom number generator (PRNG). 2-round interactions between the reader and the tags allow them to cooperate on fast authentication in parallel mode where the reader broadcasts its round messages rather than hang on for the prior tag and then fabricate apposite output for the next tag consecutively. Our design enables the reader to aggregate the first round proofs (to bind the membership of tags in the same group) generated by the tags to an authenticator of constant size (independent of the number of tags) that can then be used by the tags to generate the second round proofs (and that will be validated by the verifier). Formal security (i.e., PPT adversary cannot counterfeit valid grouping-proof that can be accepted by any verifier) of the proposed protocol relies on the hardness of the learning parity with noise (LPN) problem, which can resist against quantum computing attacks. Other appealing features (e.g., robustness, anonymity, etc.) are also inspected. Performance evaluation shows its applicability to C1G2 RFID.

The goal of the edge computing framework is to solve the problem of management and control in the access of massive 5G terminals in the power Internet of things. Firstly, this paper analyzes the needs of IOT agent in 5G ubiquitous connection, equipment management and control, intelligent computing and other aspects. In order to meet with these needs, paper develops the functions and processes of the edge computing framework, including unified access of heterogeneous devices, protocol adaptation, edge computing, cloud edge collaboration, security control and so on. Finally, the performance of edge computing framework is verified by the pressure test of 5G wireless ubiquitous connection.

Edge computing supports the deployment of ubiquitous, smart services by providing computing and storage closer to terminal devices. However, ensuring the full security and privacy of computations performed at the edge is challenging due to resource limitation. This paper responds to this challenge and proposes an adaptive approach to defense randomization among the edge data centers via a stochastic game, whose solution corresponds to the optimal security deployment at the network's edge. Moreover, security risk is evaluated subjectively based on Prospect Theory to reflect realistic scenarios where the attacker and the edge system do not similarly perceive the status of the infrastructure. The results show that a non-deterministic defense policy yields better security compared to a static defense strategy.

Internet of things (IoT) healthcare devices, like other IoT devices, typically use proprietary protocol communications. Usually, these proprietary protocols are not audited and may present security flaws. Further, new proprietary protocols are desgined in the field of IoT devices, like data-over-sound communications. Data-over-sound is a new method of communication based on audio with increasing popularity due to its low hardware requirements. Only a speaker and a microphone are needed instead of the specific antennas required by Bluetooth or Wi-Fi protocols. In this paper, we analyze, audit and reverse engineer a modern IoT healthcare device used for performing electrocardiograms (ECG). The audited device is currently used in multiple hospitals and allows remote health monitoring of a patient with heart disease. For this auditing, we follow a black-box reverse-engineering approach and used STRIDE threat analysis methodology to assess all possible attacks. Following this methodology, we successfully reverse the proprietary data-over-sound protocol used by the IoT healthcare device and subsequently identified several vulnerabilities associated with the device. These vulnerabilities were analyzed through several experiments to classify and test them. We were able to successfully manipulate ECG results and fake heart illnesses. Furthermore, all attacks identified do not need any patient interaction, being this a transparent process which is difficult to detect. Finally, we suggest several short-term solutions, centred in the device isolation, as well as long-term solutions, centred in involved encryption capabilities.

In recent times, security and privacy concerns associated with IoT devices have caught the attention of research community. The problem of securing IoT devices is immensely aggravating due to advancement in technology. These IoT devices are resource-constraint i.e. in terms of power, memory, computation, etc., so they are less capable to secure themselves. So we need a better approach to secure IoT devices within the limited resources. Several studies state that for these lightweight IoT devices Elliptic Curve Cryptography (ECC) suits perfectly. But there are several elliptic curve domain parameter standards, which may be used for different security levels. When any ECC based product is deployed then the selection of a suitable elliptic curve standard according to usability is become very important. So we have to choose one suitable standard domain parameter for the required security level. In this paper, two different elliptic curve standard domain parameters named secp256k1 and secp192k1 proposed by an industry consortium named Standards for Efficient Cryptography Group (SECG) [1] are implemented and then analyzed their performances metrics. The performance of each domain parameter is measured in computation time.

As millions of IoT devices are interconnected together for better communication and computation, compromising even a single device opens a gateway for the adversary to access the network leading to an epidemic. It is pivotal to detect any malicious activity on a device and mitigate the threat. Among multiple feasible security threats, malware (malicious applications) poses a serious risk to modern IoT networks. A wide range of malware can replicate itself and propagate through the network via the underlying connectivity in the IoT networks making the malware epidemic inevitable. There exist several techniques ranging from heuristics to game-theory based technique to model the malware propagation and minimize the impact on the overall network. The state-of-the-art game-theory based approaches solely focus either on the network performance or the malware confinement but does not optimize both simultaneously. In this paper, we propose a throughput-aware game theory-based end-to-end IoT network security framework to confine the malware epidemic while preserving the overall network performance. We propose a two-player game with one player being the attacker and other being the defender. Each player has three different strategies and each strategy leads to a certain gain to that player with an associated cost. A tailored min-max algorithm was introduced to solve the game. We have evaluated our strategy on a 500 node network for different classes of malware and compare with existing state-of-the-art heuristic and game theory-based solutions.

In this paper we propose a security and cost aware scheduling heuristic for real-time workflow jobs that process Internet of Things (IoT) data with various security requirements. The environment under study is a four-tier architecture, consisting of IoT, mist, fog and cloud layers. The resources in the mist, fog and cloud tiers are considered to be heterogeneous. The proposed scheduling approach is compared to a baseline strategy, which is security aware, but not cost aware. The performance evaluation of both heuristics is conducted via simulation, under different values of security level probabilities for the initial IoT input data of the entry tasks of the workflow jobs.

Fog computing is a new computing paradigm that utilizes numerous mutually cooperating terminal devices or network edge devices to provide computing, storage, and communication services. Fog computing extends cloud computing services to the edge of the network, making up for the deficiencies of cloud computing in terms of location awareness, mobility support and latency. However, fog nodes are not active enough to perform tasks, and fog nodes recruited by cloud service providers cannot provide stable and continuous resources, which limits the development of fog computing. In the process of cloud service providers using the resources in the fog nodes to provide services to users, the cloud service providers and fog nodes are selfish and committed to maximizing their own payoffs. This situation makes it easy for the fog node to work negatively during the execution of the task. Limited by the low quality of resource provided by fog nodes, the payoff of cloud service providers has been severely affected. In response to this problem, an appropriate incentive mechanism needs to be established in the fog computing environment to solve the core problems faced by both cloud service providers and fog nodes in maximizing their respective utility, in order to achieve the incentive effect. Therefore, this paper proposes an incentive model based on repeated game, and designs a trigger strategy with credible threats, and obtains the conditions for incentive consistency. Under this condition, the fog node will be forced by the deterrence of the trigger strategy to voluntarily choose the strategy of actively executing the task, so as to avoid the loss of subsequent rewards when it is found to perform the task passively. Then, using evolutionary game theory to analyze the stability of the trigger strategy, it proves the dynamic validity of the incentive consistency condition.

The spread of Internet of Things (IoT) devices and high-speed communications, such as 5G, makes their services rich and diverse. Therefore, it is desirable to perform functions of rich services transparently and use edge computing environments flexibly at intermediate locations on the Internet, from the perspective of a network system. When this type of edge computing environment is achieved, IoT nodes as end devices of the Internet can fully utilize edge computing systems and cloud systems without any change, such as switching destination IP addresses between them, along with protocol maintenance for the switching. However, when the data transfer in the communication is encrypted, a decryption method is necessary at the edge, to realize these transparent edge services. In this study, a transparent common key-exchanging method with cloud service has been proposed as the destination node of a communication pair, to transparently decrypt a secure sockets layer-encrypted communication stream at the edge area. This enables end devices to be free from any changes and updates to communicate with the destination node.

Radar sensors have shown great potential for surveillance and security authentication applications. However, a thorough analysis of their vulnerability to spoofing or replay attacks has not been performed yet. In this paper, the feasibility of performing spoofing attacks to radar sensor is studied and experimentally verified. First, a simple binary phase-shift keying system was used to generate artificial spectral components in the radar's demodulated signal. Additionally, an analog phase shifter was driven by an arbitrary signal generator to mimic the human cardio-respiratory motion. Characteristic time and frequency domain cardio-respiratory human signatures were successfully generated, which opens possibilities to perform spoofing attacks to surveillance and security continuous authentication systems based on microwave radar sensors.

The Interface to Network Security Functions (I2NSF) Working Group in Internet Engineering Task Force (IETF) provides data models of interfaces to easily configure Network Security Functions (NSF). The Working Group presents a high-level data model and a low-level data model for configuring the NSFs. The high-level data model is used for the users to manipulate the NSFs configuration easily without any security expertise. But the NSFs cannot be configured using the high-level data model as it needs a low-level data model to properly deploy their security operation. For that reason, the I2NSF Framework needs a security policy translator to translate the high-level data model into the corresponding low-level data model. This paper improves the previously proposed Security Policy Translator by adding an Automatic Data Model Mapper. The proposed mapper focuses on the mapping between the elements in the high-level data model and the elements in low-level data model to automate the translation without the need for a security administrator to create a mapping table.