Biblio

Data is a collection of information from the activities of the real world. The file in which such data is stored after transforming into a form that machines can process is generally known as data set. In the real world, many data sets are not complete, and they contain various types of noise. Missing values is of one such kind. Thus, imputing data of these missing values is one of the significant task of data pre-processing. This paper deals with two real time health care data sets namely life expectancy (LE) dataset and chronic kidney disease (CKD) dataset, which are very different in their nature. This paper provides insights on various data imputation techniques to fill missing values by analyzing them. When coming to Data imputation, it is very common to impute the missing values with measure of central tendencies like mean, median, mode Which can represent the central value of distribution but choosing the apt choice is real challenge. In accordance with best of our knowledge this is the first and foremost paper which provides the complete analysis of impact of basic data imputation techniques on various data distributions which can be classified based on the size of data set, number of missing values, type of data (categorical/numerical), etc. This paper compared and analyzed the original data distribution with the data distribution after each imputation in terms of their skewness, outliers and by various descriptive statistic parameters.

Security is a critical aspect in the process of designing, developing, and testing software systems. Due to the increasing need for security-related skills within software systems, there is a growing demand for these skills to be taught in computer science. A series of security modules was developed not only to meet the demand but also to assess the impact of these modules on teaching critical cyber security topics in computer science courses. This full paper in the innovative practice category presents the outcomes of six security modules in a freshman-level course at two institutions. The study adopts a Model-Eliciting Activity (MEA) as a project for students to demonstrate an understanding of the security concepts. Two experimental studies were conducted: 1) Teaching effectiveness of implementing cyber security modules and MEA project, 2) Students’ experiences in conceptual modeling tasks in problem-solving. In measuring the effectiveness of teaching security concepts with the MEA project, students’ performance, attitudes, and interests as well as the instructor’s effectiveness were assessed. For the conceptual modeling tasks in problem-solving, the results of student outcomes were analyzed. After implementing the security modules with the MEA project, students showed a great understanding of cyber security concepts and an increased interest in broader computer science concepts. The instructor’s beliefs about teaching, learning, and assessment shifted from teacher-centered to student-centered during their experience with the security modules and MEA project. Although 64.29% of students’ solutions do not seem suitable for real-world implementation, 76.9% of the developed solutions showed a sufficient degree of creativity.

Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98% accuracy in the classification, detection, and autonomous resilience actions deployment.

Cyber-Physical Systems (CPS) have a physical part that can interact with sensors and actuators. The data that is read from sensors and the one generated to drive actuators is crucial for the correct operation of this class of devices. Most implementations trust the data being read from sensors and the outputted data to actuators. Real-time validation of the input and output of data for any system is crucial for the safety of its operation. This paper proposes an architecture for handling this issue through smart data guards detached from sensors and controllers and acting solely on the data. This mitigates potential issues of malfunctioning sensors and intentional sensor and controller attacks. The data guards understand the expected data, can detect anomalies and can correct them in real-time. This approach adds more guarantees for fault-tolerant behavior in the presence of attacks and sensor failures.

Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services. Maintaining the system's reliability is critical to the continuous delivery of these services. However, the CPS running environment is full of uncertainties and can easily lead to performance degradation. As a result, the need for a recovery technique is highly needed to achieve resilience in the system, with keeping in mind that this technique should be as green as possible. This early doctorate proposal, suggests a game theory solution to achieve resilience and green in CPS. Game theory has been known for its fast performance in decision-making, helping the system to choose what maximizes its payoffs. The proposed game model is described over a real-life collaborative artificial intelligence system (CAIS), that involves robots with humans to achieve a common goal. It shows how the expected results of the system will achieve the resilience of CAIS with minimized CO2 footprint.

A simulation-based optimization framework is developed to con-currently design the system and control parameters to meet de-sired performance and operational resiliency objectives. Leveraging system information from both data and models of varying fideli-ties, a rigorous probabilistic approach is employed for co-design experimentation. Significant economic benefits and resilience im-provements are demonstrated using co-design compared to existing sequential designs for cyber-physical systems.

Considering the evolution of technology, the need to secure data is growing fast. When we turn our attention to the healthcare field, securing data and assuring privacy are critical conditions that must be accomplished. The information is sensitive and confidential, and the exchange rate is very fast. Over the years, the healthcare domain has gradually seen a growth of interest regarding the interconnectivity of different processes to optimize and improve the services that are provided. Therefore, we need intelligent complex systems that can collect and transport sensitive data in a secure way. These systems are called cyber-physical systems. In healthcare domain, these complex systems are named medical cyber physical systems. The paper presents a brief description of the above-mentioned intelligent systems. Then, we focus on wireless sensor networks and the issues and challenges that occur in securing sensitive data and what improvements we propose on this subject. In this paper we tried to provide a detailed overview about cyber-physical systems, medical cyber-physical systems, wireless sensor networks and the security issues that can appear.

Deep web refers to sites that cannot be found by search engines and makes up the 96% of the digital world. The dark web is the part of the deep web that can only be accessed through specialised tools and anonymity networks. To avoid monitoring and control, communities that seek for anonymization are moving to the dark web. In this work, we scrape five dark web forums and construct five graphs to model user connections. These networks are then studied and compared using data mining techniques and social network analysis tools; for each community we identify the key actors, we study the social connections and interactions, we observe the small world effect, and we highlight the type of discussions among the users. Our results indicate that only a small subset of users are influential, while the rapid dissemination of information and resources between users may affect behaviours and formulate ideas for future members.

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

Cyber-Physical Systems (CPS) are complex systems of computational, physical, and human components integrated to achieve some function over one or more networks. The use of distributed simulation, or co-simulation, is one method often used to analyze the behavior and properties of these systems. High-Level Architecture (HLA) is an IEEE co-simulation standard that supports the development and orchestration of distributed simulations. However, a simple HLA federation constructed with the component simulations (i.e., federates) does not satisfy several requirements that arise in real-world use cases such as the shared use of limited physical and computational resources, the need to selectively hide information from participating federates, the creation of reusable federates and federations for supporting configurable shared services, achieving performant distributed simulations, organizing federations across different model types or application concerns, and coordinating federations across organizations with different information technology policies. This paper describes these core requirements that necessitate the use of multiple HLA federations and presents various mechanisms for constructing such integrated HLA federations. An example use case is implemented using a model-based rapid simulation integration framework called the Universal CPS Environment for Federation (UCEF) to illustrate these requirements and demonstrate techniques for integrating multiple HLA federations.

The data sharing is a helpful and financial assistance provided by CC. Information substance security also rises out of it since the information is moved to some cloud workers. To ensure the sensitive and important data; different procedures are utilized to improve access manage on collective information. Here strategies, Cipher text-policyattribute based encryption (CP-ABE) might create it very helpful and safe. The conventionalCP-ABE concentrates on information privacy only; whereas client's personal security protection is a significant problem as of now. CP-ABE byhidden access (HA) strategy makes sure information privacy and ensures that client's protection isn't exposed also. Nevertheless, the vast majority of the current plans are ineffectivein correspondence overhead and calculation cost. In addition, the vast majority of thismechanism takes no thought regardingabilityauthenticationor issue of security spillescapein abilityverificationstage. To handle the issues referenced over, a security protectsCP-ABE methodby proficient influenceauthenticationis presented in this manuscript. Furthermore, its privacy keys accomplish consistent size. In the meantime, the suggestedplan accomplishes the specific safetyin decisional n-BDHE issue and decisional direct presumption. The computational outcomes affirm the benefits of introduced method.

In order to investigate the effect of island divertor on the peak heat load reduction in a tokamak, a new island divertor was developed and installed in J-TEXT tokamak. The engineering design takes into account the complexity of the device based on the physical design, and also needs to ensure the insulation performance of the coil. Before installing the coil, electromagnetic forces on conductors and thermal conditions were simulated, the electromagnetic force on the magnetic island divertor coil will not cause damage to the coil, and there will be no thermal failure behavior.

Traditional magnetic mirrors are appealing because of their comparably simple geometry which lends itself to cost-effective construction. However, magnetic mirrors suffer from several inherent problems that make them poor choices for confining and heating plasmas. The chief concerns are the loss-cone instability which continuously saps hot particles from the trap and the interchange instability which effectively transports hot plasma from the core of the trap to the edges where it is lost to the walls. Centrifugal confinement schemes address these concerns with the addition of supersonic poloidal rotation which can effectively shut off the loss-cone. In addition, velocity shear in the flow may mitigate or even turn off the interchange instability if high enough rotation speeds can be achieved. Previous experiments have verified the efficacy of centrifugal confinement but have been unable to achieve sufficient rotation velocities to entirely shut down the interchange modes. [1] The rotation velocity in these experiments was limited by the Critical-Ionization-Velocity (CIV) instability. [3] We plan an experiment to verify that the CIV is the limiting factor in supersonic plasma centrifuges and to explore strategies for avoiding the CIV limit and achieving sufficient rotation speeds to enable stable plasma confinement.

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

In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients’ needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as “chatbots”, are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13–14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients’ needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients.

Populations move across regions in search of better living possibilities, better life outcomes or going away from problems that affected their lives in the previous region they lived in. In the United States of America, this problem has been happening over decades. Intelligent Conversational Text-based Agents, also called Chatbots, and Artificial Intelligence are increasingly present in our lives and over recent years, their presence has increased considerably, due to the usability cases and the familiarity they are wining constantly. Using NLP algorithms for law in accessible platforms allows scaling of users to access a certain level of law expert who could assist users in need. This paper describes the motivation and circumstances of this problem as well as the description of the development of an Intelligent Conversational Agent system that was used by immigrants in the USA so they could get answers to questions and get suggestions about better legal options they could have access to. This system has helped thousands of people, especially in California

The digital transformation brought on by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles necessary to enable 5G to achieve its promise. 5G trustworthiness highlights the importance of embedding security capabilities from the very beginning while the 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the different layers of 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture within a risk-management framework that takes into account the evolving security-threats landscape. 5G presents a typical use-case of wireless communication and computer networking convergence, where 5G fundamental building blocks include components such as Software Defined Networks (SDN), Network Functions Virtualization (NFV) and the edge cloud. This convergence extends many of the security challenges and opportunities applicable to SDN/NFV and cloud to 5G networks. Thus, 5G security needs to consider additional security requirements (compared to previous generations) such as SDN controller security, hypervisor security, orchestrator security, cloud security, edge security, etc. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability. The working group scope fundamentally addresses the following: •5G security considerations need to overlay and permeate through the different layers of the 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture including a risk management framework that takes into account the evolving security threats landscape. •5G exemplifies a use-case of heterogeneous access and computer networking convergence, which extends a unique set of security challenges and opportunities (e.g., related to SDN/NFV and edge cloud, etc.) to 5G networks. Similarly, 5G networks by design offer potential security benefits and opportunities through harnessing the architecture flexibility, programmability and complexity to improve its resilience and reliability. •The IEEE FNI security WG's roadmap framework follows a taxonomic structure, differentiating the 5G functional pillars and corresponding cybersecurity risks. As part of cross collaboration, the security working group will also look into the security issues associated with other roadmap working groups within the IEEE Future Network Initiative.

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.

Nowadays, microservice architecture is known as a successful and promising architecture for smart city applications. Applying microservices in the designing and implementation of systems has many advantages such as autonomy, loosely coupled, composability, scalability, fault tolerance. However, the complexity of calling between microservices leads to problems in security, accessibility, and data management in the execution of systems. In order to address these challenges, in recent years, various researchers and developers have focused on the use of microservice patterns in the implementation of microservice-based systems. Microservice patterns are the result of developers’ successful experiences in addressing common challenges in microservicebased systems. However, hitherto no guideline has been provided for an in-depth understanding of microservice patterns and how to apply them to real systems. The purpose of this paper is to investigate in detail the most widely used and important microservice patterns in order to analyze the function of each pattern, extract the behavioral signatures and construct a service dependency graph for them so that researchers and enthusiasts use the provided guideline to create a microservice-based system equipped with design patterns. To construct the proposed guideline, five real open source projects have been carefully investigated and analyzed and the results obtained have been used in the process of making the guideline.

The authors' industry experiences suggest that compiler warnings, a lightweight version of program analysis, are valuable early bug detection tools. Significant costs are associated with patches and security bulletins for issues that could have been avoided if compiler warnings were addressed. Yet, the industry's attitude towards compiler warnings is mixed. Practices range from silencing all compiler warnings to having a zero-tolerance policy as to any warnings. Current published data indicates that addressing compiler warnings early is beneficial. However, support for this value theory stems from grey literature or is anecdotal. Additional focused research is needed to truly assess the cost-benefit of addressing warnings.

The strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio Sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN.

IoT connects a large number of physical objects with the Internet that capture and exchange real-time information for service provisioning. Traditional network management schemes face challenges to manage vast amounts of network traffic generated by IoT services. Software-defined networking (SDN) and information-centric networking (ICN) are two complementary technologies that could be integrated to solve the challenges of different aspects of IoT service provisioning. ICN offers a clean-slate design to accommodate continuously increasing network traffic by considering content as a network primitive. It provides a novel solution for information propagation and delivery for large-scale IoT services. On the other hand, SDN allocates overall network management responsibilities to a central controller, where network elements act merely as traffic forwarding components. An SDN-enabled network supports ICN without deploying ICN-capable hardware. Therefore, the integration of SDN and ICN provides benefits for large-scale IoT services. This article provides a comprehensive survey on software-defined information-centric Internet of Things (SDIC-IoT) for IoT service provisioning. We present critical enabling technologies of SDIC-IoT, discuss its architecture, and describe its benefits for IoT service provisioning. We elaborate on key IoT service provisioning requirements and discuss how SDIC-IoT supports different aspects of IoT services. We define different taxonomies of SDIC-IoT literature based on various performance parameters. Furthermore, we extensively discuss different use cases, synergies, and advances to realize the SDIC-IoT concept. Finally, we present current challenges and future research directions of IoT service provisioning using SDIC-IoT.

In recent decennium, hardware security has gained a lot of attention due to different types of attacks being launched, such as IP theft, reverse engineering, counterfeiting, etc. The critical testing infrastructure incorporated into ICs is very popular among attackers to mount side-channel attacks. The IEEE standard 1687 (IJTAG) is one such testing infrastructure that is the focus of attackers these days. To secure access to the IJTAG network, various techniques based on Locking SIB (LSIB) have been proposed. One such very effective technique makes use of Security Linear Feedback Shift Register (SLFSR) along with LSIB. The SLFSR obfuscates the scan chain information from the attacker and hence makes the brute-force attack against LSIB ineffective.In this work, it is shown that the SLFSR based Locking SIB is vulnerable to side-channel attacks. A power analysis attack along with known-plaintext attack is used to determine the IJTAG network structure. First, the known-plaintext attack is used to retrieve the SLFSR design information. This information is further used along with power analysis attack to determine the exact length of the scan chain which in turn breaks the whole security scheme. Further, a countermeasure is proposed to prevent the aforementioned hybrid attack.

Software supply chain attacks occur during the processes of producing software is compromised, resulting in vulnerabilities that target downstream customers. While the number of successful exploits is limited, the impact of these attacks is significant. Despite increased awareness and research into software supply chain attacks, there is limited information available on mitigating or architecting for these risks, and existing information is focused on singular and independent elements of the supply chain. In this paper, we extensively review software supply chain security using software development tools and infrastructure. We investigate the path that attackers find is least resistant followed by adapting and finding the next best way to complete an attack. We also provide a thorough discussion on how common software supply chain attacks can be prevented, preventing malicious hackers from gaining access to an organization's development tools and infrastructure including the development environment. We considered various SSC attacks on stolen code-sign certificates by malicious attackers and prevented unnoticed malware from passing by security scanners. We are aiming to extend our research to contribute to preventing software supply chain attacks by proposing novel techniques and frameworks.