Biblio

Virtual machine (VM) based application sandboxes leverage strong isolation guarantees of virtualization techniques to address several security issues through effective containment of malware. Specifically, in end-user physical hosts, potentially vulnerable applications can be isolated from each other (and the host) using VM based sandboxes. However, sharing data across applications executing within different sandboxes is a non-trivial requirement for end-user systems because at the end of the day, all applications are used by the end-user owning the device. Existing file sharing techniques compromise the security or efficiency, especially considering lack of technical expertise of many end-users in the contemporary times. In this paper, we propose MicroBlind, a security hardened file sharing framework for virtualized sandboxes to support efficient data sharing across different application sandboxes. MicroBlind enables a simple file sharing management API for end users where the end user can orchestrate file sharing across different VM sandboxes in a secure manner. To demonstrate the efficacy of MicroBlind, we perform comprehensive empirical analysis against existing data sharing techniques (augmented for the sandboxing setup) and show that MicroBlind provides improved security and efficiency.

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.

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.

State-of-the-art approaches in gait analysis usually rely on one isolated tracking system, generating insufficient data for complex use cases such as sports, rehabilitation, and MedTech. We address the opportunity to comprehensively understand human motion by a novel data model combining several motion-tracking methods. The model aggregates pose estimation by captured videos and EMG and EIT sensor data synchronously to gain insights into muscle activities. Our demonstration with biceps curl and sitting/standing pose generates time-synchronous data and delivers insights into our experiment’s usability, advantages, and challenges.

Connected devices are being deployed at a steady rate, providing services like data collection. Pervasive applications rely on those edge devices to seamlessly provide services to users. To connect applications and edge devices, using a middleware has been a popular approach. The research is active on the subject as there are many open challenges. The secure management of the edge devices and the security of the middleware are two of them. As security is a crucial requirement for pervasive environment, we propose a middleware architecture easing the secure use of edge devices for pervasive applications, while supporting the heterogeneity of communication protocols and the dynamism of devices. Because of the heterogeneity in protocols and security features, not all edge devices are equally secure. To allow the pervasive applications to gain control over this heterogeneous security, we propose a model to describe edge devices security. This model is accessible by the applications through our middleware. To validate our work, we developed a demonstrator of our middleware and we tested it in a concrete scenario.

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.

Data or information are being transferred at an enormous pace and hence protecting and securing this transmission of data are very important and have been very challenging. Cryptography and Steganography are the most broadly used techniques for safeguarding data by encryption of data and hiding the existence of data. A multi-layered secure transmission can be achieved by combining Cryptography with Steganography and by adding message authentication ensuring the confidentiality of the message. Different approach towards Steganography implementation is proposed using rotations and flips to prevent detection of encoded messages. Compression of multimedia files is set up for increasing the speed of encoding and consuming less storage space. The HMAC (Hash-based Authentication Code) algorithm is chosen for message authentication and integrity. The performance of the proposed Steganography methods is concluded using Histogram comparative analysis. Simulations have been performed to back the reliability of the proposed method.

This paper conducts simulation analysis on power transmission lines and availability of power communication link based on Latin hypercube sampling. It proposes a new method of vulnerability communication link assessment for electric cyber physical system. Wind power output, transmission line failure and communication link failure of electric cyber physical system are sampled to obtain different operating states of electric cyber physical system. The connectivity of communication links under different operating states of electric cyber physical system is calculated to judge whether the communication nodes of the links are connected with the control master station. According to the connection between the link communication node and the control master station, the switching load and switching load of the electric cyber physical system in different operating states are calculated, and the optimal switching load of the electric cyber physical system in different operating states is obtained. This method can clearly identify the vulnerable link in the electric cyber physical system, so as to monitor the vulnerable link and strengthen the link strength.

A new type of time modulated metamaterial (MTM) antenna array transmitter capable of realizing 2D directional modulation (DM) for physical layer (PHY) security is presented in this work. The proposed 2D DM MTM antenna array is formed by a time modulated corporate feed network loaded with composite right/left-handed (CRLH) leaky wave antennas (LWAs). By properly designing the on-off states of the switch for each antenna feeding branch as well as harnessing the frequency scanning characteristics of CRLH L WAs, 2D DM can be realized to form a PHY secured transmission link in the 2D space. Experimental results demonstrate the bit-error-rate (BER) is low only at a specific 2D angle for the orthogonal frequency-division multiplexing (OFDM) wireless data links.

Wireless security and privacy is gaining a significant interest due to the burgeoning growth of communication devices across the electromagnetic spectrum. In this article, we introduce the concept of the space-time modulated millimeter-wave wireless links enabling physical layer security in highspeed communication links. Such an approach does not require cryptographic key exchanges and enables security in a seamless fashion with no overhead on latency. We show both the design and implementation of such a secure system using custom integrated chips at 71-76 GHz with off-chip packaged antenna array. We also demonstrate the security metric of such a system and analyze the efficacy through distributed eavesdropper attack.

Micro grid is a small-scale power supply network designed to provide electricity to small community with integrated renewable energy sources. A micro grid can be integrated to the utility grid. Due to lack of computerized analysis, mechanical switches causing slow response time, poor visibility and situational awareness blackouts are caused due to cascading of faults. This paper presents a brief survey on communication technologies used in smart grid and its extension to micro grid. By integration of communication network, device control, information collection and remote management an intelligent power management system can be achieved

Aiming at the prevention of information security risk in protection and control of smart substation, a multi-level security defense method of substation based on data aggregation and convolution neural network (CNN) is proposed. Firstly, the intelligent electronic device(IED) uses "digital certificate + digital signature" for the first level of identity authentication, and uses UKey identification code for the second level of physical identity authentication; Secondly, the device group of the monitoring layer judges whether the data report is tampered during transmission according to the registration stage and its own ID information, and the device group aggregates the data using the credential information; Finally, the convolution decomposition technology and depth separable technology are combined, and the time factor is introduced to control the degree of data fusion and the number of input channels of the network, so that the network model can learn the original data and fused data at the same time. Simulation results show that the proposed method can effectively save communication overhead, ensure the reliable transmission of messages under normal and abnormal operation, and effectively improve the security defense ability of smart substation.

This paper presents a MATLAB Graphical User Interface (GUI) based tool that determines the performance evaluation metrics of the physically unclonable functions (PUFs). The PUFs are hardware security primitives which can be utilized in several hardware security applications like integrated circuits protection, device authentication, secret key generation, and hardware obfuscation. Like any other technology approach, PUFs evaluation requires testing different performance metrics, each of which can be determined by at least one mathematical equation. The proposed tool (PUFs Tool) reads the PUF instances’ output and then computes and generates the values of the main PUFs’ performance metrics: uniqueness, reliability, uniformity, and bit-aliasing. In addition, it generates a bar code for each PUF instance considered in the evaluation process. The PUFs Tool is designed and developed using the app designer of MATLAB software 2021b.

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.

Due to the increasing complexity of modern hetero-geneous System-on-Chips (SoC) and the growing vulnerabilities, security risk assessment and quantification is required to measure the trustworthiness of a SoC. This paper describes a systematic approach to model the security risk of a system for malicious hardware attacks. The proposed method uses graph analysis to assess the impact of an attack and the Common Vulnerability Scoring System (CVSS) is used to quantify the security level of the system. To demonstrate the applicability of the proposed metric, we consider two open source SoC benchmarks with different architectures. The overall risk is calculated using the proposed metric by computing the exploitability and impact of attack on critical components of a SoC.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.

While vehicle-to-everything communication technology enables information sharing and cooperative control for vehicles, it also poses a significant threat to the vehicles' driving security owing to cyber-attacks. In particular, Sybil malicious attacks hidden in the vehicle broadcast information flow are challenging to detect, thereby becoming an urgent issue requiring attention. Several researchers have considered this problem and proposed different detection schemes. However, the detection performance of existing schemes based on plausibility checks and neighboring observers is affected by the traffic and attacker densities. In this study, we propose a malicious attack detection scheme based on traffic-flow information fusion, which enables the detection of Sybil attacks without neighboring observer nodes. Our solution is based on the basic safety message, which is broadcast by vehicles periodically. It first constructs the basic features of traffic flow to reflect the traffic state, subsequently fuses it with the road detector information to add the road fusion features, and then classifies them using machine learning algorithms to identify malicious attacks. The experimental results demonstrate that our scheme achieves the detection of Sybil attacks with an accuracy greater than 90 % at different traffic and attacker densities. Our solutions provide security for achieving a usable vehicle communication network.

As a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.

The expanding streaming culture of large amounts of data, as well as the requirement for faster and more reliable data transport systems, necessitates the development of innovative communication technologies such as Visible Light Communication (VLC). Nonetheless, incorporating VLC into next-generation networks is challenging due to technological restrictions such as air absorption, shadowing, and beam dispersion. One technique for addressing some of the challenges is to use the multiple input multiple output (MIMO) technique, which involves the simultaneous transmission of data from several sources, hence increasing data rate. In this work, the data transmission performance of the MIMO-VLC system is evaluated using a variety of factors such as distance from the source, data bit rate, and modulation method.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space.

MPLS has been in the forefront of high-speed Wide Area Networks (WANs), for almost two decades [1], [12]. The performance advantages in implementing Multi-Protocol Label Switching (MPLS) are mainly its superior speed based on fast label switching and its capability to perform Fast Reroute rapidly when failure(s) occur - in theory under 50 ms [16], [17], which makes MPLS also interesting for real-time applications. We investigate the aforementioned advantages of MPLS by creating two real testbeds using actual routers that commercial Internet Service Providers (ISPs) use, one with a ring and one with a partial mesh architecture. In those two testbeds we compare the performance of MPLS channels versus normal routing, both using the Open Shortest Path First (OSPF) routing protocol. The speed of the Fast Reroute mechanism for MPLS when failures are occurring is investigated. Firstly, baseline experiments are performed consisting of MPLS versus normal routing. Results are evaluated and compared using both single and dual failure scenarios within the two architectures. Our results confirm recovery times within 50 ms.

Physically Unclonable Functions (PUFs) are hardware-based security primitives that utilize manufacturing process variations to realize binary keys (Weak PUFs) or binary functions (Strong PUFs). This primitive is desirable for key generation and authentication in constrained devices, due to its low power and low area overhead. However, in recent years many research papers are focused on the vulnerability of PUFs to modeling attacks. This attack is possible because the PUFs challenge and response exchanges are usually transmitted over communication channel without encryption. Thus, an attacker can collect challenge-response pairs and use it as input into a learning algorithm, to create a model that can predict responses given new challenges. In this paper we introduce a serial and a parallel novel 64-bits memory-based controlled PUF (Mc-PUF) architecture for device authentication that has high uniqueness and randomness, reliable, and resilient against modeling attacks. These architectures generate a response by utilizing bits extracted from the fingerprint of a synchronous random-access memory (SRAM) PUF with a control logic. The synthesis of the serial architecture yielded an area of 1.136K GE, while the parallel architecture was 3.013K GE. The best prediction accuracy obtained from the modeling attack was 50%, which prevents an attacker from accurately predicting responses to future challenges. We also showcase the scalability of the design through XOR-ing several Mc-PUFs, further improving upon its security and performance. The remainder of the paper presents the proposed architectures, along with their hardware implementations, area and power consumption, and security resilience against modeling attacks. The 3-XOR Mc-PUF had the greatest overhead, but it produced the best randomness, uniqueness, and resilience against modeling attacks.

The industrial Internet has built a new industrial manufacturing and service system with all elements, all industrial chains and all value chains connected through the interconnection of people, machines and things. It breaks the relatively closed and credible production environment of traditional industry. But at the same time, the full interconnection of cross-device, cross-system, and cross-region in the industrial Internet also brings a certain network trust crisis. The method proposed in this paper breaking the relatively closed manufacturing environment of traditional industries, extends the network connection object from human to machine equipment, industrial products and industrial services. It provides a safe and credible environment for the development of industrial Internet, and a trust guarantee for the across enterprises entities and data sharing.

Objective measures are ubiquitous in the formulation, design and implementation of deep space missions. Tour durations, flyby altitudes, propellant budgets, power consumption, and other metrics are essential to developing and managing NASA missions. But beyond the simple metrics of cost and workforce, it has been difficult to identify objective, quantitative measures that assist in evaluating choices made during formulation or implementation phases in terms of their impact on flight operations. As part of the development of the Europa Clipper Mission system, a set of operations metrics have been defined along with the necessary design information and software tooling to calculate them. We have applied these methods and metrics to help assess the impact to the flight team on the six options for the Clipper Tour that are currently being vetted for selection in the fall of 2021. To generate these metrics, the Clipper MOS team first designed the set of essential processes by which flight operations will be conducted, using a standard approach and template to identify (among other aspects) timelines for each process, along with their time constraints (e.g., uplinks for sequence execution). Each of the resulting 50 processes is documented in a common format and concurred by stakeholders. Process timelines were converted into generic schedules and workforce-loaded using COTS scheduling software, based on the inputs of the process authors and domain experts. Custom code was generated to create an operations schedule for a specific portion of Clipper's prime mission, with instances of a given process scheduled based on specific timing rules (e.g., process X starts once per week on Thursdays) or relative to mission events (e.g., sequence generation process begins on a Monday, at least three weeks before each Europa closest approach). Over a 5-month period, and for each of six Clipper candidate tours, the result was a 20,000+ line, workforce-loaded schedule that documents all of the process-driven work effort at the level of individual roles, along with a significant portion of the level-of-effort work. Post-processing code calculated the absolute and relative number of work hours during a nominal 5 day / 40 hour work week, the work effort during 2nd and 3rd shift, as well as 1st shift on weekends. The resultant schedules and shift tables were used to generate objective measures that can be related to both human factors and to operational risk and showed that Clipper tours which utilize 6:1 resonant (21.25 day) orbits instead of 4:1 resonant (14.17 day) orbits during the first dozen or so Europa flybys are advantageous to flight operations. A similar approach can be extended to assist missions in more objective assessments of a number of mission issues and trades, including tour selection and spacecraft design for operability.