Biblio

Currently, air pollution is still a problem that requires special attention, especially in big cities. Air pollution can come from motor vehicle fumes, factory smoke or other particles. To overcome these problems, a system is made that can monitor environmental conditions in order to know the good and bad of air quality in an environment and is expected to be a solution to reduce air pollution that occurs. The system created will utilize the Wireless Sensor Network (WSN) combined with Waspmote Smart Environment PRO, so that later data will be obtained in the form of temperature, humidity, CO levels and CO2 levels. From the sensor data that has been processed on Waspmote, it will then be used as input for data processing using a fuzzy algorithm. The classification obtained from sensor data processing using fuzzy to monitor environmental conditions there are 5 classifications, namely Very Good, Good, Average, Bad and Dangerous. Later the data that has been collected will be distributed to Meshlium as a gateway and will be stored in the database. The process of sending information between one party to another needs to pay attention to the confidentiality of data and information. The final result of the implementation of this research is that the system is able to classify values using fuzzy algorithms and is able to secure text data that will be sent to the database via Meshlium, and is able to display data sent to the website in real time.

Deep learning have a variety of applications in different fields such as computer vision, automated self-driving cars, natural language processing tasks and many more. One of such deep learning adversarial architecture changed the fundamentals of the data manipulation. The inception of Generative Adversarial Network (GAN) in the computer vision domain drastically changed the way how we saw and manipulated the data. But this manipulation of data using GAN has found its application in various type of malicious activities like creating fake images, swapped videos, forged documents etc. But now, these generative models have become so efficient at manipulating the data, especially image data, such that it is creating real life problems for the people. The manipulation of images and videos done by the GAN architectures is done in such a way that humans cannot differentiate between real and fake images/videos. Numerous researches have been conducted in the field of deep fake detection. In this paper, we present a structured survey paper explaining the advantages, gaps of the existing work in the domain of deep fake detection.

One of the essential features of confidential computing is the ability to attest to an application remotely. Remote attestation ensures that the right code is running in the correct environment. We need to ensure that all components that an adversary might use to impact the integrity, confidentiality, and consistency of an application are attested. Which components need to be attested is defined with the help of a policy. Verification of the policy is performed with the help of an attestation engine. Since remote attestation bootstraps the trust in remote applications, any vulnerability in the attestation mechanism can therefore impact the security of an application. Moreover, mistakes in the attestation policy can result in data, code, and secrets being vulnerable. Our work focuses on 1) how we can verify the attestation mechanisms and 2) how to verify the policy to ensure that data, code, and secrets are always protected.

Many organizations process and store classified data within their computer networks. Owing to the value of data that they hold; such organizations are more vulnerable to targets from adversaries. Accordingly, the sensitive organizations resort to an ‘air-gap’ approach on their networks, to ensure better protection. However, despite the physical and logical isolation, the attackers have successfully manifested their capabilities by compromising such networks; examples of Stuxnet and Agent.btz in view. Such attacks were possible due to the successful manipulation of human beings. It has been observed that to build up such attacks, persistent reconnaissance of the employees, and their data collection often forms the first step. With the rapid integration of social media into our daily lives, the prospects for data-seekers through that platform are higher. The inherent risks and vulnerabilities of social networking sites/apps have cultivated a rich environment for foreign adversaries to cherry-pick personal information and carry out successful profiling of employees assigned with sensitive appointments. With further targeted social engineering techniques against the identified employees and their families, attackers extract more and more relevant data to make an intelligent picture. Finally, all the information is fused to design their further sophisticated attacks against the air-gapped facility for data pilferage. In this regard, the success of the adversaries in harvesting the personal information of the victims largely depends upon the common errors committed by legitimate users while on duty, in transit, and after their retreat. Such errors would keep on repeating unless these are aligned with their underlying human behaviors and weaknesses, and the requisite mitigation framework is worked out.

The Internet of Things (IoT) technology has revolutionized the world where anything is smartly connected and is accessible. The IoT makes use of cloud computing for processing and storing huge amounts of data. In some way, the concept of fog computing has emerged between cloud and IoT devices to address the issue of latency. When a fog node exchanges data for completing a particular task, there are many security and privacy risks. For example, offloading data to a rogue fog node might result in an illegal gathering or modification of users' private data. In this paper, we rely on trust to detect and detach bad fog nodes. We use a Mamdani fuzzy method and we consider a hospital scenario with many fog servers. The aim is to identify the malicious fog node. Metrics such as latency and distance are used in evaluating the trustworthiness of each fog server. The main contribution of this study is identifying how fuzzy logic configuration could alter the trust value of fog nodes. The experimental results show that our method detects the bad fog device and establishes its trustworthiness in the given scenario.

Given the COVID-19 pandemic, this paper aims at providing a full-process information system to support the detection of pathogens for a large range of populations, satisfying the requirements of light weight, low cost, high concurrency, high reliability, quick response, and high security. The project includes functional modules such as sample collection, sample transfer, sample reception, laboratory testing, test result inquiry, pandemic analysis, and monitoring. The progress and efficiency of each collection point as well as the status of sample transfer, reception, and laboratory testing are all monitored in real time, in order to support the comprehensive surveillance of the pandemic situation and support the dynamic deployment of pandemic prevention resources in a timely and effective manner. Deployed on a cloud platform, this system can satisfy ultra-high concurrent data collection requirements with 20 million collections per day and a maximum of 5 million collections per hour, due to its advantages of high concurrency, elasticity, security, and manageability. This system has also been widely used in Jiangsu, Shaanxi provinces, for the prevention and control of COVID-19 pandemic. Over 100 million NAT data have been collected nationwide, providing strong informational support for scientific and reasonable formulation and execution of COVID-19 prevention plans.

The amount of information that is shared regularly has increased as a direct result of the rapid development of network administrators, Web of Things-related devices, and online users. Cybercriminals constantly work to gain access to the data that is stored and transferred online in order to accomplish their objectives, whether those objectives are to sell the data on the dark web or to commit another type of crime. After conducting a thorough writing analysis of the causes and problems that arise with wireless networks’ security and privacy, it was discovered that there are a number of factors that can make the networks unpredictable, particularly those that revolve around cybercriminals’ evolving skills and the lack of significant bodies’ efforts to combat them. It was observed. Wireless networks have a built-in security flaw that renders them more defenceless against attack than their wired counterparts. Additionally, problems arise in networks with hub mobility and dynamic network geography. Additionally, inconsistent availability poses unanticipated problems, whether it is accomplished through mobility or by sporadic hub slumber. In addition, it is difficult, if not impossible, to implement recently developed security measures due to the limited resources of individual hubs. Large-scale problems that arise in relation to wireless networks and flexible processing are examined by the Wireless Correspondence Network Security and Privacy research project. A few aspects of security that are taken into consideration include confirmation, access control and approval, non-disavowal, privacy and secrecy, respectability, and inspection. Any good or service should be able to protect a client’s personal information. an approach that emphasises quality, implements strategy, and uses a poll as a research tool for IT and public sector employees. This strategy reflects a higher level of precision in IT faculties.

Onion Routing is an encrypted communication system developed by the U.S. Naval Laboratory that uses existing Internet equipment to communicate anonymously. Miscreants use this means to conduct illegal transactions in the dark web, posing a security risk to citizens and the country. For this means of anonymous communication, website fingerprinting methods have been used in existing studies. These methods often have high overhead and need to run on devices with high performance, which makes the method inflexible. In this paper, we propose a lightweight method to address the high overhead problem that deep learning website fingerprinting methods generally have, so that the method can be applied on common devices while also ensuring accuracy to a certain extent. The proposed method refers to the structure of Inception net, divides the original larger convolutional kernels into smaller ones, and uses group convolution to reduce the website fingerprinting and computation to a certain extent without causing too much negative impact on the accuracy. The method was experimented on the data set collected by Rimmer et al. to ensure the effectiveness.

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

We present an online framework for learning and updating security policies in dynamic IT environments. It includes three components: a digital twin of the target system, which continuously collects data and evaluates learned policies; a system identification process, which periodically estimates system models based on the collected data; and a policy learning process that is based on reinforcement learning. To evaluate our framework, we apply it to an intrusion prevention use case that involves a dynamic IT infrastructure. Our results demonstrate that the framework automatically adapts security policies to changes in the IT infrastructure and that it outperforms a state-of-the-art method.

Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading.

The main intention of edge computing is to improve network performance by storing and computing data at the edge of the network near the end user. However, its rapid development largely ignores security threats in large-scale computing platforms and their capable applications. Therefore, Security and privacy are crucial need for edge computing and edge computing based environment. Security vulnerabilities in edge computing systems lead to security threats affecting edge computing networks. Therefore, there is a basic need for an intrusion detection system (IDS) designed for edge computing to mitigate security attacks. Due to recent attacks, traditional algorithms may not be possibility for edge computing. This article outlines the latest IDS designed for edge computing and focuses on the corresponding methods, functions and mechanisms. This review also provides deep understanding of emerging security attacks in edge computing. This article proves that although the design and implementation of edge computing IDS have been studied previously, the development of efficient, reliable and powerful IDS for edge computing systems is still a crucial task. At the end of the review, the IDS developed will be introduced as a future prospect.

Biometric security is the fastest growing area that receives considerable attention over the past few years. Digital hiding and encryption technologies provide an effective solution to secure biometric information from intentional or accidental attacks. Visual cryptography is the approach utilized for encrypting the information which is in the form of visual information for example images. Meanwhile, the biometric template stored in the databases are generally in the form of images, the visual cryptography could be employed effectively for encrypting the template from the attack. This study develops a share creation with improved encryption process for secure biometric verification (SCIEP-SBV) technique. The presented SCIEP-SBV technique majorly aims to attain security via encryption and share creation (SC) procedure. Firstly, the biometric images undergo SC process to produce several shares. For encryption process, homomorphic encryption (HE) technique is utilized in this work. To further improve the secrecy, an improved bald eagle search (IBES) approach was exploited in this work. The simulation values of the SCIEP-SBV system are tested on biometric images. The extensive comparison study demonstrated the improved outcomes of the SCIEP-SBV technique over compared methods.

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.

Web browsers are among the most important but also complex software solutions to access the web. It is therefore not surprising that web browsers are an attractive target for attackers. Especially in the last decade, security researchers and browser vendors have developed sandboxing mechanisms like security-relevant HTTP headers to tackle the problem of getting a more secure browser. Although the security community is aware of the importance of security-relevant HTTP headers, legacy applications and individual requests from different parties have led to possible insecure configurations of these headers. Even if specific security headers are configured correctly, conflicts in their functionalities may lead to unforeseen browser behaviors and vulnerabilities. Recently, the first work which analyzed duplicated headers and conflicts in headers was published by Calzavara et al. at USENIX Security [1]. The authors focused on inconsistent protections by using both, the HTTP header X-Frame-Options and the framing protection of the Content-Security-Policy.We extend their work by analyzing browser behaviors when parsing duplicated headers, conflicting directives, and values that do not conform to the defined ABNF metalanguage specification. We created an open-source testbed running over 19,800 test cases, at which nearly 300 test cases are executed in the set of 66 different browsers. Our work shows that browsers conform to the specification and behave securely. However, all tested browsers behave differently when it comes, for example, to parsing the Strict-Transport-Security header. Moreover, Chrome, Safari, and Firefox behave differently if the header contains a character, which is not allowed by the defined ABNF. This results in the protection mechanism being fully enforced, partially enforced, or not enforced and thus completely bypassable.

Managing electricity effectively also means knowing as accurately as possible when, where and how electricity is used. Detailed metering and timely allocation of consumption can help identify specific areas where energy consumption is excessive and therefore requires action and optimization. All those interested in the measurement process (distributors, sellers, wholesalers, managers, ultimately customers and new prosumer figures - producers / consumers -) have an interest in monitoring and managing energy flows more efficiently, in real time.Smart meter plays a key role in sending data containing consumer measurements to both the producer and the consumer, thanks to chain 2. It allows you to connect consumption and production, during use and the customer’s identity, allowing billing as Time-of-Use or Real-Time Pricing, and through the new two-way channel, this information is also made available to the consumer / prosumer himself, enabling new services such as awareness of energy consumption at the very moment of energy use.This is made possible by latest generation devices that "talk" with the end user, which use chain 2 and the power line for communication.However, the implementation of smart meters and related digital technologies associated with the smart grid raises various concerns, including, privacy. This paper provides a comparative perspective on privacy policies for residential energy customers, moreover, it will be possible to improve security through the blockchain for the introduction of smart contracts.

E-health, smart health and telemedicine are examples of sophisticated healthcare systems. For end-to-end communication, these systems rely on digital medical information. Although this digitizing saves much time, it is open source. As a result, hackers could potentially manipulate the digital medical image as it is being transmitted. It is harder to diagnose an actual disease from a modified digital medical image in medical diagnostics. As a result, ensuring the security and confidentiality of clinical images, as well as reducing the computing time of encryption algorithms, appear to be critical problems for research groups. Conventional approaches are insufficient to ensure high-level medical image security. So this review paper focuses on depicting advanced methods like DNA cryptography and Chaotic Map as advanced techniques that could potentially help in encrypting the digital image at an effective level. This review acknowledges the key accomplishments expressed in the encrypting measures and their success indicators of qualitative and quantitative measurement. This research study also explores the key findings and reasons for finding the lessons learned as a roadmap for impending findings.

This study contributes to the analysis of civil society and knowledge by examining mobilizations by civil society organizations and grassroots networks in opposition to wireless smart meters in the United States. Three types of mobilizations are reviewed: grassroots anti-smart-meter networks, privacy organizations, and organizations that advocate for reduced exposure to non-ionizing electromagnetic fields. The study shows different relationships to scientific knowledge that include publicizing risks and conducting citizen science, identifying non-controversial areas of future research, and pointing to deeper problems of undone science (a particular type of non-knowledge that emerges when actors mobilize in the public interest and find an absence or low volume of research that could have been used to support their concerns). By comparing different types of knowledge claims made by the civil society organizations and networks, the study examines the conditions under which mobilized civil society generates positive responses from incumbent organizations versus resistance and undone science.

Active consumers have now been empowered thanks to the smart grid concept. To avoid fossil fuels, the demand side must provide flexibility through Demand Response events. However, selecting the proper participants for an event can be complex due to response uncertainty. The authors design a Contextual Consumer Rate to identify the trustworthy participants according to previous performances. In the present case study, the authors address the problem of new players with no information. In this way, two different methods were compared to predict their rate. Besides, the authors also refer to the consumer privacy testing of the dataset with and without information that could lead to the participant identification. The results found to prove that, for the proposed methodology, private information does not have a high impact to attribute a rate.

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards.

How can high-level directives concerning risk, cybersecurity and compliance be operationalized in the central nervous system of any organization above a certain complexity? How can the effectiveness of technological solutions for security be proven and measured, and how can this technology be aligned with the governance and financial goals at the board level? These are the essential questions for any CEO, CIO or CISO that is concerned with the wellbeing of the firm. The concept of Zero Trust (ZT) approaches information and cybersecurity from the perspective of the asset to be protected, and from the value that asset represents. Zero Trust has been around for quite some time. Most professionals associate Zero Trust with a particular architectural approach to cybersecurity, involving concepts such as segments, resources that are accessed in a secure manner and the maxim “always verify never trust”. This paper describes the current state of the art in Zero Trust usage. We investigate the limitations of current approaches and how these are addressed in the form of Critical Success Factors in the Zero Trust Framework developed by ON2IT ‘Zero Trust Innovators’ (1). Furthermore, this paper describes the design and engineering of a Zero Trust artefact that addresses the problems at hand (2), according to Design Science Research (DSR). The last part of this paper outlines the setup of an empirical validation trough practitioner oriented research, in order to gain a broader acceptance and implementation of Zero Trust strategies (3). The final result is a proposed framework and associated technology which, via Zero Trust principles, addresses multiple layers of the organization to grasp and align cybersecurity risks and understand the readiness and fitness of the organization and its measures to counter cybersecurity risks.

Payment Service Providers (PSP) enable application developers to effortlessly integrate complex payment processing code using software development toolkits (SDKs). While providing SDKs reduces the risk of application developers introducing payment vulnerabilities, vulnerabilities in the SDKs themselves can impact thousands of applications. In this work, we propose a static analysis tool for assessing PSP SDKs using OWASP’s MASVS industry standard for mobile application security. A key challenge for the work was reapplying both the MASVS and program analysis tools designed to analyze whole applications to study only a specific SDK. Our preliminary findings show that a number of payment processing libraries fail to meet MASVS security requirements, with evidence of persisting sensitive data insecurely, using outdated cryptography, and improperly configuring TLS. As such, our investigation demonstrates the value of applying security analysis at SDK granularity to prevent widespread deployment of vulnerable code.

We propose new, more efficient targeted whitebox attacks against deep neural networks. Our attacks better align with the attacker’s goal: (1) tricking a model to assign higher probability to the target class than to any other class, while (2) staying within an -distance of the attacked input. First, we demonstrate a loss function that explicitly encodes (1) and show that Auto-PGD finds more attacks with it. Second, we propose a new attack method, Constrained Gradient Descent (CGD), using a refinement of our loss function that captures both (1) and (2). CGD seeks to satisfy both attacker objectives—misclassification and bounded `p-norm—in a principled manner, as part of the optimization, instead of via ad hoc postprocessing techniques (e.g., projection or clipping). We show that CGD is more successful on CIFAR10 (0.9–4.2%) and ImageNet (8.6–13.6%) than state-of-the-art attacks while consuming less time (11.4–18.8%). Statistical tests confirm that our attack outperforms others against leading defenses on different datasets and values of .

Self-adaptive systems commonly operate in heterogeneous contexts and need to consider multiple quality attributes. Human stakeholders often express their quality preferences by defining utility functions, which are used by self-adaptive systems to automatically generate adaptation plans. However, the adaptation space of realistic systems is large and it is obscure how utility functions impact the generated adaptation behavior, as well as structural, behavioral, and quality constraints. Moreover, human stakeholders are often not aware of the underlying tradeoffs between quality attributes. To address this issue, we present an approach that uses machine learning techniques (dimensionality reduction, clustering, and decision tree learning) to explain the reasoning behind automated planning. Our approach focuses on the tradeoffs between quality attributes and how the choice of weights in utility functions results in different plans being generated. We help humans understand quality attribute tradeoffs, identify key decisions in adaptation behavior, and explore how differences in utility functions result in different adaptation alternatives. We present two systems to demonstrate the approach’s applicability and consider its potential application to 24 exemplar self-adaptive systems. Moreover, we describe our assessment of the tradeoff between the information reduction and the amount of explained variance retained by the results obtained with our approach.

In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem. Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging. While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements. Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to see the forest for the trees. In this paper we present ExTrA (Explaining Tradeoffs of software Architecture design spaces), an approach to analyzing architectural design spaces that addresses these limitations and provides a basis for explaining design tradeoffs. Our approach employs dimensionality reduction techniques employed in machine learning pipelines like Principal Component Analysis (PCA) and Decision Tree Learning (DTL) to enable architects to understand how design decisions contribute to the satisfaction of extra-functional properties across the design space. Our results show feasibility of the approach in two case studies and evidence that combining complementary techniques like PCA and DTL is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.