Biblio

In human-robot collaboration (HRC), human trust in the robot is the human expectation that a robot executes tasks with desired performance. A higher-level trust increases the willingness of a human operator to assign tasks, share plans, and reduce the interruption during robot executions, thereby facilitating human-robot integration both physically and mentally. However, due to real-world disturbances, robots inevitably make mistakes, decreasing human trust and further influencing collaboration. Trust is fragile and trust loss is triggered easily when robots show incapability of task executions, making the trust maintenance challenging. To maintain human trust, in this research, a trust repair framework is developed based on a human-to-robot attention transfer (H2R-AT) model and a user trust study. The rationale of this framework is that a prompt mistake correction restores human trust. With H2R-AT, a robot localizes human verbal concerns and makes prompt mistake corrections to avoid task failures in an early stage and to finally improve human trust. User trust study measures trust status before and after the behavior corrections to quantify the trust loss. Robot experiments were designed to cover four typical mistakes, wrong action, wrong region, wrong pose, and wrong spatial relation, validated the accuracy of H2R-AT in robot behavior corrections; a user trust study with 252 participants was conducted, and the changes in trust levels before and after corrections were evaluated. The effectiveness of the human trust repairing was evaluated by the mistake correction accuracy and the trust improvement.

Trust is an important emerging area of study in human-robot cooperation. Many studies have begun to look at the issue of robot (agent) capability as a predictor of human trust in the robot. However, the assumption that agent capability is the sole predictor of human trust could underestimate the complexity of the problem. This study aims to investigate the effects of agent-strategy and agent-capability in a visual search task. Fourteen subjects were recruited to partake in a web-based grid search task. They were each paired with a series of autonomous agents to search an on-screen grid to find a number of outlier objects as quickly as possible. Both the human and agent searched the grid concurrently and the human was able to see the movement of the agent. Each trial, a different autonomous agent with its assigned capability, used one of three search strategies to assist their human counterpart. After each trial, the autonomous agent reported the number of outliers it found, and the human subject was asked to determine the total number of outliers in the area. Some autonomous agents reported only a fraction of the outliers they encountered, thus coding a varying level of agent capability. Human subjects then evaluated statements related to the behavior, reliability, and trust of the agent. The results showed increased measures of trust and reliability with increasing capability. Additionally, the most legible search strategies received the highest average ratings in a measure of familiarity. Remarkably, given no prior information about capabilities or strategies that they would see, subjects were able to determine consistent trustworthiness of the agent. Furthermore, both capability and strategy of the agent had statistically significant effects on the human’s trust in the agent.

In this paper, we propose a novel protocol to represent the human factor on a blockchain environment. Our approach allows single or groups of humans to propose data in blocks which cannot be validated automatically but need human knowledge and collaboration to be validated. Only if human-based consensus on the correctness and trustworthiness of the data is reached, the new block is appended to the blockchain. This human approach significantly extends the possibilities of blockchain applications on data types apart from financial transaction data.

In human-machine systems (HMS), trust placed by humans on machines is a complex concept and attracts increasingly research efforts. Herein, we reviewed recent studies on building and measuring trust in HMS. The review was based on one comprehensive model of trust – IMPACTS, which has 7 features of intention, measurability, performance, adaptivity, communication, transparency, and security. The review found that, in the past 5 years, HMS fulfill the features of intention, measurability, communication, and transparency. Most of the HMS consider the feature of performance. However, all of the HMS address rarely the feature of adaptivity and neglect the feature of security due to using stand-alone simulations. These findings indicate that future work considering the features of adaptivity and/or security is imperative to foster human trust in HMS.

Trust in autonomous teammates has been shown to be a key factor in human-autonomy team (HAT) performance, and anthropomorphism is a closely related construct that is underexplored in HAT literature. This study investigates whether perceived anthropomorphism can be measured from team communication behaviors in a simulated remotely piloted aircraft system task environment, in which two humans in unique roles were asked to team with a synthetic (i.e., autonomous) pilot agent. We compared verbal and self-reported measures of anthropomorphism with team error handling performance and trust in the synthetic pilot. Results for this study show that trends in verbal anthropomorphism follow the same patterns expected from self-reported measures of anthropomorphism, with respect to fluctuations in trust resulting from autonomy failures.

The advancement of AI enables the evolution of machines from relatively simple automation to completely autonomous systems that augment human capabilities with improved quality and productivity in work and life. The singularity is near! However, humans are still vulnerable. The COVID-19 pandemic reminds us of our limited knowledge about nature. The recent accidents involving Boeing 737 Max passengers ring the alarm again about the potential risks when using human-autonomy symbiosis technologies. A key challenge of safe and effective human-autonomy teaming is enabling “trust” between the human-machine team. It is even more challenging when we are facing insufficient data, incomplete information, indeterministic conditions, and inexhaustive solutions for uncertain actions. This calls for the imperative needs of appropriate design guidance and scientific methodologies for developing safety-critical autonomous systems and AI functions. The question is how to build and maintain a safe, effective, and trusted partnership between humans and autonomous systems. This talk discusses a context-based and interaction-centred design (ICD) approach for developing a safe and collaborative partnership between humans and technology by optimizing the interaction between human intelligence and AI. An associated trust model IMPACTS (Intention, Measurability, Performance, Adaptivity, Communications, Transparency, and Security) will also be introduced to enable the practitioners to foster an assured and calibrated trust relationship between humans and their partner autonomous systems. A real-world example of human-autonomy teaming in a military context will be explained to illustrate the utility and effectiveness of these trust enablers.

In recent years, blockchain-based cryptocurren-cies have attracted much attention. Attacks targeting cryptocurrencies and related services directly profit an attacker if successful. Related studies have reported attacks targeting configuration-vulnerable nodes in Ethereum using a method called honeypots to observe malicious user attacks. They have analyzed 380 million observed requests and showed that attacks had to that point taken at least 4193 Ether. However, long-term observations using honeypots are difficult because the cost of maintaining honeypots is high. In this study, we analyze the behavior of malicious users using our honeypot system. More precisely, we clarify the pre-investigation that a malicious user performs before attacks. We show that the cost of maintaining a honeypot can be reduced. For example, honeypots need to belong in Ethereum's P2P network but not to the mainnet. Further, if they belong to the testnet, the cost of storage space can be reduced.

IoT honeypots that mimic the behavior of IoT devices for threat analysis are becoming increasingly important. Existing honeypot systems use devices with a specific version of firmware installed to monitor cyber attacks. However, honeypots frequently receive requests targeting devices and firmware that are different from themselves. When honeypots return an error response to such a request, the attack is terminated, and the monitoring fails.To solve this problem, we introduce FirmPot, a framework that automatically generates intelligent-interaction honeypots using firmware. This framework has a firmware emulator optimized for honeypot generation and learns the behavior of embedded applications by using machine learning. The generated honeypots continue to interact with attackers by a mechanism that returns the best from the emulated responses to the attack request instead of an error response.We experimented on embedded web applications of wireless routers based on the open-source OpenWrt. As a result, our framework generated honeypots that mimicked the embedded web applications of eight vendors and ten different CPU architectures. Furthermore, our approach to the interaction improved the session length with attackers compared to existing ones.

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.

In order to solve the problem that the ordinary intrusion detection model cannot effectively identify the increasingly complex, continuous, multi-source and organized network attacks, this paper proposes an Internet of Things attack group identification model to identify the planned and organized attack groups. The model takes the common attack source IP, target IP, time stamp and target port as the characteristics of the attack log data to establish the identification benchmark of the attack gang behavior. The model also combines the spectral clustering algorithm to cluster different attackers with similar attack behaviors, and carries out the specific image analysis of the attack gang. In this paper, an experimental detection was carried out based on real IoT honey pot attack log data. The spectral clustering was compared with Kmeans, DBSCAN and other clustering algorithms. The experimental results shows that the contour coefficient of spectral clustering was significantly higher than that of other clustering algorithms. The recognition model based on spectral clustering proposed in this paper has a better effect, which can effectively identify the attack groups and mine the attack preferences of the groups.

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.

We present an algorithm for generating perfect tabulation hashing functions by reduction to Boolean satisfaction (SAT). Tabulation hashing is a high-performance family of hash functions for hash tables that involves computing the XOR of random lookup tables. Given n keys of word size W, we show how to compute a perfect hash function in O(nW) worst-case time. This is competitive with other perfect hashing methods, and the resultant hash functions are simple and performant.

Cyber deception plays an important role in both proactive and reactive defense systems. Internet of Battlefield things connecting smart devices of any military tactical network is of great importance. The goal of cyber deception is to provide false information regarding the network state, and topology to protect the IoBT's network devices. In this paper, we propose a novel deceptive approach based on game theory that takes into account the topological aspects of the network and the criticality of each device. To find the optimal deceptive strategy, we formulate a two-player game to study the interactions between the network defender and the adversary. The Nash equilibrium of the game model is characterized. Moreover, we propose a scalable game-solving algorithm to overcome the curse of dimensionality. This approach is based on solving a smaller in-size subgame per node. Our numerical results show that the proposed deception approach effectively reduced the impact and the reward of the attacker

An experimental network environment plays an important role to examine new systems and protocols. We have developed an experimental network construction tool called LiONv1 (Lightweight On-Demand Networking, ver.1). LiONv1 satisfies the following four requirements: programmer-friendly configuration file based on Infrastructure as Code, multiple virtualization technologies for virtual nodes, physical topology conscious virtual node placement, and L3 protocol agnostic virtual networks. None of existing experimental network environments satisfy all the four requirements. In this paper, we develop LiONv2 which satisfies three more requirements: diversity of available network devices, Internet-scale deployment, and disaggregation of network configuration and device configuration. LiONv2 employs NETCONF and YANG to achieve diversity of available network devices and Internet-scale deployment. LiONv2 also defines two YANG models which disaggregate network configuration and device configuration. LiONv2 is implemented in Go and C languages with public libraries for Go. Measurement results show that construction time of a virtual network is irrelevant to the number of virtual nodes if a single virtual node is created per physical node.

Intrusion Detection System (IDS) is a system that could detect suspicious activity in a network. Two approaches are known for IDS, namely signature-based and anomaly-based. The anomaly-based detection method was chosen to detect suspicious and abnormal activity for the system that cannot be performed by the signature-based method. In this study, attack testing was carried out using three DoS tools, namely the LOIC, Torshammer, and Xerxes tools, with a test scenario using IDS and without IDS. From the test results that have been carried out, IDS has successfully detected the attacks that were sent, for the delivery of the most consecutive attack packages, namely Torshammer, Xerxes, and LOIC. In the detection of Torshammer attack tools on the target FTP Server, 9421 packages were obtained, for Xerxes tools as many as 10618 packages and LOIC tools as many as 6115 packages. Meanwhile, attacks on the target Web Server for Torshammer tools were 299 packages, for Xerxes tools as many as 530 packages, and for LOIC tools as many as 103 packages. The accuracy of the IDS performance results is 88.66%, the precision is 88.58% and the false positive rate is 63.17%.

Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property. Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.

Insider threat makes enterprises or organizations suffer from the loss of property and the negative influence of reputation. User behavior analysis is the mainstream method of insider threat detection, but due to the lack of fine-grained detection and the inability to effectively capture the behavior patterns of individual users, the accuracy and precision of detection are insufficient. To solve this problem, this paper designs an insider threat detection method based on user historical behavior and attention mechanism, including using Long Short Term Memory (LSTM) to extract user behavior sequence information, using Attention-based on user history behavior (ABUHB) learns the differences between different user behaviors, uses Bidirectional-LSTM (Bi-LSTM) to learn the evolution of different user behavior patterns, and finally realizes fine-grained user abnormal behavior detection. To evaluate the effectiveness of this method, experiments are conducted on the CMU-CERT Insider Threat Dataset. The experimental results show that the effectiveness of this method is 3.1% to 6.3% higher than that of other comparative model methods, and it can detect insider threats in different user behaviors with fine granularity.

Fulcrum decoders can trade off between computational complexity and the number of received packets. This allows heterogeneous nodes to decode at different level of complexity in accordance with their computing power. Variations of Fulcrum codes, like dynamic sparsity and expansion packets (DSEP) have significantly reduced the encoders and decoders' complexity by using dynamic sparsity and expansion packets. However, limited effort had been done for recoders of Fulcrum codes and their variations, limiting their full potential when being deployed at multi-hop networks. In this paper, we investigate the drawback of the conventional Fulcrum recoding and introduce a novel recoding scheme for the family of Fulcrum codes by limiting the buffer size, and thus memory needs. Our evaluations indicate that DSEP recoding mechamism increases the recoding goodput by 50%, and reduces the decoding overhead by 60%-90% while maintaining high decoding goodput at receivers and small memory usage at recoders compared with the conventional Fulcrum recoding. This further reduces the resources needed for Fulcrum codes at the recoders.

Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts’ experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.

Cyber threat information can be utilized to investigate incidents by leveraging threat-related knowledge from prior incidents with digital forensic techniques and tools. However, the actionability of cyber threat information in digital forensics has not yet been evaluated. Such evaluation is important to ascertain that cyber threat information is as actionable as it can be and to reveal areas of improvement. In this study, a dataset of cyber threat information products was created from well-known cyber threat information sources and its actionability in digital forensics was evaluated. The evaluation results showed a high level of cyber threat information actionability that still needs enhancements in supporting some widely present types of attacks. To further enhance the provision of actionable cyber threat information, the development of the new TREVItoSTIX Autopsy module is presented. TREVItoSTIX allows the expression of the findings of an incident investigation in the structured threat information expression format in order to be easily shared and reused in future digital forensics investigations.

Both the Internet of Things (IoT) and Information Centric Networking (ICN) have gathered a lot of attention from both research and industry in recent years. While ICN has proved to be beneficial in many situations, it is not widely deployed outside research projects, also not addressing needs of IoT application programming interfaces (APIs). On the other hand, today's IoT solutions are built on top of the host-centric communication model associated with the usage of the Internet Protocol (IP). This paper contributes a discussion on the need of an integration of a specific form of IoT APIs, namely WebSocket based streaming APIs, into an ICN. Furthermore, different access models are discussed and requirements are derived from real world APIs. Finally, the design of an ICN-style extension is presented using one of the examined APIs.

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.

Network embedding, which aims to map the discrete network topology to a continuous low-dimensional representation space with the major topological properties preserved, has emerged as an essential technique to support various network inference tasks. However, incorporating both the evolutionary nature and the network's heterogeneity remains a challenge for existing network embedding methods. In this study, we propose a novel Translation-Based Dynamic Heterogeneous Network Embedding (TransDHE) approach to consider both the aspects simultaneously. For a dynamic heterogeneous network with a sequence of snapshots and multiple types of nodes and edges, we introduce a translation-based embedding module to capture the heterogeneous characteristics (e.g., type information) of each single snapshot. An orthogonal alignment module and RNN-based aggregation module are then applied to explore the evolutionary patterns among multiple successive snapshots for the final representation learning. Extensive experiments on a set of real-world networks demonstrate that TransDHE can derive the more informative embedding result for the network dynamic and heterogeneity over state-of-the-art network embedding baselines.

Most present reconfiguration methods in sharding blockchains rely on a secure randomness, whose generation might be complicated. Besides, a reference committee is usually in charge of the reconfiguration, making the process not decentralized. To address the above issues, this paper proposes a secure and decentralized shard reconfiguration protocol, which allows each shard to complete the selection and confirmation of its own shard members in turn. The PoW mining puzzle is calculated using the public key hash value in the member list confirmed by the last shard. Through the mining and shard member list commitment process, each shard can update its members safely and efficiently once in a while. Furthermore, it is proved that our protocol satisfies the safety, consistency, liveness, and decentralization properties. The honest member proportion in each confirmed shard member list is guaranteed to exceed a certain safety threshold, and all honest nodes have an identical view on the list. The reconfiguration is ensured to make progress, and each node has the same right to participate in the process. Our secure and decentralized shard reconfiguration protocol could be applied to all committee-based sharding blockchains.

This work presents a system which allows image data and extracted features from a real-world location to be captured and modelled in a Virtual Reality (VR) environment combined with Augmented Situated Visualizations (ASV) overlaid and registered in a virtual environment. Combining these technologies with techniques from Data Science and Artificial Intelligence (AI)(such as image analysis and 3D reconstruction) allows the creation of a setting where remote locations can be modelled and interacted with from anywhere in the world. This Enhanced Photorealistic Immersive (EPI) system is highly adaptable to a wide range of use cases and users as it can be utilized to model and interact with any environment which can be captured as image data (such as training for operation in hazardous environments, accessibility solutions for exploration of historical/tourism locations and collaborative learning environments). A use case example focused on a structural examination of railway tunnels along with a pilot study is presented, which can demonstrate the usefulness of the EPI system.