Biblio

The military operations in low communications infrastructure scenarios employ flexible solutions to optimize the data processing cycle using situational awareness systems, guaranteeing interoperability and assisting in all processes of decision-making. This paper presents an architecture for the integration of Command, Control, Computing, Communication, Intelligence, Surveillance and Reconnaissance Systems (C4ISR), developed within the scope of the Brazilian Ministry of Defense, in the context of operations with Unmanned Aerial Vehicles (UAV) - swarm drones - and the Internet-to-the-battlefield (IoBT) concept. This solution comprises the following intelligent subsystems embedded in UAV: STFANET, an SDN-Based Topology Management for Flying Ad Hoc Network focusing drone swarms operations, developed by University of Rio Grande do Sul; Interoperability of Command and Control (INTERC2), an intelligent communication middleware developed by Brazilian Navy; A Mission-Oriented Sensors Array (MOSA), which provides the automatization of data acquisition, data fusion, and data sharing, developed by Brazilian Army; The In-Flight Awareness Augmentation System (IFA2S), which was developed to increase the safety navigation of Unmanned Aerial Vehicles (UAV), developed by Brazilian Air Force; Data Mining Techniques to optimize the MOSA with data patterns; and an adaptive-collaborative system, composed of a Software Defined Radio (SDR), to solve the identification of electromagnetic signals and a Geographical Information System (GIS) to organize the information processed. This research proposes, as a main contribution in this conceptual phase, an application that describes the premises for increasing the capacity of sensing threats in the low structured zones, such as the Amazon rainforest, using existing communications solutions of Brazilian defense monitoring systems.

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.

Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS, an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.

The ongoing COVID-19 virus pandemic has resulted in a global tragedy due to its lethal spread. The population's vulnerability grows as a result of a lack of effective helping agents and vaccines against the virus. The spread of viruses can be mitigated by minimizing close connections between people. Social distancing is a critical containment tool for COVID-19 prevention. In this paper, the social distancing violations that are being made by the people when they are in public places are detected. As per CDC (Centers for Disease Control and Prevention) minimum distance that should be maintained by people is 2-3 meters to prevent the spread of COVID- 19, the proposed tool will be used to detect the people who are maintaining less than 2-3 meters of distance between themselves and record them as a violation. As a result, the goal of this work is to develop a deep learning-based system for object detection and tracking models in social distancing detection. For object detection models, You Only Look Once, Version 3 (YOLO v3) is used in conjunction with deep sort algorithms to balance speed and accuracy. To recognize persons in video segments, the approach applies the YOLOv3 object recognition paradigm. An efficient computer vision-based approach centered on legitimate continuous tracking of individuals is presented to determine supportive social distancing in public locations by creating a model to generate a supportive climate that contributes to public safety and detect violations through camera.

Using multi-UAV systems to accomplish both civil and military missions is becoming a popular trend. With the development of software and hardware technologies, Unmanned aerial vehicles (UAVs) are now able to operate autonomously at edge. However, the remote control of manned systems, e.g., ground control station (GCS), remains essential to mission success, and the system's control and non-payload communication (CNPC) are facing severe cyber threats caused by smart attacks. To avoid hijacking, in this paper, we propose a secure mechanism that reduces such security risks for multi-UAV systems. We introduce friendly jamming from UAVs to block eavesdropping on the remote control channel. The trade-off between security and energy consumption is optimized by three approaches designed for UAV and GCS under algorithms of different complexities. Numerical results show the approach efficiency under different mission conditions and security demands, and demonstrate the features of the proposed mechanism for various scenarios.

ROS 2 is rapidly becoming a standard in the robotics industry. Built upon DDS as its default communication middleware and used in safety-critical scenarios, adding secu-rity to robots and ROS computational graphs is increasingly becoming a concern. The present work introduces SROS2, a series of developer tools and libraries that facilitate adding security to ROS 2 graphs. Focusing on a usability-centric approach in SROS2, we present a methodology for securing graphs systematically while following the DevSecOps model. We also demonstrate the use of our security tools by presenting an application case study that considers securing a graph using the popular Navigation2 and SLAM Toolbox stacks applied in a TurtieBot3 robot. We analyse the current capabilities of SROS2 and discuss the shortcomings, which provides insights for future contributions and extensions. Ultimately, we present SROS2 as usable security tools for ROS 2 and argue that without usability, security in robotics will be greatly impaired.

Internet of Medical Things (IoMT) is a rapidly growing branch of IoT (Internet of Things), which requires special treatment to cyber security due to confidentiality of healthcare data and patient health threat. Healthcare data and automated medical devices might become vulnerable targets of malicious cyber-attacks. While a large number of robotic applications, including medical and healthcare, employ robot operating system (ROS) as their backbone, not enough attention is paid for ROS security. The paper discusses a security of ROS-based swing doors automation in the context of a robotic hospital framework, which should be protected from cyber-attacks.

Systems for relative localization in multi-robot systems based on ultra-wideband (UWB) ranging have recently emerged as robust solutions for GNSS-denied environments. Scalability remains one of the key challenges, particularly in adhoc deployments. Recent solutions include dynamic allocation of active and passive localization modes for different robots or nodes in the system. with larger-scale systems becoming more distributed, key research questions arise in the areas of security and trustability of such localization systems. This paper studies the potential integration of collaborative-decision making processes with distributed ledger technologies. Specifically, we investigate the design and implementation of a methodology for running an UWB role allocation algorithm within smart contracts in a blockchain. In previous works, we have separately studied the integration of ROS2 with the Hyperledger Fabric blockchain, and introduced a new algorithm for scalable UWB-based localization. In this paper, we extend these works by (i) running experiments with larger number of mobile robots switching between different spatial configurations and (ii) integrating the dynamic UWB role allocation algorithm into Fabric smart contracts for distributed decision-making in a system of multiple mobile robots. This enables us to deliver the same functionality within a secure and trustable process, with enhanced identity and data access management. Our results show the effectiveness of the UWB role allocation for continuously varying spatial formations of six autonomous mobile robots, while demonstrating a low impact on latency and computational resources of adding the blockchain layer that does not affect the localization process.

Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

AbuSaif is a human-like social robot designed and built at the UAE University's Artificial Intelligence and Robotics Lab. AbuSaif was initially operated by a classical personal computer (PC), like most of the existing social robots. Thus, most of the robot's functionalities are limited to the capacity of that mounted PC. To overcome this, in this study, we propose a web-based platform that shall take the benefits of clustering in cloud computing. Our proposed platform will increase the operational capability and functionality of AbuSaif, especially those needed to operate artificial intelligence algorithms. We believe that the robot will become more intelligent and autonomous using our proposed web platform.

The Robotic Operating System (ROS) is a popular framework for robotics research and development. It's a system that provides hardware abstraction with low-level device management to handle communications and services. ROS is a distributed system, which allows various nodes in a network to communicate using a method such as message passing. When integrating systems using ROS, it is vital to consider the security and privacy of the data and information shared across ROS nodes, which is considered to be one of the most challenging aspects of ROS systems. The goal of this study is to examine the ROS architecture, primary components, and versions, as well as the types of vulnerabilities that might compromise the system. In order to achieve the CIA's three fundamental security criteria on a ROS-based platform, we categorized these vulnerabilities and looked into various security solutions proposed by researchers. We provide a comparative analysis of the ROS-related security solutions, the security threats and issues they addressed, the targeted architecture of the protection or defense system, the solution's evaluation methodology and the evaluation metric, and the limitations that might be viewed as unresolved issues for the future course of action. Finally, we look into future possibilities and open challenges to assist researchers to develop more secure and efficient ROS systems.

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.

This study purpose was to examine the determinant factors that affect the Micro, Small, and Medium Enterprise (MSME) merchants who had the intention to use Quick Response Code Indonesian Standard (QRIS) as a payment system. QRIS was expected to be applied by merchants to diminish the virus spread and keep the circulation of money safe; but there were not many merchants using the QRIS as a payment method. The factors MSME merchant might not use the QRIS were related to perceived usefulness, perceived security, perceived ease of use, and trust. The population was MSMEs in South Tangerang City who did not use QRIS yet and the population was unknown. Using the Lemeshow formula, obtained a sample of 115 people, and the sampling technique used purposive sampling. Then data were analyzed using multi-regression analysis and processed by SPSS. The results indicated that perceived usefulness and perceived security had a significant affect on trust, whereas trust and ease of use significant affect the intention to use QRIS. Moreover, trust was able to mediate the perceived usefulness to intention to use. Since ease of use had no significant affect on trust, then the mediation given by trust to perceived ease of use had no significant affect on intention to use.

Mobile small cells that are enabled with Network Coding (NC) are seen as a potentially useful technique for Fifth Generation (5G) networks, since they can cover an entire city and can be put up on demand anywhere, any time, and on any device. Despite numerous advantages, significant security issues arise as a result of the fact that the NC-enabled mobile small cells are vulnerable to attacks. Intrusions are a severe security threat that exploits the inherent vulnerabilities of NC. In order to make NC-enabled mobile small cells to realize their full potential, it is essential to implement intrusion detection systems. When compared to homomorphic signature or hashing systems, homomorphic message authentication codes (MACs) provide safe network coding techniques with relatively smaller overheads. A number of research studies have been conducted with the goal of developing mobile small cells that are enabled with secure network coding and coming up with integrity protocols that are appropriate for such crowded situations. However, the intermediate nodes alter packets while they are in transit and hence the integrity of the data cannot be confirmed by using MACs and checksums. This research study has analyzed numerous intrusion detection models for NC enabled small cells. This research helps the scholars to get a brief idea about various intrusion detection models.

With the development of Industrial Internet identification analysis, various encryption methods have been widely used in identification analysis to ensure the security of identification encoding and data. However, the past encryption methods failed to consider the problem of encryption efficiency in the case of high concurrency, so it will reduce the identification resolution efficiency and increase the computational pressure of secondary nodes when applying these methods to the identification analysis. In this paper, in order to improve the efficiency of identification analysis under the premise of ensuring information security, a safe and efficient analytical encryption method for industrial Internet identification based on Secure Hash Algorithm 256 (SHA-256), and Rivest-Shamir-Adleman (RSA) is presented. Firstly, by replacing the secret key in the identification encoding encryption with the SHA-256 function, the number of secret keys is reduced, which is beneficial to improve the efficiency of identification analysis. Secondly, by replacing the large prime number of the RSA encryption algorithm with multiple small prime numbers, the generation speed of RSA key pair is improved, which is conducive to reduce the computation of secondary nodes. Finally, by assigning a unique RSA private key to the identification code during the identification registration phase, SHA-256 and RSA are associated, the number of key exchanges is reduced during the encryption process, which is conducive to improve the security of encryption. The experiment verifies that the proposed method can improve security of encryption and efficiency of identification analysis, by comparing the complexity of ciphertext cracking and the identification security analysis time between the traditional encryption method and this method.

Nowadays, online cloud storage networks can be accessed by third parties. Businesses that host large data centers buy or rent storage space from individuals who need to store their data. According to customer needs, data hub operators visualise the data and expose the cloud storage for storing data. Tangibly, the resources may wander around numerous servers. Data resilience is a prior need for all storage methods. For routines in a distributed data center, distributed removable code is appropriate. A safe cloud cache solution, AES-UCODR, is proposed to decrease I/O overheads for multi-block updates in proxy re-encryption systems. Its competence is evaluated using the real-world finance sector.

Proving secure compilation of partial programs typically requires back-translating an attack against the compiled program to an attack against the source program. To prove back-translation, one can syntactically translate the target attacker to a source one-i.e., syntax-directed back-translation-or show that the interaction traces of the target attacker can also be emitted by source attackers—i.e., trace-directed back-translation. Syntax-directed back-translation is not suitable when the target attacker may use unstructured control flow that the source language cannot directly represent. Trace-directed back-translation works with such syntactic dissimilarity because only the external interactions of the target attacker have to be mimicked in the source, not its internal control flow. Revealing only external interactions is, however, inconvenient when sharing memory via unforgeable pointers, since information about shared pointers stashed in private memory is not present on the trace. This made prior proofs unnecessarily complex, since the generated attacker had to instead stash all reachable pointers. In this work, we introduce more informative data-flow traces, combining the best of syntax- and trace-directed back-translation in a simpler technique that handles both syntactic dissimilarity and memory sharing well, and that is proved correct in Coq. Additionally, we develop a novel turn-taking simulation relation and use it to prove a recomposition lemma, which is key to reusing compiler correctness in such secure compilation proofs. We are the first to mechanize such a recomposition lemma in the presence of memory sharing. We use these two innovations in a secure compilation proof for a code generation compiler pass between a source language with structured control flow and a target language with unstructured control flow, both with safe pointers and components.

Internet speeds and technological advancements have made individuals increasingly concerned about their personal information being compromised by criminals. There have been a slew of new steganography and data concealment methods suggested in recent years. Steganography is the art of hiding information in plain sight (text, audio, image and video). Unauthorized users now have access to steganographic analysis software, which may be used to retrieve the carrier files valuable secret information. Unfortunately, because to their inefficiency and lack of security, certain steganography techniques are readily detectable by steganalytical detectors. We present a video steganography technique based on the linear block coding concept that is safe and secure. Data is protected using a binary graphic logo but also nine uncompressed video sequences as cover data and a secret message. It's possible to enhance the security by rearranging pixels randomly in both the cover movies and the hidden message. Once the secret message has been encoded using the Hamming algorithm (7, 4) before being embedded, the message is even more secure. The XOR function will be used to add the encoded message's result to a random set of values. Once the message has been sufficiently secured, it may be inserted into the video frames of the cover. In addition, each frame's embedding region is chosen at random so that the steganography scheme's resilience can be improved. In addition, our experiments have shown that the approach has a high embedding efficiency. The video quality of stego movies is quite close to the original, with a PSNR (Pick Signal to Noise Ratio) over 51 dB. Embedding a payload of up to 90 Kbits per frame is also permissible, as long as the quality of the stego video is not noticeably degraded.

In this paper, the malicious code is run in the sandbox in a safe and controllable environment, the API sequence is deduplicated by the idea of the longest common subsequence, and the CNN and Bi-LSTM are integrated to process and analyze the API sequence. Compared with the method, the method using deep learning can have higher accuracy and work efficiency.

The increasing number of vehicles registered demands for safe and secure carparks due to increase in vehicle theft. The current Automatic Number Plate Recognition (ANPR) systems is a single authentication system and hence it is not secure. Therefore, this research has developed a double authentication system by combing ANPR with a Quick Response (QR) code system to create ANPR-DAS that improves the security at a carpark. It has yielded an accuracy of up to 93% and prevents car theft at a car park.

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.

The spread of the Internet of Things (IoT) and the use of smart control systems in many mission-critical or safety-critical applications domains, like automotive or aeronautical, make devices attractive targets for attackers. Nowadays, several of these are mixed-criticality systems, i.e., they run both high-criticality tasks (e.g., a car control system) and low-criticality ones (e.g., infotainment). High-criticality routines often employ Real-Time Operating Systems (RTOS) to enforce hard real-time requirements, while the tasks with lower constraints can be delegated to more generic-purpose operating systems (GPOS).Much of the control code for these devices is written in memory-unsafe languages such as C and C++. This makes them susceptible to powerful binary attacks, such as the famous Return-Oriented Programming (ROP). Control-Flow Integrity (CFI) is the most investigated security technique to protect against such threats. At now, CFI solutions for real-time embedded systems are not as mature as the ones for general-purpose systems, and even more, there is a lack of in-depth studies on how different operating systems with different security requirements and timing constraints can coexist on a single multicore platform.This paper aims at drawing attention to the subject, discussing the current scientific proposal, and in turn proposing a solution for an optimized asymmetric verification system for execution integrity. By using an embedded hypervisor, predefined cores could be dedicated to only high or low-criticality tasks, with the high-priority core being monitored by the lower-criticality core, relying on offline binary instrumentation and a light exchange of information and signals at runtime. The work also presents preliminary results about a possible implementation for multicore ARM platforms, running both RTOS and GPOS, both in terms of security and performance penalties.

Microcontroller-based embedded systems have become ubiquitous with the emergence of IoT technology. Given its critical roles in many applications, its security is becoming increasingly important. Unfortunately, MCU devices are especially vulnerable. Code reuse attacks are particularly noteworthy since the memory address of firmware code is static. This work seeks to combat code reuse attacks, including ROP and more advanced JIT-ROP via continuous randomization. Previous proposals are geared towards full-fledged OSs with rich runtime environments, and therefore cannot be applied to MCUs. We propose the first solution for ARM-based MCUs. Our system, named HARM, comprises a secure runtime and a binary analysis tool with rewriting module. The secure runtime, protected inside the secure world, proactively triggers and performs non-bypassable randomization to the firmware running in a sandbox in the normal world. Our system does not rely on any firmware feature, and therefore is generally applicable to both bare-metal and RTOS-powered firmware. We have implemented a prototype on a development board. Our evaluation results indicate that HARM can effectively thaw code reuse attacks while keeping the performance and energy overhead low.

This paper proposes a control flow integrity checking method based on the LBR register: through an analysis of the static target program loaded binary modules, gain function attributes such as borders and build the initial transfer of legal control flow boundary, real-time maintenance when combined with the dynamic execution of the program flow of control transfer record, build a complete profile control flow transfer security; Get the call location of /bin/sh or system() in the program to build an internal monitor for control-flow integrity checks. In the process of program execution, on the one hand, the control flow transfer outside the outline is judged as the abnormal control flow transfer with attack threat; On the other hand, abnormal transitions across the contour are picked up by an internal detector. In this method, by identifying abnormal control flow transitions, attacks are initially detected before the attack code is executed, while some attacks that bypass the coarse-grained verification of security profile are captured by the refined internal detector of control flow integrity. This method reduces the cost of control flow integrity check by using the safety profile analysis of coarse-grained check. In addition, a fine-grained shell internal detector is inserted into the contour to improve the safety performance of the system and achieve a good balance between performance and efficiency.