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Distributed systems play a crucial role in today's information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since 'information' has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment. [ABSTRACT FROM AUTHOR]

The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have been actively researched. The threat analysis framework used in existing studies was limited to specific protocols and did not target IoWT devices. In addition, In the literature surveyed to date, no cyber threat analysis framework is targeting IoWT. Therefore, the threat model presented in the existing research on cyber threat analysis and modeling for IoWT is specialized for specific devices. In addition, because it does not present standardized attack tactics and techniques, there is a limitation in that it is difficult to identify attacks quickly. In this paper, we propose an Internet of Wearable Things threat analysis frameWork (IWTW) framework that can derive security threats through systematic analysis of IoWT attack cases and possible security threats and perform cyber threat analysis based on them. The methodology for developing the IWTW framework consists of three steps: Analysis, Standardization, and Compilation. IoWT attack cases and potential security threats are analyzed in the analysis stage. In the standardization stage, attack tactics and techniques derived from the analysis of attack cases and potential security threats are standardized, resulting in 3 attack categories, 18 attack tactics, and 68 attack techniques. In the compilation stage, standardized security threats are combined to develop the IWTW framework ultimately. We present four case studies targeting MiBand 2, Fitbit Charge HR/Surge, Samsung Gear 3, Xiaomi Amazifit, Honor Band 5, Honor Watch ES, and Senbono CF-58 devices to validate the proposed IWTW framework. We analyzed the attack process through a case study and applied the IWTW framework to derive standardized attack categories, tactics, and techniques effectively. By applying the IWTW framework to cyber threat analysis targeting IoWT, security threats can be standardized, and the attack process can be quickly derived, enabling effective attack analysis on IoWT. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Interamericana de Psicología is the property of Sociedad Interamericana de Psicologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: to review the results of the 7th Siberian Anti-Corruption Forum “Incentives and Restrictions in the System of Anticorruption Security” held in Krasnoyarsk on December 15-16, 2023 (organizers: Siberian Federal University, Krasnoyarsk Krai Governor’s Administration, Krasnoyarsk Krai Prosecutor’s Office, Krasnoyarsk Krai Department of the Ministry of Justice of Russia for Krasnoyarsk Krai, Krasnoyarsk Regional Branch of the Russian Lawyers’ Association).Methods: general scientific method of dialectical cognition, as well as a number of private scientific methods, such as historicallegal, system-structural, comparative-legal, formal-logical (deduction, induction, definition and division of the concept), etc.Results: the forum discussed the following key topics: anti-corruption security as an integrative scientific category, the role of incentives and restrictions in the system of its provision; history of development, concept, types and system of anti-corruption incentives and restrictions; international legal regulation of incentives and restrictions in the system of anticorruption security; foreign and Russian experience of resource provision of anti-corruption incentives and restrictions; the role of law enforcement agencies and civil society institutions in providing anti-corruption security; anti-corruption potential of digitalization processes, etc.Scientific novelty: the leading idea of the Forum scientific and practical dialogue was harmonization of anti-corruption incentives and restrictions, since the search for a successful combination of these tools will create legal prerequisites for a qualitative transition to a new model of anti-corruption activities and ensure the security of individuals, society and the state from various corruption threats.Practical significance: based on the Forum results, the author formulated proposals aimed at improving the state policy in the field of combating corruption, as well as the anti-corruption activities of various subjects. These results are of interest to scientists, educators, law enforcement officials, and all those interested in the issues of combating corruption.

Using data from a cross-nationally harmonised correspondence test, we examined how employers in five European labour markets (Britain, Germany, Netherlands, Norway, Spain) respond to applications received from Muslim job seekers with ancestry from 22 different countries of origin. Drawing on the interdisciplinary literature on anti-Muslim prejudice, we expected that callbacks would depend on characteristics of applicants’ origin countries that could signal cultural value incompatibility and political and military oppression, thus triggering perceptions of symbolic and security threats, respectively. The results point to lower callback rates for Muslims, the higher the level of authoritarianism and gender inequality in their origin country. Results for authoritarianism are especially robust across different operationalizations of threat and model specifications. We also find that the association between authoritarianism and callbacks was only statistically significant for men, indicating that Muslim men are especially at risk of exclusion from employment opportunities. [ABSTRACT FROM AUTHOR]

Deep learning models are misled into making false predictions by adversarial attacks that implant tiny perturbations into the original input, which are imperceptible to the human eye. This poses a huge security threat to computer vision systems that are based on deep learning. Compared to digital-domain adversarial attacks, physical-domain adversarial attacks are enabled to introduce perturbations into the input before the adversarial input is captured by the acquisition device and converted into a binary image within the vision system, posing a real security threat to deep learning-based computer vision systems. Optical-based physical-domain adversarial attack techniques, such as those using projected irradiation as a typical example, are more likely to be overlooked and provided negligible protection due to their perturbations being very similar to effects produced by natural environments in the real world. Given their high degree of invisibility and executability, they could pose a significant or even fatal threat to real systems. Based on existing research work, the introduction and discussion of optical-based physical-domain adversarial attack techniques within computer vision systems were presented. The attack scenarios, tools, goals, and performances of these techniques were compared and analyzed. Potential future research directions for optical-based physical-domain adversarial attacks were also discussed.

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of voice data collection, storage, analysis technology, and artificial intelligence technology, voice recognition technology is being used in many fields. In this situation, security threats abusing voice recognition technology have arisen, and the scale and extent of the damage has been increasing recently. Advances in deep learning technology have made it possible to randomly generate or modify voices to create completely different voice data. Security threats using such altered or randomly generated voice data not only cause problems such as invasion of personal privacy, financial fraud, and social chaos, but can also cause serious social problems due to a decrease in mutual trust. Therefore, it must be possible to deny security threats using voice data or identify altered or generated voice data. To this end, this study researches and analyzes technological trends regarding security threats using voice data and proposes response methods and technologies. [ABSTRACT FROM AUTHOR]

Objectives According to the authors, such an assumption requires describing the impact of information security management on the proper, efficient functioning of the organization in cyberspace. Increasingly, the concept of information is becoming a key category, a central point of consideration, which is a set common to research conducted at the interface of various fields and scientific disciplines. Material and methods method of analyzing literature and documents relating to the studied issues and diagnostic survey, participatory observation. The research problems posed: What are the threats in cyberspace for organizations? Hypothesis: Information security management determines the provision of cybersecurity in the organization, which makes it necessary to implement the applicable norms and standards in this area. Results The result was to draw the final conclusion and verify the research hypothesis adopted in the dissertation, which says that information security management determines the provision of cybersecurity in the organization, which makes it necessary to imply the applicable norm and standards in this area. Conclusions One of the aspects of this utility is the level of security of information resources collected and operated by various types of entities. Currently, in organizations, information technologies support a whole range of manufacturing and service processes. Their efficiency is determined by quick and reliable access to the requested information. Hence, information resources – especially shared ones – require appropriate protection mechanisms. Therefore, attention is paid to problems whose solution may determine the survival of the organization in a turbulent environment. Undoubtedly, one of them is the proper use and assurance of information security – as a key resource of modern organizations.

Instantaneous traffic changes in high-speed networks will interfere with abnormal traffic characteristics, making it difficult to accurately identify hidden targets of security threats. This paper designs a high-speed network security threat hidden target recognition method based on attack graph theory. Using the high-speed network traffic reduction method, under the condition that the network topology remains unchanged, the instantaneous input traffic is reduced according to a certain proportion, and after compressing the flow data scale, the abnormal traffic of the high-speed network is identified through the convolutional recurrent neural network, and the information entropy is used to describe the high-speed network. The abnormal traffic characteristics of the network are used as constraints to design an attack graph of hidden targets of high-speed network security threats, and an attack path discovery method based on multi-heuristic information fusion is designed to extract attack paths of high-speed networks, locate attacking hosts, and identify hidden threat targets. In the experiment, the method can accurately identify the hidden targets of high-speed network security threats, and has better identification ability. [ABSTRACT FROM AUTHOR]

Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners' strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves. [ABSTRACT FROM AUTHOR]

The aim of this work paper is to explore illegal migration as a security threat and challenge for contemporary societies and states.Migration, in particular,illegal migrationis a major problem in today's world.Nowadays, migration stands on top of the European Union policy agenda.All in all, massive and 'unwanted' migrants are deemed to threaten the stability of national states.Dealing with migration is an impossible mission for a national state. That is why it is necessary to cooperate and coordinate all the countries that are affected by this issue.As security challenges overcome national borders, they become a challenge that needs to be addressed by more actors, requiring therefore broader patterns of cooperation. Defined as a strategic priority impinging on overall stability and security, the management of migration and especially of illegal migration is considered as a security matter needing coordination and cooperation processes at more levels and with more state and nonstate actors.Dealing with security threats means envisioning the strategies, techniques, tactics and the actors more suited to preserve the stability of the state, in the narrowest sense. Both elements are today more difficult to identify because of the transnational character of the risks and threats.The migration is primarily a product of economic and political crises that push people to leave their states, most often involuntarily. Most migrants come from the middle ranks of the social hierarchy of the sending society. The people at the top of the social hierarchy have little motivation to move and those at the bottom lack the wherewithal to consider migration. The numbers of refugees are large, both historically and at present, often numbering in the millions. Any public policy that engages migration must include the flight of refugees as a central issue. [ABSTRACT FROM AUTHOR]

Since the end of the Cold War, the notion of global security, and presumed threats to it, has undergone considerable expansion and diversification. This process has been led by the United Nations Security Council (UNSC), where active deliberations concerning "threat(s) to the peace" have taken place among major international actors. Despite a sizable accumulation of scholarly arguments, however, the defining features of the structure and dynamics of the post-Cold War security discourse remain ambiguous. To address these ambiguities, this study investigates the entire body of Council deliberations over the past three decades. Based on an original dataset consisting of policy statements delivered at the UNSC, the study employs quantitative text analysis tools, including word embedding, to examine how council members have conceived the notion of security threat in terms of the various issues and entities discussed. It shows the security discourse at the UNSC to be highly stratified and reveals the persistent and pervasive influence of terrorism and the proliferation of weapons of mass destruction, which constitute the limited common grounds shared by the Council's permanent members. These findings caution against the unconstrained use of certain theoretical constructs widely employed in other works, most notably, "securitization" and "interpretative community." [ABSTRACT FROM AUTHOR]

In this study, the performance indicators of Multiservice Telecommunication Systems (MTS) based on Future Networks (FN) using Software-Defined Networking (SDN) technologies are analyzed. In SDN, the network management enables a dynamic and programmatically efficient network configuration to improve network performance and monitoring, thus making it more like cloud computing than traditional network management approach. Based on the study of MTS using the architectural concepts of Future networks, a mathematical model of SDN’s is proposed. Analytical expressions have been obtained that make it possible to evaluate the indicators of service quality, information security and fault tolerance of the system in the provision of multimedia services.

Phishing is a security threat with serious effects on individuals as well as on the targeted brands. Although this threat has been around for quite a long time, it is still very active and successful. In fact, the tactics used by attackers have been evolving continuously in the years to make the attacks more convincing and effective. In this context, phishing detection is of primary importance. The literature offers many diverse solutions that cope with this issue and in particular with the detection of phishing websites. This paper provides a broad and comprehensive review of the state of the art in this field by discussing the main challenges and findings. More specifically, the discussion is centered around three important categories of detection approaches, namely, list-based, similarity-based and machine learning-based. For each category we describe the detection methods proposed in the literature together with the datasets considered for their assessment and we discuss some research gaps that need to be filled.

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges. The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems (CPPSs). This paper presents a comprehensive review of some of the latest attack detection and defense strategies. Firstly, the vulnerabilities brought by some new information and communication technologies (ICTs) are analyzed, and their impacts on the security of CPPSs are discussed. Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework, and their features and negative impacts are discussed. Secondly, two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed, and their benefits and drawbacks are discussed. Moreover, two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed. Finally, the trends and challenges in attack detection and defense strategies in CPPSs are provided.

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.

Industry Revolution 4.0 connects the Internet of Things (IoT) resource-constrained devices to Smart Factory solutions and delivers insights. As a result, a complex and dynamic network with a vulnerability inherited from the Internet becomes an attractive target for hackers to attack critical infrastructures. Therefore, this paper selects three potential attacks with the evaluation of the protections, namely (1) distributed denial of service (DDoS), (2) address resolution protocol (ARP) spoofing, and (3) Internet protocol (IP) fragmentation attacks. In the DDoS protection, the F1-score, accuracy, precision, and recall of the four-feature random forest with principal component analysis (RFPCA) model are 95.65%, 97%, 97.06%, and 94.29%, respectively. In the ARP spoofing, a batch processing method adopts the entropy calculated in the 20 s window with sensitivity to network abnormalities detection of various ARP spoofing scenarios involving victims' traffic. The detected attacker's MAC address is inserted in the block list to filter malicious traffic. The proposed protection in the IP fragmentation attack is implementing one-time code (OTC) and timestamp fields in the packet header. The simulation shows that the method detected 160 fake fragments from attackers among 2040 fragments. [ABSTRACT FROM AUTHOR]

The phenomenon known as cybercrime is gaining momentum over time, becoming an increasing threat to the security of the twenty-first century. The problem is growing and is becoming a growing challenge both for the legislator, who is required to take steps in the field of legal regulations, and for law enforcement agencies, which are expected not only to ensure security, but also to counteract the phenomenon of cybercrime. It is not the right question if, but when, a cyberattack will occur. It is necessary to be properly prepared for its early identification and proper reaction. This reality places new demands on the organization, which is currently forced to design and operate information processing systems in secure cyberspace. The huge impact on information security organizations cannot be underestimated, every smallest decision in the organization is based on the flow of information, including in ICT systems. Taking into account the above, information was taken into account as a decisive factor determining the functioning of the organization. According to the authors, such an assumption requires describing the impact of information security management on the proper, efficient functioning of the organization in cyberspace. Increasingly, the concept of information is becoming a key category, a central point of consideration, which is a set common to research conducted at the interface of various fields and scientific disciplines. When discussing the organization of its operation and functioning, it is impossible to undertake this discussion without specifying the role of information in it, including that processed in ICT systems. The aim of this article is to answer the research problems posed: 1. How is cybercrime shaping up in Poland? 2. What are the threats in cyberspace for organizations? 3. How do organizations counter threats in cyberspace? 4. How organizations should counter threats in cyberspace 5. How were the solutions of the EU Directive transposed into the national legal order? The formulation of research problems made it possible to generate the main adopted goal of the research, which is to analyze the solutions adopted in organizations and their impact on cyberspace security. In addition, the issue was presented, in terms of the practical application of the applicable standards in this area. As part of the research, the method of analyzing literature and documents relating to the studied issues and diagnostic survey was used. In addition, as part of the participatory observation, experience from participation in the implementations of the solutions described in the article was indicated and, on their basis, the following research hypothesis was formulated: Information security management determines the provision of cybersecurity in the organization, which makes it necessary to implement the applicable norms and standards in this area. [ABSTRACT FROM AUTHOR]

Edge computing has its application in a lot of areas now, but with the increasing popularity and benefits, it suffers from some challenges such as data privacy and security. Intruder attacks should be prevented and only authentic users should have access to data storage. Most of the authentication techniques apply some trusted entity to undergo the process. Users and servers both have to be registered in the trusted entity to get permission of authenticating other users. In this scenario, the entire system depends on a single trusted entity; so, a single point of failure can cause the failure of the total system, and scalability issues are there also. To address these issues remaining in the existing systems, in this paper, a decentralized approach has been discussed which is capable of eliminating the concept of a single trusted entity by introducing a blockchain paradigm in edge computing where every time a user or server wants to enter the system, it does not have to register itself manually, but the authentication process is carried out throughout the scheme automatically. Experimental results and performance analysis prove that the proposed architecture is definitely beneficial and it outperforms the existing ones in the concerned domain. [ABSTRACT FROM AUTHOR]

As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial example (AE) and perform a more efficient query should be further explored. This study aims to use the architecture of GAN combined with the model-stealing attack to train surrogate models and generate high-quality AE. This study proposes an image AE generation method based on the generative adversarial networks with dual discriminators and a single generator (DDSG-GAN) and designs the corresponding loss function for each model. The generator can generate adversarial perturbation, and two discriminators constrain the perturbation, respectively, to ensure the visual quality and attack effect of the generated AE. We extensively experiment on MNIST, CIFAR10, and Tiny-ImageNet datasets. The experimental results illustrate that our method can effectively use query feedback to generate an AE, which significantly reduces the number of queries on the target model and can implement effective attacks. [ABSTRACT FROM AUTHOR]

With the increasing risks of Internet of vehicles (IoV) attack, the network security threats of vehicle-mounted systems, vehicle-mounted terminals, vehicle-mounted information and service applications, the operation and service platform of intelligent connected vehicles (ICVs) are prominent.Information tampering and virus intrusion in the generalized network attack have been proved to be suitable for the attack of ICVs.The characteristics of weak password authentication and weak encryption in traditional IoV are hard to satisfy the current requirements of multi-network and multi-node security protection in the field of IoV.In addition, the lack of domestic security encryption authentication mechanism and the imperfect encryption authentication system make it more difficult to satisfy the requirements of IoV security.To solve the problem of IoV security encryption authentication, the paper studies IoV security encryption authentication technology in the age of big data.Firstly, this paper introduces the current situation and relevant concepts of IoV security in the era of big data.Then it contrasts and analyzes the current IoV security architecture, and puts forward IoV security encryption authentication system in the era of big data, and elaborates systematically the IoV security technology architecture and the encryption authentication way of IoV communication module.Then the architecture proposed in this paper is compared with the information security standards of the IoV and elaborates key technology and innovations of the IoV security encryption authentication.Finally, the paper summarizes and proposes the problems and challenges faced by the current security encryption authentication technology of IoV.

Over the past years, the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life. However, using machine learning in intelligent networks also presents potential security and privacy threats. A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model. In this survey, we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time. We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms, and analyze the strengths and limitations of corresponding defense methods in a compact form. We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

In this study, the performance indicators of Multiservice Telecommunication Systems (MTS) based on Future Networks (FN) using SoftwareDefined Networking (SDN) technologies are analyzed. In SDN, the network management enables a dynamic and programmatically efficient network configuration to improve network performance and monitoring, thus making it more like cloud computing than traditional network management approach. Based on the study of MTS using the architectural concepts of Future networks, a mathematical model of SDN’s is proposed. Analytical expressions have been obtained that make it possible to evaluate the indicators of service quality, information security and fault tolerance of the system in the provision of multimedia services. [ABSTRACT FROM AUTHOR]

Surveillance policies aimed at combating terrorism and improving public security can also lead to constraints on civil liberties. In view of this trade‐off between the potential benefits and risks of surveillance, it is particularly important to study how effectiveness considerations shape public support for surveillance. We argue that effectiveness perceptions enhance policy support, but that the manner in which citizens view policy effectiveness depends on their perceptions of threats related to terrorism and to violations of civil liberties. Using data from a factorial survey experiment in Germany, we show that policy effectiveness is the most relevant predictor of citizens' support for different surveillance measures. Moreover, we find evidence that depending on the scope of surveillance, respondents perceiving threats to their liberty rely much less heavily on policy effectiveness as a criterion for evaluating policies, compared to those whose threat perception is low. Related Articles: Awan, Imran. 2012."'I Am a Muslim Not an Extremist': How the Prevent Strategy Has Constructed a 'Suspect' Community." Politics & Policy 40(6): 1158–85. https://doi.org/10.1111/j.1747‐1346.2012.00397.x. Doan, Alesha E., and Corinne Schwarz. 2020. "Father Knows Best: 'Protecting' Women through State Surveillance and Social Control in Anti‐Abortion Policy." Politics & Policy 48(1): 6–37. https://doi.org/10.1111/polp.12337. Jenkins‐Smith, Hank C., and Kerry G. Herron. 2009. "Rock and a Hard Place: Public Willingness to Trade Civil Rights and Liberties for Greater Security." Politics & Policy 37(5): 1095–129. http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2009.00215.x/abstract. [ABSTRACT FROM AUTHOR]

Scalable and secure authorization of smart things is of the crucial essence for the successful deployment of the Internet of Things (IoT). Unauthorized access to smart things could exacerbate the security and privacy concern, which could, in turn, lead to the reduced adoption of the IoT, and ultimately to the emergence of severe threats. Even though there are a variety of IoT solutions for secure authorization, authorization schemes in highly dynamic distributed environments remain a daunting challenge. Access rights can dynamically change due to the heterogeneous nature of shared IoT devices and, thus, the identity and access control management are challenging. This survey provides a comprehensive comparative analysis of the current state-of-the-art IoT authorization schemes to highlight their strengths and weaknesses. Then, it defines the most important requirements and highlights the authorization threats and weaknesses impacting authorization in the IoT. Finally, the survey presents the ongoing open authorization challenges and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

Shared traumatic reality describes situations in which helpers and those they help are subject to the same traumatic events. This qualitative study examined perceptions among 16 school counselors who live and work in Israeli communities located along the border with the Gaza Strip. Data were collected through semi-structured interviews and analyzed thematically. The counselors described security tensions that threaten to disrupt their daily routine, harm their emotional well-being and negatively affect their family life. They noted exposure to traumatic incidents, emotional flooding, overload and tension. They reported having difficulty distinguishing between their professional and personal lives, primarily because they had to help and support their students while also functioning as parents. [ABSTRACT FROM AUTHOR]

Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to the input before it is captured and converted to a image inside the vision system, when compared to digital adversarial attacks. In this paper, we focus on physical adversarial attacks and further classify them into invasive and non-invasive. Optical-based physical adversarial attack techniques (e.g. using light irradiation) belong to the non-invasive category. The perturbations can be easily ignored by humans as the perturbations are very similar to the effects generated by a natural environment in the real world. With high invisibility and executability, optical-based physical adversarial attacks can pose a significant or even lethal threat to real systems. This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques. [ABSTRACT FROM AUTHOR]

In the era of big data, artificial intelligence is widely used in computer network technology, which makes the functional and structural layers of computer network system comprehensively optimized. The use of natural language generation, speech recognition and virtual assistants make people's lives easier and faster. At the same time, the application of big data artificial intelligence has effectively strengthened the construction of computer network security system, and realized the high-end, intelligent and automatic operation of computer network. In recent years, major network security incidents have occurred frequently, and network attacks and security threats are everywhere. Security threats in cyberspace include physical infrastructure, network information systems and social media security information. In personal information and property security and so on constitute a serious threat. Strategies that empower artificial intelligence to respond to cyber attacks and security threats have led to significant changes in cyberspace security. The research on coping strategies required by the combination of artificial intelligence and cyberspace security plays a very important role in promoting the development of artificial intelligence and network security. [ABSTRACT FROM AUTHOR]