Biblio

When a robot breaks a person's trust by making a mistake or failing, continued interaction will depend heavily on how the robot repairs the trust that was broken. Prior work in psychology has demonstrated that both the trust violation framing and the trust repair strategy influence how effectively trust can be restored. We investigate trust repair between a human and a robot in the context of a competitive game, where a robot tries to restore a human's trust after a broken promise, using either a competence or integrity trust violation framing and either an apology or denial trust repair strategy. Results from a 2×2 between-subjects study ( n=82) show that participants interacting with a robot employing the integrity trust violation framing and the denial trust repair strategy are significantly more likely to exhibit behavioral retaliation toward the robot. In the Dyadic Trust Scale survey, an interaction between trust violation framing and trust repair strategy was observed. Our results demonstrate the importance of considering both trust violation framing and trust repair strategy choice when designing robots to repair trust. We also discuss the influence of human-to-robot promises and ethical considerations when framing and repairing trust between a human and robot.

There has been a growing expansion in the use of steganography, due to the evolution in using internet technology and multimedia technology. Hence, nowadays, the information is not secured sufficiently while transmitting it over the network. Therefore, information security has taken an important role to provide security against unauthorized individuals. This paper proposes steganography and cryptography technique to secure image based on hybrid edge detector. Cryptography technique is used to encrypt a secret image by using Vernam cipher algorithm. The robust of this algorithm is depending on pseudorandom key. Therefore, pseudo-random key is generated from a nonlinear feedback shift register (Geffe Generator). While in steganography, Hybrid Sobel and Kirch edge detector have been applied on the cover image to locate edge pixels. The least significant bit (LSB) steganography technique is used to embed secret image bits in the cover image in which 3 bits are embedded in edge pixel and 2 bits in smooth pixel. The proposed method can be used in multi field such as military, medical, communication, banking, Electronic governance, and so on. This method gives an average payload ratio of 1.96 with 41.5 PSNR on average. Besides, the maximum size of secret image that can be hidden in the cover image of size 512*512 is 262*261. Also, when hiding 64800 bits in baboon cover image of size 512*512, it gives PSNR of 50.42 and MSE of 0.59.

The Internet of Things (IoT) market is growing rapidly, allowing continuous evolution of new technologies. Alongside this development, most IoT devices are easy to compromise, as security is often not a prioritized characteristic. This paper proposes a novel IoT Security Model (IoTSM) that can be used by organizations to formulate and implement a strategy for developing end-to-end IoT security. IoTSM is grounded by the Software Assurance Maturity Model (SAMM) framework, however it expands it with new security practices and empirical data gathered from IoT practitioners. Moreover, we generalize the model into a conceptual framework. This approach allows the formal analysis for security in general and evaluates an organization's security practices. Overall, our proposed approach can help researchers, practitioners, and IoT organizations, to discourse about IoT security from an end-to-end perspective.

Nowadays with the huge technological development, the reliance on technology has become enormous. Wireless Sensor Networks (WSN) is an example of using the Internet and communication between the patient and the hospital. Easy use of such networks helps to increase the quality of communication between patient and hospital. With the development of technology increased risk in use. Any change in this data between the patient and the hospital may cause false data that may harm the patient. In this paper, a secure protocol is designed to ensure the confidentiality, integrity, and availability of data transfer between the hospital and the patient, depending on the AES and RC4 algorithms.

With the development of big data and machine learning techniques, medical data sharing for the use of disease diagnosis has received considerable attention. Blockchain, as an emerging technology, has been widely used to resolve the efficiency and security issues in medical data sharing. However, the existing studies on blockchain-based medical data sharing have rarely concerned about the reasonable incentive mechanism. In this paper, we propose a cooperation model where medical data is shared via blockchain. We derive the topological relationships among the participants consisting of data owners, miners and third parties, and gradually develop the computational process of Shapley value revenue distribution. Specifically, we explore the revenue distribution under different consensuses of blockchain. Finally, we demonstrate the incentive effect and rationality of the proposed solution by analyzing the revenue distribution.

Reconfigurable Scan Networks (RSNs) are a powerful tool for testing and maintenance of embedded systems, since they allow for flexible access to on-chip instrumentation such as built-in self-test and debug modules. RSNs, however, can be also exploited by malicious users as a side-channel in order to gain information about sensitive data or intellectual property and to recover secret keys. Hence, implementing appropriate counter-measures to secure the access to and data integrity of embedded instrumentation is of high importance. In this paper we present a novel hardware and software combined approach to ensure data privacy in IEEE Std 1687 (IJTAG) RSNs. To do so, both a secure IJTAG compliant plug-and-play instrument wrapper and a versatile software toolchain are introduced. The wrapper demonstrates the necessary architectural adaptations required when using a lightweight stream cipher, whereas the software toolchain provides a seamless integration of the testing workflow with stream cipher. The applicability of the method is demonstrated by an FPGA-based implementation. We report on the performance of the developed instrument wrapper, which is empirically shown to have only a small impact on the workflow in terms of hardware overhead, operational costs and test time overhead.

Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.

Logic locking, and Integrated Circuit (IC) Camouflaging, are techniques that try to hide the design of an IC from a malicious foundry or end-user by introducing ambiguity into the netlist of the circuit. While over the past decade an array of such techniques have been proposed, their security has been constantly challenged by algorithmic attacks. This may in part be due to a lack of formally defined notions of security in the first place, and hence a lack of security guarantees based on long-standing hardness assumptions. In this paper we take a formal approach. We define the problem of circuit locking (cL) as transforming an original circuit to a locked one which is ``unintelligable'' without a secret key (this can model camouflaging and split-manufacturing in addition to logic locking). We define several notions of security for cL under different adversary models. Using long standing results from computational learning theory we show the impossibility of exponentially approximation-resilient locking in the presence of an oracle for large classes of Boolean circuits. We then show how exact-recovery-resiliency and a more relaxed notion of security that we coin ``best-possible'' approximation-resiliency can be provably guaranteed with polynomial overhead. Our theoretical analysis directly results in stronger attacks and defenses which we demonstrate through experimental results on benchmark circuits.

Mixed-criticality scheduling theory (MCSh) was developed to allow for more resource-efficient implementation of systems comprising different components that need to have their correctness validated at different levels of assurance. As originally defined, MCSh deals exclusively with pre-runtime verification of such systems; hence many mixed-criticality scheduling algorithms that have been developed tend to exhibit rather poor survivability characteristics during run-time. (E.g., MCSh allows for less-important (“Lo-criticality”) workloads to be completely discarded in the event that run-time behavior is not compliant with the assumptions under which the correctness of the LO-criticality workload should be verified.) Here we seek to extend MCSh to incorporate survivability considerations, by proposing quantitative metrics for the robustness and resilience of mixed-criticality scheduling algorithms. Such metrics allow us to make quantitative assertions regarding the survivability characteristics of mixed-criticality scheduling algorithms, and to compare different algorithms from the perspective of their survivability. We propose that MCSh seek to develop scheduling algorithms that possess superior survivability characteristics, thereby obtaining algorithms with better survivability properties than current ones (which, since they have been developed within a survivability-agnostic framework, tend to focus exclusively on pre-runtime verification and ignore survivability issues entirely).

Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.

In last decades' web application security has become one of the most important case study of information security studies. Business processes are transferred to web platforms. So web application usage is increased very fast. Web-based attacks have also increased due to the increased use of web applications. In order to ensure the security of web applications, intrusion detection and prevention systems and web application firewalls are used against web based attacks. Blockchain technology, which has become popular in recent years, enables reliable and transparent sharing of data with all stakeholders. In this study, in order to detect web-based attacks, a blockchain based web attack detection model that uses the signature based detection method is proposed. The signature based detection refers to the detection of attacks by looking for specific patterns against known web based attack types, such as Structured Query Language (SQL) Injection, Cross Site Scripting (XSS), Command Injection. Three web servers were used for the experimental study. A blockchain node has been installed with the MultiChain application for each server. Attacks on web applications are detected using the signature list found in the web application as well as detected using the signature list updated on the blockchain. According to the experimental results, the attacks signature detected and defined by a web application are updated in the blockchain lists and used by all web applications.

The article focuses on Personal Data Cyber Security Systems. These systems are the critical components for Health Information Management Systems of Public Health enterprises. The purpose of this article is to inform and provide the reader with Personal Data Cyber Security Legislation and Regulation in Public Health Sector and enlighten him with the Information Systems that were designed and implemented for Personal Data Cyber Security in Public Health.

Knowing malware types in every malware attacks is very helpful to the administrators to have proper defense policies for their system. It must be a massive benefit for the organization as well as the social if the automatic protection systems could themselves detect, classify an existence of new malware types in the whole network system with a few malware samples. This feature helps to prevent the spreading of malware as soon as any damage is caused to the networks. An approach introduced in this paper takes advantage of One-shot/few-shot learning algorithms in solving the malware classification problems by using some well-known models such as Matching Networks, Prototypical Networks. To demonstrate an efficiency of the approach, we run the experiments on the two malware datasets (namely, MalImg and Microsoft Malware Classification Challenge), and both experiments all give us very high accuracies. We confirm that if applying models correctly from the machine learning area could bring excellent performance compared to the other traditional methods, open a new area of malware research.

With the proposal of the national industrial 4.0 strategy, the integration of industrial control network and Internet technology is getting higher and higher. At the same time, the closeness of industrial control networks has been broken to a certain extent, making the problem of industrial control network security increasingly serious. S7 protocol is a private protocol of Siemens Company in Germany, which is widely used in the communication process of industrial control network. In this paper, an industrial control intrusion detection model based on S7 protocol is proposed. Traditional protocol parsing technology cannot resolve private industrial control protocols, so, this model uses deep analysis algorithm to realize the analysis of S7 data packets. At the same time, in order to overcome the complexity and portability of static white list configuration, this model dynamically builds a white list through white list self-learning algorithm. Finally, a composite intrusion detection method combining white list detection and abnormal behavior detection is used to detect anomalies. The experiment proves that the method can effectively detect the abnormal S7 protocol packet in the industrial control network.

In this paper, decentralized dynamic power allocation problem has been investigated for mobile ad hoc network (MANET) at tactical edge. Due to the mobility and self-organizing features in MANET and environmental uncertainties in the battlefield, many existing optimal power allocation algorithms are neither efficient nor practical. Furthermore, the continuously increasing large scale of the wireless connection population in emerging Internet of Battlefield Things (IoBT) introduces additional challenges for optimal power allocation due to the “Curse of Dimensionality”. In order to address these challenges, a novel Actor-Critic-Mass algorithm is proposed by integrating the emerging Mean Field game theory with online reinforcement learning. The proposed approach is able to not only learn the optimal power allocation for IoBT in a decentralized manner, but also effectively handle uncertainties from harsh environment at tactical edge. In the developed scheme, each agent in IoBT has three neural networks (NN), i.e., 1) Critic NN learns the optimal cost function that minimizes the Signal-to-interference-plus-noise ratio (SINR), 2) Actor NN estimates the optimal transmitter power adjustment rate, and 3) Mass NN learns the probability density function of all agents' transmitting power in IoBT. The three NNs are tuned based on the Fokker-Planck-Kolmogorov (FPK) and Hamiltonian-Jacobian-Bellman (HJB) equation given in the Mean Field game theory. An IoBT wireless network has been simulated to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the actor-critic-mass algorithm can effectively approximate the probability distribution of all agents' transmission power and converge to the target SINR. Moreover, the optimal decentralized power allocation is obtained through integrated mean-field game theory with reinforcement learning.

With the deep integration of industrial control systems and Internet technologies, how to effectively detect whether industrial control systems are threatened by intrusion is a difficult problem in industrial security research. Aiming at the difficulty of high dimensionality and non-linearity of industrial control system network data, the stacked auto-encoder is used to extract the network data features, and the multi-classification support vector machine is used for classification. The research results show that the accuracy of the intrusion detection model reaches 95.8%.

The increasing diffusion of malware endowed with steganographic techniques requires to carefully identify and evaluate a new set of threats. The creation of a covert channel to hide a communication within network traffic is one of the most relevant, as it can be used to exfiltrate information or orchestrate attacks. Even if network steganography is becoming a well-studied topic, only few works focus on IPv6 and consider real network scenarios. Therefore, this paper investigates IPv6 covert channels deployed in the wild. Also, it presents a performance evaluation of six different data hiding techniques for IPv6 including their ability to bypass some intrusion detection systems. Lastly, ideas to detect IPv6 covert channels are presented.

In this article the combination of secret sharing schemes and the requirement of discretionary security policy is considered. Secret sharing schemes of Shamir and Blakley are investigated. Conditions for parameters of schemes the providing forbidden information channels are received. Ways for concealment of the forbidden channels are suggested. Three modifications of the Shamir's scheme and two modifications of the Blakley's scheme are suggested. Transition from polynoms to exponential functions for formation the parts of a secret is carried out. The problem of masking the presence of the forbidden information channels is solved. Several approaches with the complete and partial concealment are suggested.

Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decision-making processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.

The paper presents the results of load testing of embedded hardware platforms for Internet of Things solutions. Analyzed the available hardware. The operating systems from different manufacturers were consolidated into a single classification, and for the two most popular, load testing was performed by an external and internal wireless network adapter. Developed its own software solution based on the Python programming language. The number of wireless subscribers ranged from 7 to 14. Experimental results will be useful in deploying wireless infrastructure for small commercial and scientific wireless networks.

Trajectory data in the Internet of Things contains many behavioral information of users, and the method of Mix-zone can be used to separate the association among the user's movement trajectories. In this paper, the weighted undirected graph is used to establish a mathematical model for the Mix-zone, and a user flow-based algorithm is proposed to estimate the probability of migration between nodes in the graph. In response to the attack method basing on the migration probability, the traditional Mix-zone is improved. Finally, an algorithms for adaptively building enhanced Mix-zone is proposed and the simulation using real data sets shows the superiority of the algorithm.

The low attention to security and privacy causes some problems on data and information that can lead to a lack of public trust in e-Gov service. Security threats are not only included in technical issues but also non-technical issues and therefore, it needs the implementation of inclusive security. The application of inclusive security to e-Gov needs to develop a model involving security and privacy requirements as a trusted security solution. The method used is the elicitation of security and privacy requirements in a security perspective. Identification is carried out on security and privacy properties, then security and privacy relationships are determined. The next step is developing the design of an inclusive security model on e-Gov. The last step is doing an analysis of e-Gov service activities and the role of inclusive security. The results of this study identified security and privacy requirements for building inclusive security. Identification of security requirements involves properties such as confidentiality (C), integrity (I), availability (A). Meanwhile, privacy requirement involves authentication (Au), authorization (Az), and Non-repudiation (Nr) properties. Furthermore, an inclusive security design model on e-Gov requires trust of internet (ToI) and trust of government (ToG) as an e-Gov service provider. Access control is needed to provide solutions to e-Gov service activities.

Relevance feedback can be considered as a learning problem. It has been extensively used to improve the performance of retrieval multimedia information. In this paper, after the relevance feedback upon content-based image retrieval (CBIR) discussed, a hybrid learning scheme on multi-target retrieval (MTR) with relevance feedback was proposed. Suppose the symbolic image database (SID) of object-level with combined image metadata and feature model was constructed. During the interactive query for remote sensing image, we calculate the similarity metric so as to get the relevant image sets from the image library. For the purpose of further improvement of the precision of image retrieval, a hybrid learning scheme parameter also need to be chosen. As a result, the idea of our hybrid learning scheme contains an exception maximization algorithm (EMA) used for retrieving the most relevant images from SID and an algorithm called supported vector machine (SVM) with relevance feedback used for learning the feedback information substantially. Experimental results show that our hybrid learning scheme with relevance feedback on MTR can improve the performance and accuracy compared the basic algorithms.

We analyze expert review and student performance data to evaluate the validity of the Cybersecurity Concept Inventory (CCI) for assessing student knowledge of core cybersecurity concepts after a first course on the topic. A panel of 12 experts in cybersecurity reviewed the CCI, and 142 students from six different institutions took the CCI as a pilot test. The panel reviewed each item of the CCI and the overwhelming majority rated every item as measuring appropriate cybersecurity knowledge. We administered the CCI to students taking a first cybersecurity course either online or proctored by the course instructor. We applied classical test theory to evaluate the quality of the CCI. This evaluation showed that the CCI is sufficiently reliable for measuring student knowledge of cybersecurity and that the CCI may be too difficult as a whole. We describe the results of the expert review and the pilot test and provide recommendations for the continued improvement of the CCI.

In view of the problem that the intrusion detection method based on One-Class Support Vector Machine (OCSVM) could not detect the outliers within the industrial data, which results in the decision function deviating from the training sample, an anomaly intrusion detection algorithm based on Robust Incremental Principal Component Analysis (RIPCA) -OCSVM is proposed in this paper. The method uses RIPCA algorithm to remove outliers in industrial data sets and realize dimensionality reduction. In combination with the advantages of OCSVM on the single classification problem, an anomaly detection model is established, and the Improved Particle Swarm Optimization (IPSO) is used for model parameter optimization. The simulation results show that the method can efficiently and accurately identify attacks or abnormal behaviors while meeting the real-time requirements of the industrial control system (ICS).