Biblio

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

International regulations specified in WP.29 and international standards specified in ISO/SAE 21434 require security operations such as cyberattack detection and incident responses to protect vehicles from cyberattacks. To meet these requirements, many vehicle manufacturers are planning to install Intrusion Detection Systems (IDSs) in the Controller Area Network (CAN), which is a primary component of in-vehicle networks, in the coming years. Besides, many vehicle manufacturers and information security companies are developing technologies to identify attack paths related to IDS alerts to respond to cyberattacks appropriately and quickly. To develop the IDSs and the technologies to identify attack paths, it is essential to grasp normal communications performed on in-vehicle networks. Thus, our study aims to develop a technology that can easily grasp normal communications performed on in-vehicle networks. In this paper, we propose the first message source identification method that easily identifies CAN-IDs used by each Electronic Control Unit (ECU) connected to the CAN for message transmissions. We realize the proposed method by utilizing diagnostic communications and an IDS installed in the CAN (CAN-IDS). We evaluate the proposed method using an ECU installed in an actual vehicle and four kinds of simulated CAN-IDSs based on typical existing intrusion detection methods for the CAN. The evaluation results show that the proposed method can identify the CAN-ID used by the ECU for CAN message transmissions if a suitable simulated CAN-IDS for the proposed method is connected to the vehicle.

In this paper, we deal with the resilient consensus problem in networked control systems in which a group of agents are interacting with each other. A min-max-based resilient consensus algorithm has been proposed to help normal agents reach an agreement upon their state values in the presence of misbehaving ones. It is shown that the use of the developed algorithm will result in less computational load and fast convergence. Both synchronous and asynchronous update schemes for the network have been studied. Finally, the effectiveness of the proposed algorithm has been evaluated through numerical examples.

Propagation delay in blockchain networks is a major impairment of message transmission and validation in the bitcoin network. The transaction delay caused by message propagation across long network chains can cause significant threats to the bitcoin network integrity by allowing miners to find blocks during the message consensus process. Potential threats of slow transaction dissemination include double-spending, partitions, and eclipse attacks. In this paper, we propose a method for minimizing propagation delay by reducing non-compulsory message broadcasts during transaction dissemination in the underlying blockchain network. Our method will decrease the propagation delay in the bitcoin network and consequently mitigate the security threats based on message dissemination delay. Our results show improvement in the delay time with more effect on networks with a large number of nodes.

We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. AdaMix reduces the error increase from the non-private upper bound from the 167–311% of the baseline, on average across 6 datasets, to 68-92% depending on the desired privacy level selected by the user. AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis.

The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.

A multi-designated receiver authentication code (MDRA-code) with information-theoretic security is proposed as an extension of the traditional multi-receiver authentication code. The purpose of the MDRA-code is to securely transmit a message via a broadcast channel from a single sender to an arbitrary subset of multiple receivers that have been designated by the sender, and only the receivers in the subset (i.e., not all receivers) should accept the message if an adversary is absent. This paper proposes a model and security formalization of MDRA-codes, and provides constructions of MDRA-codes.

Intelligent service network under the paradigm of the Internet of Things (IoT) uses sensor and network communication technology to realize the interconnection of everything and real-time communication between devices. Under the background of combat, all kinds of sensor devices and equipment units need to be highly networked to realize interconnection and information sharing, which makes the Internet of Things technology hopeful to be applied in the battlefield to interconnect these entities to form the Internet of Battlefield Things (IoBT). This paper analyzes the related concepts of IoBT, and constructs the IoBT multilayer dependency network model according to the typical characteristics and topology of IoBT, then constructs the weighted super-adjacency matrix according to the coupling weights within and between different layers, and the stability model of IoBT is analyzed and derived. Finally, an example of IoBT network is given to provide a reference for analyzing the stability factors of IoBT network.

Multi robot systems are defined as a collection of two or more robots that are capable of working autonomously while coordinating with each other. Three challenges emerge while designing any multi robot system. The robots have to coordinate their path planning or trajectory planning in order to avoid collision during the course of navigation, while collaborating tasks with other robots to achieve a specific end goal for the system. The other challenge, which is the focus of this paper, is the security of the entire multi robot system. Since robots have to coordinate with each other, any one of them being malicious due to any kind of security threat, can lead to a chain reaction that may compromise the entire system. Such security threats can be fatal if not dealt with immediately. This paper proposes the use of a Hybridized Blockchain Model (HBM) to identify such security threats and take necessary actions in real time so that the system does not encounter any catastrophic failure. The proposed security architecture uses ROS (Robot operating system) to decentralize the information collected by robot clients and HBM to monitor the clients and take necessary real time actions.

Internet of Vehicles consists of a three-layer architecture of electric vehicles, charging piles, and a grid dispatch management control center. Therefore, V2G presents multi-level, multi-agent and frequent information interaction, which requires a highly secure and lightweight identity authentication method. Based on the characteristics of Internet of Vehicles, this paper designs a multi-subject information interaction and one-way hash chain authentication method, it includes one-way hash chain and key distribution update strategy. The operation experiment of multiple electric vehicles and charging piles shows that the algorithm proposed in this paper can meet the V2G ID authentication requirements of Internet of Vehicles, and has the advantages of lightweight and low consumption. It is of great significance to improve the security protection level of Internet of Vehicles V2G.

This paper is a conceptual paper that explores how the sensemaking process by intelligence analysts completed within a cognitive immersive environment might be impacted by the inclusion of a progressive dialog system. The tools enabled in the sensemaking room (a specific instance within the cognitive immersive environment) were informed by tools from the intelligence analysis domain. We explore how a progressive dialog system would impact the use of tools such as the collaborative brainstorming exercise [1]. These structured analytic techniques are well established in intelligence analysis training literature, and act as ways to access the intended users' cognitive schema as they use the cognitive immersive room and move through the sensemaking process. A prior user study determined that the sensemaking room encouraged users to be more concise and representative with information while using the digital brainstorming tool. We anticipate that addition of the progressive dialog function will enable a more cohesive link between information foraging and sensemaking behaviors for analysts.

In this study, the nature of human trust in communication robots was experimentally investigated comparing with trusts in other people and artificial intelligence (AI) systems. The results of the experiment showed that trust in robots is basically similar to that in AI systems in a calculation task where a single solution can be obtained and is partly similar to that in other people in an emotion recognition task where multiple interpretations can be acceptable. This study will contribute to designing a smooth interaction between people and communication robots.

We investigate the fuzzy adaptive compensation control problem for nonlinear cyber-physical system with false data injection attack over digital communication links. The fuzzy logic system is first introduced to approximate uncertain nonlinear functions. And the time-varying sliding mode surface is designed. Secondly, for the actual require-ment of data transmission, three uniform quantizers are designed to quantify system state and sliding mode surface and control input signal, respectively. Then, the adaptive fuzzy laws are designed, which can effectively compensate for FDI attack and the quantization errors. Furthermore, the system stability and the reachability of sliding surface are strictly guaranteed by using adaptive fuzzy laws. Finally, we use an example to verify the effectiveness of the method.

The exponential rise of online services has heightened awareness of safeguarding the various applications that cooperate with and provide Internet users. Users must present their credentials, such as user name and secret code, to the servers to be authorized. This sensitive data should be secured from being exploited due to numerous security breaches, resulting in criminal activity. It is vital to secure systems against numerous risks. This article offers a novel approach to protecting against brute force attacks. A solution is presented where the user obtains the keypad on each occurrence. Following the establishment of the keypad, the webserver produces an encrypted password for the user's Computer/device authentication. The encrypted password will be used for authentication; users must type the amended one-time password (OTP) every time they access the website. This research protects passwords using reformation-based encryption and decryption and optimal honey encryption (OH-E) and decryption.

A novel secure physical layer key generation method for Connected and Autonomous Vehicles (CAVs) against an attacker is proposed under fading and Additive White Gaussian Noise (AWGN). In the proposed method, a random sequence key is added to the demodulated sequence to generate a unique pre-shared key (PSK) to enhance security. Extensive computer simulation results proved that an attacker cannot extract the same legitimate PSK generated by the received vehicle even if identical fading and AWGN parameters are used both for the legitimate vehicle and attacker.

This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm returns an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a modelling approach for power networks subject to D-LAAs, suitable for the designed robust control protection scheme. The paper also proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases. The approach is evaluated by numerical simulations on large networks.

This article provides an overview of the security of VANET, which is a vehicle network. When reviewing this topic, publications of various researchers were considered. The article provides information security requirements for VANET, an overview of security research, an overview of existing attacks, methods for detecting attacks and appropriate countermeasures against such threats.

Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

This paper presents a physically-secure wireless communication system utilizing orbital angular momentum (OAM) waves at 0.31THz. A trustworthy key distribution mechanism for symmetric key cryptography is proposed by exploiting random hopping among the orthogonal OAM-wave modes and phases. Keccak-f[400] based pseudorandom number generator provides randomness to phase distribution of OAM-wave modes for additional security. We assess the security vulnerabilities of using OAM modulation in a THz communication system under various physical-layer threat models as well as analyze the effectiveness of these threat models for varying attacker complexity levels under different conditions.

Trust is a cognitive ability that can be dependent on behavioral consistency. In this paper, a partially observable Markov Decision Process (POMDP)-based computational robot-human trust model is proposed for hand-over tasks in human-robot collaborative contexts. The robot's trust in its human partner is evaluated based on the human behavior estimates and object detection during the hand-over task. The human-robot hand-over process is parameterized as a partially observable Markov Decision Process. The proposed approach is verified in real-world human-robot collaborative tasks. Results show that our approach can be successfully applied to human-robot hand-over tasks to achieve high efficiency, reduce redundant robot movements, and realize predictability and mutual understanding of the task.

Cloud computing solutions enable Cyber-Physical Systems (CPSs) to utilize significant computational resources and implement sophisticated control algorithms even if limited computation capabilities are locally available for these systems. However, such a control architecture suffers from an important concern related to the privacy of sensor measurements and the computed control inputs within the cloud. This paper proposes a solution that allows implementing a set-theoretic model predictive controller on the cloud while preserving this privacy. This is achieved by exploiting the offline computations of the robust one-step controllable sets used by the controller and two affine transformations of the sensor measurements and control optimization problem. It is shown that the transformed and original control problems are equivalent (i.e., the optimal control input can be recovered from the transformed one) and that privacy is preserved if the control algorithm is executed on the cloud. Moreover, we show how the actuator can take advantage of the set-theoretic nature of the controller to verify, through simple set-membership tests, if the control input received from the cloud is admissible. The correctness of the proposed solution is verified by means of a simulation experiment involving a dual-tank water system.

With 3.78 billion social media users worldwide in 2021 (48% of the human population), almost 3 billion images are shared daily. At the same time, a consistent evolution of smartphone cameras has led to a photography explosion with 85% of all new pictures being captured using smartphones. However, lately, there has been an increased discussion of privacy concerns when a person being photographed is unaware of the picture being taken or has reservations about the same being shared. These privacy violations are amplified for people with disabilities, who may find it challenging to raise dissent even if they are aware. Such unauthorized image captures may also be misused to gain sympathy by third-party organizations, leading to a privacy breach. Privacy for people with disabilities has so far received comparatively less attention from the AI community. This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A real time Privacy-Preserving AI System) a novel framework to identify sensitive content. Additionally, we curate and annotate a dataset to identify and localize accessibility markers and classify whether an image is sensitive to a featured subject with a disability. We demonstrate that the proposed lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline, trained on face anonymized data. achieves an F1-score of 73.1%.

With the advent of information and communication technology, the digital space is becoming a playing ground for criminal activities. Criminals typically prefer darkness or a hidden place to perform their illegal activities in a real-world while sometimes covering their face to avoid being exposed and getting caught. The same applies in a digital world where criminals prefer features which provide anonymity or hidden features to perform illegal activities. It is from this spirit the Darkweb is attracting all kinds of criminal activities conducted over the Internet such as selling drugs, illegal weapons, child pornography, assassination for hire, hackers for hire, and selling of malicious exploits, to mention a few. Although the anonymity offered by Darkweb can be exploited as a tool to arrest criminals involved in cybercrime, an in-depth research is needed to advance criminal investigation on Darkweb. Analysis of illegal activities conducted in Darkweb is in its infancy and faces several challenges like lack of standard operating procedures. This study proposes progressive standard operating procedures (SOPs) for Darkweb forensics investigation. We provide the four stages of SOP for Darkweb investigation. The proposed SOP consists of the following stages; identification and profiling, discovery, acquisition and preservation, and the last stage is analysis and reporting. In each stage, we consider the objectives, tools and expected results of that particular stage. Careful consideration of this SOP revealed promising results in the Darkweb investigation.

Sometimes we have the need to inject new services in an operational satellite, but as the injection of new codes in equipment that has communication link is a critical process due to the possibility of injection of broke or malicious codes, this document proposes a protocol for the safe injection of code in satellite microcontrollers of the CubeSat’ type. This protocol is based on the use of HMAC with SHA-3 to guarantee integrity and authenticity and is enhanced by the same security measures to mitigate communication link problems and satellite attacks, such as the guarantee of delivery and displacement between communication windows and periods of high processing.

We use mobile apps on a daily basis and there is an app for everything. We trust these applications with our most personal data. It is therefore important that these apps are as secure and well usable as possible. So far most studies on the maintenance and security of mobile applications have been done on Android applications. We do, however, not know how well these results translate to iOS.This research project aims to close this gap by analysing iOS applications with regards to maintainability and security. Regarding maintainability, we analyse code smells in iOS applications, the evolution of code smells in iOS applications and compare code smell distributions in iOS and Android applications. Regarding security, we analyse the evolution of the third-party library dependency network for the iOS ecosystem. Additionally, we analyse how publicly reported vulnerabilities spread in the library dependency network.Regarding maintainability, we found that the distributions of code smells in iOS and Android applications differ. Code smells in iOS applications tend to correspond to smaller classes, such as Lazy Class. Regarding security, we found that the library dependency network of the iOS ecosystem is not growing as fast as in some other ecosystems. There are less dependencies on average than for example in the npm ecosystem and, therefore, vulnerabilities do not spread as far.