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The Internet of Things (IoT) nodes consist of sensors that collect environmental data and then perform data exchange with surrounding nodes and gateways. Cybersecurity attacks pose a threat to the data security that is being transmitted in any IoT network. Cryptographic primitives are widely adopted to address these threats; however, the substantial computation demands limit their applicability in the IoT ecosystem. In addition, each IoT node varies with respect to the area and throughput (TP) requirements, thus demanding flexible implementation for encryption/decryption processes. To solve these issues, this work implements the NIST lightweight cryptography standard, Ascon, on a SAED 32 nm process design kit (PDK) library by employing loop folded, loop unrolled and fully unrolled architectures. The fully unrolled architecture can achieve the highest TP but at the cost of higher area utilisation. Unrolling by a lower factor results in lower area implementations, enabling the exploration of design space to tackle the trade-off between area and TP performance of the design. The implementation results show that, for loop folded architecture, Ascon-128 and Ascon-128a require 36.7k μ m 2 and 38.5k μ m 2 chip area, respectively compared to 277.1k μ m 2 and 306.6k μ m 2 required by their fully unrolled implementations. The proposed implementation strategies can adjust the number of rounds to accommodate the varied requirements of IoT ecosystems. An implementation with an open-source 45 nm PDK library is also undertaken for enhanced generalization and reproducibility of the results. [ABSTRACT FROM AUTHOR]

In today's digital world, where the amount of information that is stored and processed in cyberspace is constantly growing, the issue of personal data protection is becoming more and more relevant. Personal data, such as financial information, medical records, and social media data, is a valuable resource that can be used for both legitimate and malicious purposes. Despite the continuous improvement of security technologies, the growing complexity of cyber threats and the evolution of cyber attack methods create new challenges for the protection of personal information. In this connection, there is a need to analyze the modern problems of personal data protection, in particular the legal, technical and ethical aspects of this issue. Ensuring the security of personal data remains an urgent problem today, because more and more people use it. On the Internet, they pass authorization on various resources, using their personal data for this purpose, and more and more different institutions create peculiar databases, not always taking care of their safety. The insufficient measure of ensuring the security of personal data in cyberspace leads to excessive activity of cyberattacks and the commission of various manipulations with personal data. The relevance of the topic of personal data protection in cyberspace is due to several important factors that have a significant impact on modern society, economy and security. The main aspects that emphasize the relevance of this issue: the growth of digitalization; cyber threats; legal requirements; social responsibility and trust; economic consequences; technological challenges; international relations and geopolitics. Thus, the relevance of the topic of personal data protection in cyberspace is due to the complexity and global nature of this issue, as well as its importance for preserving privacy, security and trust in the digital world. This article is aimed at researching key challenges in the field of personal data protection in cyberspace, as well as providing recommendations for increasing the level of security and minimizing the risks of information leakage. [ABSTRACT FROM AUTHOR]

Have you ever experienced the frustration of trying to hit a bullseye on a moving target? In the medical device industry, cybersecurity is one of the fastest-moving targets. Medical device [...]

I am going to wade into a subject not usually discussed in polite circles; in fact, it may be considered by some to be a 'third rail' topic. Specifically, I [...]

https://b2b-contenthub.com/wp-content/uploads/2024/11/shutterstock_2341919789.png?w=1024 Um Ihre Software-Lieferkette zu schützen, kann Generative AI sehr hilfreich sein. NTPY -Shutterstock.com Es klingt wie ein Agentenkrimi: Unbekannten Drahtziehern ist es gelungen, eine https://www.csoonline.com/de/a/xz-utils-backdoor-gefaehrdet-linux-systeme,3681318, Teil vieler Open-Source-Plattformen, zu [...]

The fourth CSO30 ASEAN Awards programme recognises the top 30 cybersecurity leaders driving business value, demonstrating leadership, and influencing rapid change across Southeast Asia and Hong Kong. In addition to [...]

A hacker group known as “Grep” has reportedly infiltrated Schneider Electric’s internal project tracking system, stealing about 40 GB of data in the latest cyberattack targeting the French multinational. Grep, [...]

Most cybersecurity professionals are accustomed by now to the widespread adoption of open-source software (OSS) and the https://www.csoonline.com/article/574615/top-10-open-source-software-risks.html that target the OSS ecosystem. What we don’t seem to be getting [...]

https://images.computerwoche.de/bdb/3376702/1200x.jpg Lesen Sie, welche Cybersecurity-Zertifizierungen Ihrer Karriere als CISO einen Schub geben. Foto: Elnur – shutterstock.com Zertifizierungen in der Cybersicherheit können das vorhandene Fachwissen hervorheben, die Glaubwürdigkeit erhöhen und Aufstiegsmöglichkeiten [...]

In the summer of 2020, https://www.computerworld.es/article/2119037/el-90-de-los-ordenadores-de-mapfre-quedo-fuera-de-servicio.html that impacted thousands of the company’s servers and workstations in Spain. The timing could not have been more complicated — in the middle of [...]

Personal health information of 100 million individuals was stolen during the February ransomware attack on Change Healthcare, a unit of UnitedHealth, the US health department has revealed. The cyberattack caused [...]

Artificial intelligence, especially large language models (LLMs) and the agents powered by them, has been transformative across the cybersecurity spectrum, and the game-changing technology has been nothing short of revolutionary [...]

Election security is a topic that percolates to the forefront every couple of years, especially as US national elections hit the calendar. There have been more than 60 national elections [...]

India’s hill state of Uttarakhand’s entire IT infrastructure came to a standstill on Friday after malware was detected on systems at the State Data Center. The shutdown impacted several critical [...]

Download the October 2024 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network [...]

Rationale, Aims, and Objectives Method Results Conclusion Medical device‐integrated electronic medical records (MDI‐EMR) pose significant challenges in ensuring effective usage, data security and patient safety. The complexities of MDI‐EMR necessitate applying various security mechanisms to safeguard against cyber threats. Therefore, we evaluated cyber threats to MDI‐EMR and the effectiveness of applied security controls using a proposed framework from sociotechnical and risk assessment perspectives.We conducted a qualitative case study evaluation in a general hospital in Saudi Arabia using interviews, observation, and document analysis from the perspectives of major MDI‐EMR stakeholders, including healthcare providers, IT professionals and cybersecurity specialists.The results showed the interplay among physical, technical and administrative security controls that maintained a secure posture of MDI‐EMR. The effectiveness of security controls is highly influenced by the staff's cybersecurity awareness and training. The perceived effectiveness of security controls varied among users, with some expressing satisfaction with the ease of use and reliability, while others highlighting challenges such as password complexity and access procedures. Understanding these diverse perspectives is crucial for tailoring security measures to meet the needs of different stakeholders effectively.Collaboration among the key stakeholders is crucial for implementing security controls for MDI‐EMR. Balancing security measures with usability concerns is essential, as highlighted by challenges in implementing technical controls. A comprehensive approach encompassing physical, technical and administrative controls, continuous education and awareness initiatives are significant to empower staff in recognising and mitigating cyber threats effectively to safeguard medical data and ensure the integrity of healthcare systems. [ABSTRACT FROM AUTHOR]

COVID-19 has significantly impacted humankind's lifestyle since its emergence. Educational institutions quickly adopted online courses to deal with its spread. Regrettably, the adoption of e-learning solutions, particularly in Africa, has not given due priority to security. However, cyberattacks and cyber incidents are leaping forward. To propose suitable solutions adapted to West African schools and universities' realities, it is essential to understand their online learning environment fully. This paper presents the status of West Africa's online learning workspace security during the COVID-19 lockdown by highlighting the tools and devices used for the courses and security risks associated, and students' cybersecurity awareness level. The research employs a quantitative methodology involving an online survey, which gathered data from a total of 1653 participants hailing from countries in West Africa. The data analysis revealed that students are concerned about privacy and personal data security, and West African online learning environment security faces many challenges and issues. Identified issues are related to students' low level of cybersecurity concepts, best practices, and applicable privacy laws and regulations. Finally, recommendations are made on needed efforts to secure West African online learning space through existing or proposed security mechanisms. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the main trends in the development of digital technologies in the financial sector of the economy, namely, the topic is relevant in the context of rapid globalization, digitalization and transition to digital ecosystems. The modern financial sector of the economy at different stages of its life cycle cannot function without the use of digital technologies. In addition, online activity, browsing social networks, and web pages, and using various applications are essential for civil society. The purpose of this paper is to study the changing impact of advanced technologies on digital security, as well as to substantiate the trends in the development of digital technologies in the financial sector of the economy. The article characterizes the risks arising from the digitalization of the economy - the results of the analysis show that the effective use of these factors can stimulate the competitiveness of financial institutions. The level of threat in data management is investigated, which should be taken into account when forecasting potential threats of digitalization in the financial sector of the economy, which allows the management of institutions to optimize: financial, human and technological resources for risk management and take measures to offset possible losses from cyber threats and restore the stable development of economic systems. The article identifies trends in the development of digital technologies in the financial sector of the economy that require detailed study and provide for the transition to a modern digital financial platform. This transition will not only strengthen the country's domestic market, increasing the level of independence from the import of foreign technologies but will also contribute to improving the global importance of the economy. Therefore, the use of digital technologies in the financial sector of the economy is of key importance, and effective financing in the new economic reality largely ensures the success of the country's socio-economic development. [ABSTRACT FROM AUTHOR]

Security adversaries are rampant on the Internet, constantly seeking vulnerabilities to exploit. The sheer proliferation of these sophisticated threats necessitates innovative and swift defensive measures to protect the vulnerable infrastructure. Tools such as honeypots effectively determine adversary behavior and safeguard critical organizational systems. However, it takes a significant amount of time to analyze these attacks on the honeypots, and by the time actionable intelligence is gathered from the attacker's tactics, techniques, and procedures (TTPs), it is often too late to prevent potential damage to the organization's critical systems. This paper contributes to the advancement of cybersecurity practices by presenting a cutting-edge methodology, capitalizing on the synergy between artificial intelligence and threat analysis to combat evolving cyber threats. The current research articulates a novel strategy, outlining a method to analyze large volumes of attacker data from honeypots utilizing large language models (LLMs) to assimilate TTPs and apply this knowledge to identify real-time anomalies in regular user activity. The effectiveness of this model is tested in real-world scenarios, demonstrating a notable reduction in response time for detecting malicious activities in critical infrastructure. Moreover, we delve into the proposed framework's practical implementation considerations and scalability, underscoring its adaptability in diverse organizational contexts. [ABSTRACT FROM AUTHOR]

In the digital age, the integrity of business operations and the smoothness of their execution heavily depend on cybersecurity and network efficiency. The need for robust solutions to prevent cyber threats and enhance network functionality has never been more critical. This research aims to utilize machine learning (ML) techniques for the meticulous analysis of network traffic, with the dual goals of detecting anomalies and categorizing network activities to bolster security and performance. Employing a detailed methodology, this study begins with data preparation and progresses through to the deployment of advanced ML models, including logistic regression, decision trees, and ensemble learning techniques. This approach ensures the accuracy of the analysis and facilitates a nuanced understanding of network dynamics. Our findings indicate a notable enhancement in identifying network inefficiencies and in the more accurate classification of network traffic. The application of ML models significantly reduces network delays and bottlenecks by providing a strong defence strategy against cyber threats and network shortcomings, thereby improving user satisfaction, and boosting the organizational reputation as a secure and effective service layer. Conclusively, the research highlights the pivotal role of machine learning in network traffic analysis, offering innovative insights and fresh perspectives on anomaly detection and the identification of malicious activities. It lays a foundation for future explorations and acts as an evaluation benchmark in the fields of cybersecurity and network management. [ABSTRACT FROM AUTHOR]