Biblio

Internet Protocol Version 6 (IPv6) is expected for widespread deployment worldwide. Such rapid development of IPv6 may lead to safety problems. The main threats in IPv6 networks are denial of service (DoS) attacks and distributed DoS (DDoS) attacks. In addition to the similar threats in Internet Protocol Version 4 (IPv4), IPv6 has introduced new potential vulnerabilities, which are DoS and DDoS attacks based on Internet Control Message Protocol version 6 (ICMPv6). We divide such new attacks into two categories: pure flooding attacks and source address spoofing attacks. We propose P4-NSAF, a scheme to defend against the above two IPv6 DoS and DDoS attacks in the programmable data plane. P4-NSAF uses Count-Min Sketch to defend against flooding attacks and records information about IPv6 agents into match tables to prevent source address spoofing attacks. We implement a prototype of P4-NSAF with P4 and evaluate it in the programmable data plane. The result suggests that P4-NSAF can effectively protect IPv6 networks from DoS and DDoS attacks based on ICMPv6.

As a new generation of power grid system, smart grid and smart meter conduct two-way communication to realize the intelligent collection, monitoring and dispatching of user power data, so as to achieve a safer, stable, reliable and efficient power grid environment. With the vigorous development of power grid, there are also some security and privacy problems. This paper uses Paillier homomorphic encryption algorithm and role-based access control strategy to ensure the privacy security in the process of multi-dimensional aggregation, data transmission and sharing of power data. Applying the characteristics of blockchain technology such as decentralization, non tampering and traceability to the smart grid can effectively solve the privacy and security problems of power data transmission and sharing in the smart grid. This paper compares Paillier encryption algorithm with PPAR algorithm and SIAHE algorithm in terms of encryption mechanism, number of aggregators and computational complexity respectively. The results show that Paillier homomorphic encryption algorithm has higher data privacy and security.

The vehicular networks extend the internet services to road edge. They allow users to stay connected offering them a set of safety and infotainment services like weather forecasts and road conditions. The security and privacy are essential issues in computing systems and networks. They are particularly important in vehicular networks due to their direct impact on the users’ safety on road. Various researchers have concentrated their efforts on resolving these two issues in vehicular networks. A great number of researches are found in literature and with still existing open issues and security risks to be solved, the research is continuous in this area. However, the researchers may face some difficulties in choosing the correct method to prove their works or to illustrate their excellency in comparison with existing solutions. In this paper, we review a set of evaluation methodologies and metrics to measure, proof or analyze privacy and security solutions. The aim of this review is to illuminate the readers about the possible existing methods to help them choose the correct techniques to use and reduce their difficulties.

Memory corruption attacks have existed for multiple decades, and have become a major threat to computer systems. At the same time, a number of defense techniques have been proposed by research community. With the wide adoption of CPU-based memory safety solutions, sophisticated attackers tend to tamper with system memory via direct memory access (DMA) attackers, which leverage DMA-enabled I/O peripherals to fully compromise system memory. The Input-Output Memory Management Units (IOMMUs) based solutions are widely believed to mitigate DMA attacks. However, recent works point out that attackers can bypass IOMMU-based protections by manipulating the DMA interfaces, which are particularly vulnerable to race conditions and other unsafe interactions.State-of-the-art hardware-supported memory protections rely on metadata to perform security checks on memory access. Consequently, the additional memory request for metadata results in significant performance degradation, which limited their feasibility in real world deployments. For quantitative analysis, we separate the total metadata access latency into DRAM latency, on-chip latency, and cache latency, and observe that the actual DRAM access is less than half of the total latency. To minimize metadata access latency, we propose EMC, a low-overhead heap memory safety solution that implements a tripwire based mechanism on the memory controller. In addition, by using memory controller as a natural gateway of various memory access data paths, EMC could provide comprehensive memory safety enforcement to all memory data paths from/to system physical memory. Our evaluation shows an 0.54% performance overhead on average for SPEC 2017 workloads.

The burglary of a safe in the city of Jombang, East Java, lost valuables belonging to the Cemerlang Multipurpose Trading Cooperative. Therefore, a security system tool was created in the safe that serves as a place to store valuables and important assets. Change the security system using the security system with a private unique method with modulo arithmetic pattern. The security system of the safe is designed in layers which are attached with the RFID tag by registering and then verifying it on the card. Entering the password on the card cannot be read or is not performed, then the system will refuse to open it. arduino mega type 256 components, RFID tag is attached to the RFID reader, only one validated passive tag can open access to the security system, namely number B9 20 E3 0F. Meanwhile, of the ten passwords entered, only three match the modulo arithmetic format and can open the security system, namely password numbers 22540, 51324 and 91032. The circuit system on the transistor in the solenoid driver circuit works after the safety system opens. The servo motor can rotate according to the input of the open 900 servo angle rotation program.

This paper presents a definition of a secure system and design principles, which help govern security policies within an embedded system. By understanding a secure system, a common system on chip (SoC) architecture is evaluated and their vulnerabilities explored. This effort helped define requirements for a framework for a secure and isolated SoC architecture for users to develop in. Throughout this paper, a SoC architecture framework for isolated domains has been proposed and its robustness verified against different attack scenarios. To support different levels of criticality and complexity in developing user applications, three computing domains were proposed: security and safety critical (SSC) domain, high performance (HP) domain, and sandbox domain. These domains allow for complex applications to be realized with varying levels of security. Isolation between different computing domains is established using consumer off the shelf (COTS) techniques and architectural components provided by the Zynq Ultrascale+ (ZU+) multiprocessor SoC (MPSoC). To the best of our knowledge, this is the first work that implements a secure system design on the ZU+ platform. There have been many other implementations in hardware security to mitigate certain attack scenarios such as side channel attacks, temporal attacks, hardware trojans, etc. However, our work is different than others, as it establishes the framework for isolated computing domains for secure applications and also verifies system security by attacking one domain from the others.

An often overlooked but equally important aspect of unmanned aerial system (UAS) design is the security of their networking protocols and how they deal with cyberattacks. In this context, cyberattacks are malicious attempts to monitor or modify incoming and outgoing data from the system. These attacks could target anywhere in the system where a transfer of data occurs but are most common in the transfer of data between the control station and the UAS. A compromise in the networking system of a UAS could result in a variety of issues including increased network latency between the control station and the UAS, temporary loss of control over the UAS, or a complete loss of the UAS. A complete loss of the system could result in the UAS being disabled, crashing, or the attacker overtaking command and control of the platform, all of which would be done with little to no alert to the operator. Fortunately, the majority of higher-end, enterprise, and government UAS platforms are aware of these threats and take actions to mitigate them. However, as the consumer market continues to grow and prices continue to drop, network security may be overlooked or ignored in favor of producing the lowest cost product possible. Additionally, these commercial off-the-shelf UAS often use uniform, standardized frequency bands, autopilots, and security measures, meaning a cyberattack could be developed to affect a wide variety of models with minimal changes. This paper will focus on a low-cost educational-use UAS and test its resilience to a variety of cyberattack methods, including man-in-the-middle attacks, spoofing of data, and distributed denial-of-service attacks. Following this experiment will be a discussion of current cybersecurity practices for counteracting these attacks and how they can be applied onboard a UAS. Although in this case the cyberattacks were tested against a simpler platform, the methods discussed are applicable to any UAS platform attempting to defend against such cyberattack methods.

The design of safety-critical embedded systems is a complex process that involves the reuse of proven solutions to fulfill a set of requirements. While safety is considered as the major requirement to be satisfied in safety-critical embedded systems, the security attacks can affect the security as well as the safety of these systems. Therefore, ensuring the security of the safety-critical embedded systems is as important as ensuring the safety requirements. The concept of design patterns, which provides common solutions to widely recurring design problems, have been extensively engaged in the design of the hardware and software in many fields, including embedded systems. However, there is an inadequacy of experience with security patterns in the field of safety-critical embedded systems. To address this problem, this paper proposes an approach to integrate security patterns with safety patterns in the design of safety-critical embedded systems. Moreover, it presents a customized representation for security patterns to be more relevant to the common safety patterns in the context of safety-critical embedded systems.

In recent years, technological advancements in the AI and VR fields have increasingly often been paired with considerations on ethics and safety aimed at mitigating unintentional design failures. However, cybersecurity-oriented AI and VR safety research has emphasized the need to additionally appraise instantiations of intentional malice exhibited by unethical actors at pre- and post-deployment stages. On top of that, in view of ongoing malicious deepfake developments that can represent a threat to the epistemic security of a society, security-aware AI and VR design strategies require an epistemically-sensitive stance. In this vein, this paper provides a theoretical basis for two novel AIVR safety research directions: 1) VR as immersive testbed for a VR-deepfake-aided epistemic security training and 2) AI as catalyst within a deepfake-aided so-called cyborgnetic creativity augmentation facilitating an epistemically-sensitive threat modelling. For illustration, we focus our use case on deepfake text – an underestimated deepfake modality. In the main, the two proposed transdisciplinary lines of research exemplify how AIVR safety to defend against unethical actors could naturally converge toward AIVR ethics whilst counteracting epistemic security threats.

Nowadays, safety is a first-rate subject for all applications. There has been an exponential growth year by year in the number of businesses going digital since the few decades following the birth of the Internet. In these technologically advanced times, cyber security is a must mainly for internet applications, so we have the notion of diving deeper into the Cyber security domain and are determined to make a complete project. We aim to develop a website portal for ease of communication between us and the end user. Utilizing the power of python scripting and flask server to make independent automated tools for detection of SQLI, XSS & a Spider(Content Discovery Tool). We have also integrated skipfish as a website vulnerability scanner to our project using python and Kali Linux. Since conducting a penetration test on another website without permission is not legal, we thought of building a dummy website prone to OS Command Injection in addition to the above-mentioned attacks. A well-documented report will be generated after the penetration test/ vulnerability scan. In case the website is vulnerable, patching of the website will be done with the user's consent.

The use of software to support the information infrastructure that governments, critical infrastructure providers and businesses worldwide rely on for their daily operations and business processes is gradually becoming unavoidable. Commercial off-the shelf software is widely and increasingly used by these organizations to automate processes with information technology. That notwithstanding, cyber-attacks are becoming stealthier and more sophisticated, which has led to a complex and dynamic risk environment for IT-based operations which users are working to better understand and manage. This has made users become increasingly concerned about the integrity, security and reliability of commercial software. To meet up with these concerns and meet customer requirements, vendors have undertaken significant efforts to reduce vulnerabilities, improve resistance to attack and protect the integrity of the products they sell. These efforts are often referred to as “software assurance.” Software assurance is becoming very important for organizations critical to public safety and economic and national security. These users require a high level of confidence that commercial software is as secure as possible, something only achieved when software is created using best practices for secure software development. Therefore, in this paper, we explore the need for information assurance and its importance for both organizations and end users, methodologies and best practices for software security and information assurance, and we also conducted a survey to understand end users’ opinions on the methodologies researched in this paper and their impact.

Security is an essential requirement of Industrial Control System (ICS) environments and its underlying communication infrastructure. Especially the lowest communication level within Supervisory Control and Data Acquisition (SCADA) systems - the field level - commonly lacks security measures.Since emerging wireless technologies within field level expose the lowest communication infrastructure towards potential attackers, additional security measures above the prevalent concept of air-gapped communication must be considered.Therefore, this work analyzes security aspects for the wireless communication protocol IO-Link Wireless (IOLW), which is commonly used for sensor and actuator field level communication. A possible architecture for an IOLW safety layer has already been presented recently [1].In this paper, the overall attack surface of IOLW within its typical environment is analyzed and attack preconditions are investigated to assess the effectiveness of different security measures. Additionally, enhanced security measures are evaluated for the communication systems and the results are summarized. Also, interference of security measures and functional safety principles within the communication are investigated, which do not necessarily complement one another but may also have contradictory requirements.This work is intended to discuss and propose enhancements of the IOLW standard with additional security considerations in future implementations.

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.

The fast-evolving Intelligent Transportation Systems (ITS) are crucial in the 21st century, promising answers to congestion and accidents that bother people worldwide. ITS applications such as Connected and Autonomous Vehicle (CAVs) update and broadcasts road incident event messages, and this requires significant data to be transmitted between vehicles for a decision to be made in real-time. However, broadcasting trusted incident messages such as accident alerts between vehicles pose a challenge for CAVs. Most of the existing-trust solutions are based on the vehicle's direct interaction base reputation and the psychological approaches to evaluate the trustworthiness of the received messages. This paper provides a scheme for improving trust in the received incident alert messages for real-time decision-making to detect malicious alerts between CAVs using direct and indirect interactions. This paper applies artificial intelligence and statistical data classification for decision-making on the received messages. The model is trained based on the US Department of Technology Safety Pilot Deployment Model (SPMD). An Autonomous Decision-making Trust Scheme (ADmTS) that incorporates a machine learning algorithm and a local trust manager for decision-making has been developed. The experiment showed that the trained model could make correct predictions such as 98% and 0.55% standard deviation accuracy in predicting false alerts on the 25% malicious data

Face recognition technology is widely employed in a variety of applications, including public security, criminal identification, multimedia data management, and so on. Because of its importance for practical applications and theoretical issues, the facial recognition system has received a lot of attention. Furthermore, numerous strategies have been offered, each of which has shown to be a significant benefit in the field of facial and pattern recognition systems. Face recognition still faces substantial hurdles in unrestricted situations, despite these advancements. Deep learning techniques for facial recognition are presented in this paper for accurate detection and identification of facial images. The primary goal of facial recognition is to recognize and validate facial features. The database consists of 500 color images of people that have been pre-processed and features extracted using Linear Discriminant Analysis. These features are split into 70 percent for training and 30 percent for testing of decision tree classifiers for the computation of face recognition system performance.

With the increasing complexity of the driving environment, more and more attention has been paid to the research on improving the intelligentization of traffic control. Among them, the digital twin-based internet of vehicle can establish a mirror system on the cloud to improve the efficiency of communication between vehicles, provide warning and safety instructions for drivers, avoid driving potential dangers. To ensure the security and effectiveness of data sharing in traffic control, this paper proposes a secure and privacy-preserving scheme for digital twin-based traffic control. Specifically, in the data uploading phase, we employ a group signature with a time-bound keys technique to realize data source authentication with efficient members revocation and privacy protection, which can ensure that data can be securely stored on cloud service providers after it synchronizes to its twin. In the data sharing stage, we employ the secure and efficient attribute-based access control technique to provide flexible and efficient data sharing, in which the parameters of a specific sub-policy can be stored during the first decryption and reused in subsequent data access containing the same sub-policy, thus reducing the computing complexity. Finally, we analyze the security and efficiency of the scheme theoretically.

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.

Industrial control systems (ICSs) have become more complex due to their increasing connectivity, heterogeneity and, autonomy. As a result, cyber-threats against such systems have been significantly increased as well. Since a compromised industrial system can easily lead to hazardous safety and security consequences, it is crucial to develop security countermeasures to protect coexisting IT systems and industrial physical processes being involved in modern ICSs. Accordingly, in this study, we propose a deep learning-based semantic anomaly detection framework to model the complex behavior of ICSs. In contrast to the related work assuming only simpler security threats targeting individual controllers in an ICS, we address multi-PLC attacks that are harder to detect as requiring to observe the overall system state alongside single-PLC attacks. Using industrial simulation and emulation frameworks, we create a realistic setup representing both the production and networking aspects of industrial systems and conduct some potential attacks. Our experimental results indicate that our model can detect single-PLC attacks with 95% accuracy and multi-PLC attacks with 80% accuracy and nearly 1% false positive rate.

The Internet of Vehicles (IoV) has a tremendous prospect for numerous vehicular applications. IoV enables vehicles to transmit data to improve roadway safety and efficiency. Data security is essential for increasing the security and privacy of vehicle and roadway infrastructures in IoV systems. Several researchers proposed numerous solutions to address security and privacy issues in IoV systems. However, these issues are not proper solutions that lack data authentication and verification protocols. In this paper, a blockchain-enabled automated data management system for vehicles has been proposed and demonstrated. This work enables automated data verification and authentication using smart contracts. Certified organizations can only access vehicle data uploaded by the vehicle user to the Interplanetary File System (IPFS) server through that vehicle user’s consent. The proposed system increases the security of vehicles and data. Vehicle privacy is also maintained here by increasing data privacy.

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.

Technological innovations are expanding rapidly in the public safety sector providing opportunities for more targeted and comprehensive urban crime deterrence and detection. Yet, the spatial dispersion of crimes may vary over time. Therefore, it is unclear whether and how sensors can optimally impact crime rates. We developed a Smart City Digital Twin-based method to dynamically place license plate reader (LPR) sensors and improve their detection and deterrence performance. Utilizing continuously updated crime records, the convolutional long short-term memory algorithm predicted areas crimes were most likely to occur. Then, a Monte Carlo traffic simulation simulated suspect vehicle movements to determine the most likely routes to flee crime scenes. Dynamic LPR placement predictions were made weekly, capturing the spatiotemporal variation in crimes and enhancing LPR performance relative to static placement. We tested the proposed method in Warner Robins, GA, and results support the method's promise in detecting and deterring crime.

Databases are at the heart of modern applications and any threats to them can seriously endanger the safety and functionality of applications relying on the services offered by a DBMS. It is therefore pertinent to identify key risks to the secure operation of a database system. This paper identifies the key risks, namely, SQL injection, weak audit trails, access management issues and issues with encryption. A malicious actor can get help from any of these issues. It can compromise integrity, availability and confidentiality of the data present in database systems. The paper also identifies various means and ways to defend against these issues and remedy them. This paper then proceeds to identify from the literature, the potential solutions to these ameliorate the threat from these vulnerabilities. It proposes the usage of encryption to protect the data from being breached and leveraging encrypted databases such as CryptoDB. Better access control norms are suggested to prevent unauthorized access, modification and deletion of the data. The paper also recommends ways to prevent SQL injection attacks through techniques such as prepared statements.

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

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.

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.