Biblio

Blockchain will increase supply chains' productivity and accountability, and have a positive effect on anything from warehousing to distribution to payment. To bridge the supply chain visibility gap, blockchain is being deployed because of its security features like immutability, tamper-resistant and hash proof. Blockchain integration with IoT increases the traceability and verifiability of the supply chain management and drastically eradicates the fraudulent activities including bribery, money laundering, forged checks, sanction violations, misrepresentation of goods and services. Blockchain can help to cross-check the verification, identification and authenticity of IoT devices to reduce the frequency and ramifications of fraud in supply chain management. The epidemic outbreak of SARS-CoV-2 has disrupted many global supply chains. The Geneva-based World Economic Forum declared that SARS-CoV-2 exposed supply chain failures can be tackled by blockchain technology. This paper explores the modern methodologies of supply chain management with integration of blockchain and IoT.

Crime is a social problem that after the confinement of COVID-19 has increased significantly worldwide, which is why it is important to know what technological tools can be used to prevent criminal acts. In the present work, a systemic analysis was carried out to determine the importance of how to prevent crime using new information technologies. Fifty research articles were selected between 2015 and 2021. The information was obtained from different databases such as IEEE Xplore, Redalyc, Scopus, SciELO and Medline. Keywords were used to delimit the search and be more precise in our inquiry on the web. The results obtained show specific information on how to prevent crime using new information technologies. We conclude that new information technologies help to prevent crime since several developed countries have implemented their security system effectively, while underdeveloped countries do not have adequate technologies to prevent crime.

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion (MIF) concept being studied on the Z-machine at Sandia National Laboratories. MagLIF relies on quasi-adiabatic heating of a gaseous deuterium (DD) fuel and flux compression of a background axially oriented magnetic field to achieve fusion relevant plasma conditions. The magnetic flux per fuel radial extent determines the confinement of charged fusion products and is thus of fundamental interest in understanding MagLIF performance. It was recently shown that secondary DT neutron spectra and yields are sensitive to the magnetic field conditions within the fuel, and thus provide a means by which to characterize the magnetic confinement properties of the fuel. 1 , 2 , 3 We utilize an artificial neural network to surrogate the physics model of Refs. [1] , [2] , enabling Bayesian inference of the magnetic confinement parameter for a series of MagLIF experiments that systematically vary the laser preheat energy deposited in the target. This constitutes the first ever systematic experimental study of the magnetic confinement properties as a function of fundamental inputs on any neutron-producing MIF platform. We demonstrate that the fuel magnetization decreases with deposited preheat energy in a fashion consistent with Nernst advection of the magnetic field out of the hot fuel and diffusion into the target liner.

Security has become a significant factor of Internet of Things (IoT) and Cyber Physical Systems (CPS) wherein the devices usually vary in computing power and intrinsic hardware features. It is necessary to use security-by-design method in the development of these systems. This paper focuses on the security design issue about this sort of heterogeneous embedded systems and proposes a systematic approach aiming to achieve optimal security design objective.

In this paper, we highlight a fundamental vulnerability associated with the widely adopted “Just Tap” push-based authentication in the face of a concurrency attack, and propose the method REPLICATE, a redesign to counter this vulnerability. In the concurrency attack, the attacker launches the login session at the same time the user initiates a session, and the user may be fooled, with high likelihood, into accepting the push notification which corresponds to the attacker's session, thinking it is their own. The attack stems from the fact that the login notification is not explicitly mapped to the login session running on the browser in the Just Tap approach. REPLICATE attempts to address this fundamental flaw by having the user approve the login attempt by replicating the information presented on the browser session over to the login notification, such as by moving a key in a particular direction, choosing a particular shape, etc. We report on the design and a systematic usability study of REPLICATE. Even without being aware of the vulnerability, in general, participants placed multiple variants of REPLICATE in competition to the Just Tap and fairly above PIN-based authentication.

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students.

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.

Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryp-tocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through cryptojacking malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful cryptojacking malware campaigns. Nonetheless, existing detection methods such as browser extensions that protect users with blacklist methods or antivirus programs with different analysis methods can only provide a partial panacea to this emerging crypto-jacking issue as the attackers can easily bypass them by using obfuscation techniques or changing their domains or scripts frequently. Therefore, many studies in the literature proposed cryptojacking malware detection methods using various dynamic/behavioral features. However, the literature lacks a systemic study with a deep understanding of the emerging cryptojacking malware and a comprehensive review of studies in the literature. To fill this gap in the literature, in this SoK paper, we present a systematic overview of cryptojacking malware based on the information obtained from the combination of academic research papers, two large cryptojacking datasets of samples, and 45 major attack instances. Finally, we also present lessons learned and new research directions to help the research community in this emerging area.

In the computer era, various digital devices are used along with networking technology for data communication in secured manner. But sometimes these systems are misused by the attackers. Information security with the high efficiency devices, tools are utilized for protecting the communication media and valuable data. In case of any unwanted incidents and security breaches, digital forensics methods and measures are well utilized for detecting the type of attacks, sources of attacks, their purposes. By utilizing information related to security measures, digital forensics evidences with suitable methodologies, digital forensics investigators detect the cyber-crimes. It is also necessary to prove the cyber-crimes before the law enforcement department. During this process investigators type to collect different types of information from the digital devices concerned to the cyber-attack. One of the major tasks of the digital investigator is collecting and managing the seizure records from the crime-scene. The present paper discusses the seizure record framework for digital forensics investigations.

For the purpose of stealthiness, trigger-based Hardware Trojans(HTs) tend to have at least one trigger signal with an extremely low transition probability to evade the functional verification. In this paper, we discuss the correlation between poor testability and low transition probability, and then propose a kind of systematic Trojan trigger model with extremely low transition probability but reasonable testability, which can disable the Controllability and Observability for hardware Trojan Detection (COTD) technique, an efficient HT detection method based on circuits testability. Based on experiments and tests on circuits, we propose that the more imbalanced 0/1-controllability can indicate the lower transition probability. And a trigger signal identification method using the imbalanced 0/1-controllability is proposed. Experiments on ISCAS benchmarks show that the proposed method can obtain a 100% true positive rate and average 5.67% false positive rate for the trigger signal.

With the rapid development of cloud computing which has been extensively applied in the health research, the concept of medical cloud has become widespread. In this paper, we proposed an integrated medical cloud architecture with multiple applications based on privacy protection. The scheme in this paper adopted attribute encryption to ensure the PHR files encrypted all the time in order to protect the health privacy of the PHR owners not leaked. In addition, the medical cloud architecture proposed in this paper is suitable for multiple application scenarios. Different from the traditional domain division which has public domain (PUD) and private domain (PSD), the PUD domain is further divided into PUD1and PUD2 with finer granularity based on different permissions of the PHR users. In the PUD1, the PHR users have read or write access to the PHR files, while the PHR users in the PUD2 only have read permissions. In the PSD, we use key aggregation encryption (KAE) to realize the access control. For PHR users of PUD1 and PUD2, the outsourcable ABE technology is adopted to greatly reduce the computing burden of users. The results of function and performance test show that the scheme is safe and effective.

The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.

Physical protection system (PPS) is developed to protect the assets or facilities against threats. A systematic analysis of the capabilities and intentions of potential threat capabilities is needed resulting in a so-called Design Basis Threat (DBT) document. A proper development of DBT is important to identify the system requirements that are required for adequately protecting a system and to optimize the resources needed for the PPS. In this paper we propose a model-based systems engineering approach for developing a DBT based on feature models. Based on a domain analysis process, we provide a metamodel that defines the key concepts needed for developing DBT. Subsequently, a reusable family feature model for PPS is provided that includes the common and variant properties of the PPS concepts detection, deterrence and response. The configuration processes are modeled to select and analyze the required features for implementing the threat scenarios. Finally, we discuss the integration of the DBT with the PPS design process.

With the booming of Internet technology, the continuous emergence of new technologies and new algorithms greatly expands the application boundaries of cyberspace. While enjoying the convenience brought by informatization, the society is also facing increasingly severe threats to the security of cyberspace. In cyber security defense, cyberspace operators rely on the discovered vulnerabilities, attack patterns, TTPs, and other knowledge to observe, analyze and determine the current threats to the network and security situation in cyberspace, and then make corresponding decisions. However, most of such open-source knowledge is distributed in different data sources in the form of text or web pages, which is not conducive to the understanding, query and correlation analysis of cyberspace operators. In this paper, a knowledge graph for cyber security is constructed to solve this problem. At first, in the process of obtaining security data from multi-source heterogeneous cyberspaces, we adopt efficient crawler to crawl the required data, paving the way for knowledge graph building. In order to establish the ontology required by the knowledge graph, we abstract the overall framework of security data sources in cyberspace, and depict in detail the correlations among various data sources. Then, based on the \$$\backslash$mathbfOWL +$\backslash$mathbfSWRL\$ language, we construct the cyber security knowledge graph. On this basis, we design an analysis system for situation in cyberspace based on knowledge graph and the Snort intrusion detection system (IDS), and study the rules in Snort. The system integrates and links various public resources from the Internet, including key information such as general platforms, vulnerabilities, weaknesses, attack patterns, tactics, techniques, etc. in real cyberspace, enabling the provision of comprehensive, systematic and rich cyber security knowledge to security researchers and professionals, with the expectation to provide a useful reference for cyber security defense.

Cyber attacks are a major concern for network administrators as the occurrences of such events are continuously increasing on the Internet. Software-defined networks (SDN) enable many management applications, but they may also become targets for attackers. Due to the separation of the data plane and the control plane, the controller appears as a new element in SDN networks, allowing centralized control of the network, becoming a strategic target in carrying out an attack. According to reports generated by security labs, the frequency of the distributed denial of service (DDoS) attacks has seen an increase in recent years, characterizing a major threat to the SDN. However, few research papers address the prevention of DDoS attacks on SDN. Therefore, this work presents a Systematic Mapping of Literature, aiming at identifying, classifying, and thus disseminating current research studies that propose techniques and methods for preventing DDoS attacks in SDN. When answering these questions, it was determined that the SDN controller was vulnerable to possible DDoS attacks. No prevention methods were found in the literature for the first phase of the attack (when attackers try to deceive users and infect the host). Therefore, the security of software-defined networks still needs improvement over DDoS attacks, despite the evident risk of an attack targeting the SDN controller.

This paper proposes an artificial immune information security protection technology architecture for embedded system of Electric power equipment. By simulating the three functions of human immunity, namely "immune homeostasis", "immune surveillance" and "immune defense", the power equipment is endowed with the ability of human like active immune security protection. Among them, "immune homeostasis" is constructed by trusted computing technology components to establish a trusted embedded system running environment. Through fault-tolerant component construction, "immune surveillance" and "immune defense" realize illegal data defense, business logic legitimacy check and equipment status evaluation, realize real-time perception and evaluation of power equipment's own security status, as well as fault emergency handling and event backtracking record, so that power equipment can realize self recovery from abnormal status. The proposed technology architecture is systematic, scientific and rich in scalability, which can significantly improve the information security protection ability of electric power equipment.

Numerous measurement researches have been performed to discover the IPv4 network security issues by leveraging the fast Internet-wide scanning techniques. However, IPv6 brings the 128-bit address space and renders brute-force network scanning impractical. Although significant efforts have been dedicated to enumerating active IPv6 hosts, limited by technique efficiency and probing accuracy, large-scale empirical measurement studies under the increasing IPv6 networks are infeasible now. To fill this research gap, by leveraging the extensively adopted IPv6 address allocation strategy, we propose a novel IPv6 network periphery discovery approach. Specifically, XMap, a fast network scanner, is developed to find the periphery, such as a home router. We evaluate it on twelve prominent Internet service providers and harvest 52M active peripheries. Grounded on these found devices, we explore IPv6 network risks of the unintended exposed security services and the flawed traffic routing strategies. First, we demonstrate the unintended exposed security services in IPv6 networks, such as DNS, and HTTP, have become emerging security risks by analyzing 4.7M peripheries. Second, by inspecting the periphery's packet routing strategies, we present the flawed implementations of IPv6 routing protocol affecting 5.8M router devices. Attackers can exploit this common vulnerability to conduct effective routing loop attacks, inducing DoS to the ISP's and home routers with an amplification factor of \textbackslashtextbackslashgt 200. We responsibly disclose those issues to all involved vendors and ASes and discuss mitigation solutions. Our research results indicate that the security community should revisit IPv6 network strategies immediately.

The annotated dataset is the foundation for Supervised Natural Language Processing. However, the cost of obtaining dataset is high. In recent years, the Few-Shot Learning has gradually attracted the attention of researchers. From the definition, in this paper, we conclude the difference in Few-Shot Learning between Natural Language Processing and Computer Vision. On that basis, the current Few-Shot Learning on Natural Language Processing is summarized, including Transfer Learning, Meta Learning and Knowledge Distillation. Furthermore, we conclude the solutions to Few-Shot Learning in Natural Language Processing, such as the method based on Distant Supervision, Meta Learning and Knowledge Distillation. Finally, we present the challenges facing Few-Shot Learning in Natural Language Processing.

With the rapid growth of online services, the number of online accounts proliferates. The security of a single user account no longer depends merely on its own service provider but also the accounts on other service platforms (We refer to this online account environment as Online Account Ecosystem). In this paper, we first uncover the vulnerability of Online Account Ecosystem, which stems from the defective multi-factor authentication (MFA), specifically the ones with SMS-based verification, and dependencies among accounts on different platforms. We propose Chain Reaction Attack that exploits the weakest point in Online Account Ecosystem and can ultimately compromise the most secure platform. Furthermore, we design and implement ActFort, a systematic approach to detect the vulnerability of Online Account Ecosystem by analyzing the authentication credential factors and sensitive personal information as well as evaluating the dependency relationships among online accounts. We evaluate our system on hundreds of representative online services listed in Alexa in diversified fields. Based on the analysis from ActFort, we provide several pragmatic insights into the current Online Account Ecosystem and propose several feasible countermeasures including the online account exposed information protection mechanism and the built-in authentication to fortify the security of Online Account Ecosystem.

Spin-transfer torque magnetoresistive random-access memories (STT-MRAMs) are gradually superseding conventional SRAMs as last-level cache in System-on-Chip designs. Their manufacturing process includes trimming a reference resistance in STT-MRAM modules to reliably determine the logic values of 0 and 1 during read operations. Typically, an on-chip trimming routine consists of multiple runs of a test algorithm with different settings of a trimming port. It may inherently produce a large number of mismatches. Diagnosis of such a sizeable volume of errors by means of existing memory built-in self-test (MBIST) schemes is either infeasible or a time-consuming and expensive process. In this paper, we propose a new memory fault diagnosis scheme capable of handling STT-MRAM-specific error rates in an efficient manner. It relies on a convolutional reduction of memory outputs and continuous shifting of the resultant data to a tester through a few output channels that are typically available in designs using an on-chip test compression technology, such as the embedded deterministic test. It is shown that processing the STT-MRAM output by using a convolutional compactor is a preferable solution for this type of applications, as it provides a high diagnostic resolution while incurring a low hardware overhead over traditional MBIST logic.

Today we live in a hyper-connected world, where a large amount of applications and services are supported by ad hoc networks. They have a decentralized management, are flexible and versatile but their characteristics are in turn their main weaknesses. This work introduces a preliminary analysis of the evolution, trends and the state of the art in the context of the security in ad hoc networks. To this end, two different methodologies are applied: a bibliometric analysis and a Systematic Literature Review. Results show that security in MANETs and VANETs are still an appealing research field. In addition, we realized that there is no clear separation of solutions by line of defense. This is because they are sometimes misclassified by the authors or simply there is no line of defense that totally fit well with the proposed solution. Because of that, new taxonomies including novel definitions of lines of defense are needed. In this work, we propose the use of tolerant or survivable solutions which are the ones that preserve critical system or network services in presence of fault, malfunctions or attacks.

These days, almost everyone has been entirely relying on mobile devices and mobile related applications running on Android Operating Systems, the most used Mobile Operating System in the world with the largest market share. These Mobile devices and applications can become an information goldmine for hackers and are considered one of the significant concerns mobile users face who stand a chance of being victimized during data breach from hackers due to lapse in information security and controls. Such challenge can be put to bare through systematic digital forensic analysis through penetration testing for a humanoid robot like Zenbo, which run Android OS and related application, to help identify associated security vulnerabilities and develop controls required to improve security using popular penetration testing tools such as Drozer, Mobile Application Security framework (mobSF), and AndroBugs with the help of Santoku Linux distribution.

This article describes a research work in progress, in which a methodology for the development of computerized educational materials based on augmented reality is proposed. The development of the proposal is preceded by a systematic review of the literature in which the convenience of having a methodology that assists teachers and developers interested in the development of educational materials related to augmented reality technology is concluded. The proposed methodology consists of four stages: (1) initiation, (2) design of the learning scenario, (3) implementation and (4) evaluation, as well as specific elements that must be considered in each of them for their correct fulfillment. Finally, the article briefly describes the validation strategy designed to evaluate this methodological proposal.

This work proposes an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators. Firstly, by employing the command filtered backstepping technology, the ``explosion of complexity'' problem is overcame. Then, the event-triggered strategy is utilized which makes that the control input is updated aperiodically when the event-trigger occurs. The utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Moreover, it can be proved that all the variables in the closed-loop system are bounded and the tracking error converges asymptotically to zero. Finally, the simulation studies are included to show the effectiveness of the proposed control scheme.

Machine learning (ML) models are now a key component for many applications. However, machine learning based systems (MLBSs), those systems that incorporate them, have proven vulnerable to various new attacks as a result. Currently, there exists no systematic process for eliciting security requirements for MLBSs that incorporates the identification of adversarial machine learning (AML) threats with those of a traditional non-MLBS. In this research study, we explore the applicability of traditional threat modeling and existing attack libraries in addressing MLBS security in the requirements phase. Using an example MLBS, we examined the applicability of 1) DFD and STRIDE in enumerating AML threats; 2) Microsoft SDL AI/ML Bug Bar in ranking the impact of the identified threats; and 3) the Microsoft AML attack library in eliciting threat mitigations to MLBSs. Such a method has the potential to assist team members, even with only domain specific knowledge, to collaboratively mitigate MLBS threats.