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Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact. [Display omitted] • To design a fast and secure protocol for securely authenticating visually impaired (VI) and disabled people of the telehealth system in the digital world. • To present a secure mechanism that not only protects the privacy of VI and disabled people when transmitting personal sensitive information remotely but also opens up new possibilities for their participation in telehealth. • To confirm the security of the proposed authentication protocol formally through the Real-Or-Random (RoR) model and ProVerif and informally through discussions of known attacks. • To conduct a comprehensive performance analysis of the proposed protocol, considering storage, communication and computation costs in providing a robust and reliable solution. • To comparatively analyze the proposed security mechanism with state-of-the-art mechanisms by scrutinizing its efficacy and robustness based on a set of predefined criteria. This will provide a clear understanding of how our protocol stands against existing solutions. [ABSTRACT FROM AUTHOR]

In today's digital world, cybersecurity has become one of the most pressing issues, as the growing number of connected devices has led to an increase in cyber threats that pose a serious risk to national and international security. In this context, cooperation between countries becomes extremely important to protect sovereignty and ensure cybersecurity at the global level. Ukraine, which has extensive experience in countering cyber threats, is actively involved in international initiatives and cooperation in this area. The article is devoted to the analysis of international cooperation in the field of cybersecurity and Ukraine's role in this process. The study reveals the importance of an effective regulatory framework, in particular the Bug Bounty program and the vulnerability detection mechanism. Ukraine actively cooperates with international partners, which facilitates the exchange of experience and strengthening of cybersecurity. International cooperation with organizations that recognize security as one of their main goals, such as NATO and the European Union, is also of key importance. It is noted that Ukraine, being an active participant in international initiatives and having extensive experience in the field of cybersecurity, continues to play an important role in strengthening global cybersecurity and contributes to the creation of a safer cyberspace for all countries. In the process of ensuring cybersecurity, Ukraine is developing and implementing new programs and strategies, such as Bug Bounty, to help identify and eliminate vulnerabilities in information systems. In addition, an important step is the adoption of the Vulnerability Search and Identification Procedure, which allows business owners and government agencies to request permission to search for weaknesses in their systems. These initiatives allow Ukraine to actively participate in international cybersecurity processes and contribute to improving overall Internet security. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models. [ABSTRACT FROM AUTHOR]

Mobile payment risk has become a critical cybersecurity factor in the cashless society. The outbreak of COVID-19 helped proliferate mobile payments that also bring significant risks to users. Using AI methods, this study analyzed six dimensions of mobile payment risks (financial, privacy, performance, psychology, time, and security) in a survey of 748 respondents from three countries (UK, Taiwan, and Mozambique). The decision tree method was employed to identify and analyze critical perceived risks. The ANOVA test provided insights on the perceived risks between countries. The ANOVA test showed that UK users were concerned about financial and time risks; those in Mozambique were concerned about performance, psychological, and security risks; and those in Taiwan were concerned about privacy risks. The results revealed that decision trees outperformed other methods (such as neural networks, logistic regression, support vector machine (SVM), random forest, and Naïve Bayes models). Performance risk (Taiwan and Mozambique) and security risk (UK) are the most significant factors. Cultural differences influence mobile payment risk perception in different countries. The risk-reduction strategies were also matched to the critical factors by the decision tree. This showed that simplification and risk-sharing strategies were the major tactics in all three countries. The clarification strategy works for Taiwan and Mozambique, which focuses on the benefits of using mobile payments. The results suggest that enterprises should improve and simplify the mobile payment process and collaborate with the third parties to reduce and share cybersecurity risk. [ABSTRACT FROM AUTHOR]

The Internet of Things' (IoT) rapid growth has resulted in a rise in vulnerabilities, making safeguarding IoT systems against intrusions and illegal access a top priority. Intrusion Detection Systems (IDS) are essential for keeping an eye out for irregularities in network traffic. However, the challenge lies in the IDS's ability to detect attacks within high-speed networks while minimizing computational complexity promptly. To improve detection efficiency in IoT networks, we proposed lightweight detection models in this paper that are based on Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and a GRU-based self-attention mechanism. The Grid Search (GS) algorithm optimizes the models by adjusting the hyperparameters, such as the learning rate and the number of hidden units. The proposed models are evaluated using the ToN-IoT dataset. The achieved detection accuracy for all models is as follows: 97% for GRU, 98.1% for LSTM, 98.4% for Bi-LSTM, and 99% for the GRU-based self-attention mechanism. Furthermore, the GRU-based self-attention mechanism has fewer parameters, which leads to a significant saving in classification time of up to 84% compared to GRU. These findings demonstrate that the GRU-based self-attention mechanism is superior in accuracy and computational efficiency, which makes it particularly effective for real-time intrusion detection in IoT networks. [ABSTRACT FROM AUTHOR]

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface "Ambari" was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection. [ABSTRACT FROM AUTHOR]

The increasing reliance on computer networks and blockchain technology has led to a growing concern for cybersecurity and privacy. The emergence of zero-day vulnerabilities and unexpected exploits has highlighted the need for innovative solutions to combat these threats. Bug bounty programs have gained popularity as a cost-effective way to crowdsource the task of identifying vulnerabilities, providing a secure and efficient means of enhancing cybersecurity. This paper provides a comprehensive survey of various free and paid bug bounty programs in the computer networks and blockchain industry, evaluating their effectiveness, impact, and credibility. The study explores the structure, incentives, and nature of vulnerabilities uncovered by these programs, as well as their unique value proposition. A comparative analysis is conducted to identify advantages and disadvantages, highlighting the strengths and weaknesses of each program. The paper also examines the role of ethical hackers in bug bounty programs and their contributions to strengthening cybersecurity and privacy. Finally, the study concludes with recommendations for addressing the challenges faced by bug bounty programs and suggests potential future directions to enhance their impact on computer networks and blockchain security. [ABSTRACT FROM AUTHOR]

Introduction: Traditional network intrusion detection systems (NIDS) face significant challenges in detecting ever-evolving cyber-threats. With the evolution of cyber-attacks, comes a mounting requirement for predictive methods which are competent in identifying both familiar and unknown threats with an effective level of accuracy. Objective: This study is motivated to apply Generative Adversarial Networks (GAN) technology in NIDS for synthetic data generation, so when the NIDS use this technique generates high-quality fake samples which will tremendously increase its accuracy and decrease false positives. In this paper, we aim to evaluate the performance of GAN-boosted NIDS in different environments, such as novel obfuscated and adversarial attacks. Methods: In this study, was developed and trained a GAN by large datasets like UNSW-NB15 and CICIDS2017 using the proposed methodology. The performance of the GAN model was compared with classical machine learning models — Support Vector Machines (SVM) and Random Forests (RF)— via various evaluation metrics such as detection accuracy, false positive rate, and robustness to attacks. Furthermore, expert interviews were included for the qualitative aspects of how practitioners felt about deploying GAN-enhanced NIDS in reality. Results: Along with enhancing detection capabilities, the study will also explore the computational and operational effects of incorporating GAN into existing cybersecurity systems. Findings indicate that the GAN-based system enhances detection accuracy to 95.8% and reduces false positive rate to 2.4%. We additionally discuss the execution of these systems, the necessary deployment process, computing and real-time performance trade-offs, and offer guidance for maximizing resource utilization. The system displayed a performance better than of detecting novel and obfuscated attacks with an accuracy of 88.2%. It also showed resistance to adversarial attacks, keeping detection rates above 90% for various attack vectors. Conclusions: The results indicate that GANs are promising to improve NIDS by increasing its detection and robustness accurately. Nonetheless, improved research and development demands to ensure that GANs meet practical requirements are required due to high computational demands and integration challenges associated with implementation.

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact.

As artificial intelligence (AI) is becoming increasingly integrated into critical infrastructures, it brings about both transformative benefits and unprecedented risks. AI has the potential to revolutionize the efficiency, reliability, and responsiveness of essential services, but it can also offer these benefits along with the vulnerability to a growing array of sophisticated adversarial attacks. This paper explores the evolving landscape of adversarial threats to AI systems, highlighting the potential of nation-state actors to exploit these vulnerabilities for geopolitical gains. A range of adversarial techniques is examined, including dataset poisoning, model stealing, and privacy inference attacks, and their potential impact on sectors such as energy, transportation, healthcare, and water management is assessed. The consequences of successful attacks are substantial, encompassing economic disruption, public safety risks, national security implications, and the erosion of public trust. Given the escalating sophistication of these threats, this paper proposes a comprehensive security framework that includes robust incident response protocols, specialized training, the development of a collaborative ecosystem, and the continuous evaluation of AI systems. The findings of this study 11 underscore the critical need for a proactive approach to AI security in order to safeguard the future of critical infrastructures in an increasingly AI-driven world.

The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users' behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules. [ABSTRACT FROM AUTHOR]

This study explored the integration of blockchain and artificial intelligence technologies to enhance the security framework of electronic voting (e-voting) systems. Amid increasing vulnerabilities and cyber threats to electoral integrity, these technologies provided robust solutions by ensuring the immutability of voting records and enabling real-time anomaly detection. Blockchain technology secured votes in a decentralized, tamper-proof ledger, preventing unauthorized modifications, and enhancing transparency. Concurrently, artificial intelligence leveraged predictive analytics to dynamically monitor and respond to potential security threats, thereby ensuring the reliability and integrity of the voting process. This paper presented a dual-technology approach where blockchain's transparency complemented artificial intelligence's (AI) threat detection capabilities, providing a comprehensive security solution for e-voting systems. Through theoretical models and empirical data, we demonstrated significant improvements in transaction throughput, threat detection accuracy, and system scalability. The findings suggested that the strategic application of these technologies could substantially mitigate current e-voting vulnerabilities, offering a pathway to more secure, transparent, and efficient electoral processes globally. [ABSTRACT FROM AUTHOR]

This article is devoted to the assessment of the country’s sustainable cyberspace strategy by comparing the cyber security strategies of those countries that are economically developed and use advanced information and communication technologies. Based on the components of the National Cyber Security Index, a calculation was made for 10 countries, which made it possible to monitor the effectiveness of their cyber strategies and identify their shortcomings. The calculation is made for the years 2018 and 2021. During this period, no significant critical changes form abnormal indicators, but it is possible to reflect a particular trend. Application of the PROMETHEE method (I and II) and the formation of net flows, determination of importance and comparison of alternatives allowed to carry out an in-depth analysis of various aspects of cyber security, such as protection measures, response to cyber threats and general resistance to cyber-attacks. It is determined that Belgium and Lithuania are the most resistant to threats from cyber-attacks and in the fight against money laundering among the countries considered, as they are rapidly developing and implementing new measures. Their indicators increased by 0.1 values in 2021 compared to 2018. According to the calculations, Great Britain is the most conservative in implementing certain strategies, and its indicators remained at the level of 0-0.2 according to various criteria for the years studied. Most countries improve their indicators by a total of 0.2 values, which reflects their development. Ukraine tends to decrease some indicators by 0.1 value; some are at the same level. The regulatory framework's introduction rate in the cyber security field increases. It shows that Ukraine has many rapidly changing risks, but is trying to achieve flexibility in responding to them. The research findings identify each country’s cybersecurity strengths and weaknesses, as well as potential risks and vulnerabilities. Thanks to this analysis, recommendations are made for developing and implementing effective cyber security strategies, both at the national and international levels. The study contributes to the understanding of the complexity of modern cyber threats. It emphasizes the need for continuous improvement and adaptation of protection measures to ensure the stability and security of financial and information systems in a global digital environment.

The potential breach of access to confidential content hosted in a university's Private Academic Cloud (PAC) underscores the need for developing new protection methods. This paper introduces a Threat Analyzer Software (TAS) and a predictive algorithm rooted in both an operational model and discrete threat recognition procedures (DTRPs). These tools aid in identifying the functional layers that attackers could exploit to embed malware in guest operating systems (OS) and the PAC hypervisor. The solutions proposed herein play a crucial role in ensuring countermeasures against malware introduction into the PAC. Various hypervisor components are viewed as potential threat sources to the PAC's information security (IS). Such threats may manifest through the distribution of malware or the initiation of processes that compromise the PAC's security. The demonstrated counter-threat method, which is founded on the operational model and discrete threat recognition procedures, facilitates the use of mechanisms within the HIPV to quickly identify cyber attacks on the PAC, especially those employing "rootkit" technologies. This prompt identification empowers defenders to take swift and appropriate actions to safeguard the PAC.

Abstract With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA’s potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

Cyber defense solutions that can adapt to new threats and learn to act independently of human guidance are necessary in light of the proliferation of so-called 'next-generation' cyberattacks. Multi-granularity feature aggregation is a method for detecting network intrusions, but its accuracy is often low due to class imbalance and various classifications of intrusions. To address this issue, this model employs a hybrid sampling algorithm composed of ADASYN and repeated edited nearest neighbors (RENN) for sample processing. The feature-discriminative ability of various assaults is improved by employing channel self-attention at the block level during classification. Finally, an enhanced reptile search algorithm (IRSA) is proposed, which uses a sine cosine algorithm and Levy flight to optimally select the weight of the proposed model. The Levy factor boosts the exploitation capabilities of the search agents, and an algorithm with improved global search capabilities prevents local minimal entrapment by undertaking a full-scale search space. To learn binary and multiclass classification, the model was trained on the CIC-IDS 2017, UNSW-NB15, and WSN-DS datasets. Accuracy and falsehood are just some of the evaluation criteria used in the confusion matrix to determine the system's efficacy. Experimental consequences demonstrate a high detection rate, good accuracy, and a relatively low false alarm rate (FAR), validating the efficacy of the suggested approach. Following that, K4 achieved an accuracy score of 81.99, the precision-recall (PR) was 82.69, the detection rate (D.R.) was 82.12, the F1-score was 80.33, and the FAR was 2.3, all in that order. [ABSTRACT FROM AUTHOR]

The article deals with modern technologies and devices of the Internet of Things, features of providing their cybersecurity, current risks and the latest and effective approaches to high-level cyber defense in the architecture of the Internet of things. The problems of cyber threats in modern internet devices are diverse and generate a significant problem on the path of further promotion of IoT technologies. These are various and complex information risks of cybersecurity for processing and transmission processes in the IoT structures and devices that use different technologies of intervention in the main functionality. As the IoT devices and systems use the Internet and interfaces of the Internet, as well as wireless radio interfaces in combination with mobile operating systems based on high -yielding ARM processors, the risks of IoT cybersecurity are growing significantly and put in question the long - term stable functioning of IoT systems. Sometimes these risks of cybersecurity create a significant problem for information data in IoT systems, and significantly slow down their promotion in a number of industries. Therefore, this problem must be resolved. The work has evaluated and analyzed the problems of cybersecurity in the IoT, the existing approaches are considered and new effective practical provisions and approaches to ensure high cyber defense systems and Internet devices are suggested. The aspects have been considered and the analysis of the problems of cybersecurity of the Internet of things using approaches and methods of the improved protection of data transmission in systems and channels of the Internet of things that are connected to modern Internet information network is carried out. Approaches to more protected data transmission in IoTs, based on a comprehensive combination of known technologies with a combination of their aggregate use and symbiosis with other technologies, are proposed. This enables to enhance cybersecurity and reduce the risks of cyber threats in IoT systems that occur and also takes place in other modern information systems that are often connected and guided via the Internet. These approaches and principles allow to increase the reliability and overall protection of data transmission to IoT, evaluate the main factors of the impact of information threats and to reduce the consequences of their implementation. The prospects for the development of these approaches and methods in complex IoT data exchange systems are considered. The advantages of the method and approaches of cyber defense while the transmission of data to IoT and protected cryptographic processing and transmission of information in the devices and information systems of the Internet of things are described. [ABSTRACT FROM AUTHOR]

Copyright of Forum Pedagogiczne / Pedagogical Forum is the property of Uniwersytet Kardynala Stefana Wyszynskiego w Warszawie 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.)

Copyright of Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică is the property of National Institute for Research & Development in Informatics - ICI Bucharest 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.)

Copyright of Strategic Universe Journal / Univers Strategic is the property of Dimitrie Cantemir Christian University, Institute for Security Studies 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 urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]

In our rapidly digitalizing world, the safeguarding of sensitive information stands as a critical concern for organizations across industries. This paper addresses the evolving complexities in data security by advocating the Advanced Encryption Standard (AES) algorithm as a robust defense against the expanding landscape of cyber threats. As financial, healthcare, and educational data transition to digital formats, vulnerabilities in data transmission become more pronounced. To counter these risks and ensure the integrity, confidentiality, and authenticity of essential data, this paper emphasizes the implementation of AES encryption. Thoroughly scrutinizing the widely acknowledged AES algorithm, we highlight its effectiveness and adaptability in securing data. The paper underscores the indispensability of AES in the modern data security milieu, emphasizing its role not only in securing information during transmission and storage but also in decryption, granting authorized users access to protected data. By embracing AES encryption and decryption, organizations can strengthen their defenses against various data-related threats, maintaining the trust and assurance of stakeholders. This work emphasizes the pressing demand for AES encryption and decryption in an era where data security takes precedence, offering valuable insights into their significance and practical application for the protection of crucial digital assets. [ABSTRACT FROM AUTHOR]

This article details Singapore's efforts against disinformation and state-sponsored subversion. The methods examined include both legal and non-legal measures that have been enacted (with the broader canvas, also examined, of what other nations have attempted to shore up their own defences). The article also discusses economic coercion: the issue is not in itself new, but has in recent years been tinged with added potency on account of the wider geopolitical struggle for the commanding heights between the United States and China. Some consideration is also given at the conclusion to the way forward: what nations need to do in the face of emergent challenges (including generative AI) which might further complement the already impressive toolkit of malign actors. [ABSTRACT FROM AUTHOR]

The introduction of the capability to “program” a biological system is referred to as Engineered biology and can be compared to the introduction of the internet and the capability of programming a computer. Engineered biology is supported by a digital infrastructure that includes data, data storage, computer-dependent laboratory equipment, internet-connected communication networks, and supply chains. This connectivity is important. It can improve workflows and enhance productivity. At the same time and unlike computer programs, biological systems introduce unique threats as they can self-assemble, self-repair, and self-replicate. The aim of this paper is to systematically review the cyber implications of engineered biology. This includes cyber-bio opportunities and threats as engineered biology continues to integrate into cyberspace. We used a systematic search methodology to review the academic literature, and supplemented this with a review of opensource materials and “grey” literature that is not disseminated by academic publishers. A comprehensive search of articles published in or after 2017 until the 21st of October 2022 found 52 studies that focus on implications of engineered biology to cyberspace. The search was conducted using search engines that index over 60 databases–databases that specifically cover the information security, and biology literatures, as well as the wider set of academic disciplines. Across these 52 articles, we identified a total of 7 cyber opportunities including automated bio-foundries and 4 cyber threats such as Artificial Intelligence misuse and biological dataset targeting. We highlight the 4 main types of cyberbiosecurity solutions identified in the literature and we suggest a total of 9 policy recommendations that can be utilized by various entities, including governments, to ensure that cyberbiosecurity remains frontline in a growing bioeconomy.