The cybersecurity of critical infrastructures is an essential topic within national and international security as 16 critical infrastructure sectors touch various aspects of American society. Because the failure to provide adequate cybersecurity controls within the critical infrastructure sectors renders the country open to an attack that could have a debilitating effect on security, national public health, safety, and economic security, this matter is so vital that there is the Presidential Policy Directive (PPD) 21 Critical Infrastructure Security and Resilience advances a national policy to strengthen and maintain secure, functioning and resilient critical infrastructure. An organization identified as the Cybersecurity and Infrastructure Security Agency (CISA) at the Department of Homeland Security (DHS) has the mission to be the risk advisor for the United States (US). Other organizations, such as the National Security Agency (NSA), have approved a specific Knowledge Unit (KU) to address cybersecurity for critical infrastructures associated with doctoral-level granting programs. To address this challenge, it is necessary to identify threats better and defend against them while mitigating risks to an acceptable level. Only then can a nation build a more secure and resilient infrastructure for the future while defending against present-day bad actors as cyberwarfare, cyber espionage, and cybersecurity attacks are the modern-day threats that need to be addressed in planning, designing, implementation, and maintenance. Therefore, the researchers developed a case study reviewing threats against different sectors defined in the PPD.

Cyberspace is quickly becoming overwhelmed with ever-evolving malware that breaches all security defenses and secretly leaks confidential business data. One of the most pressing challenges faced by business organizations when they experience a cyber-attack is that, more often than not, those organizations do not have the knowledge nor readiness of how to analyze malware. The objective of this research is to present the fundamentals of malware reverse-engineering, the tools, and techniques needed to properly analyze malicious programs to determine their characteristics. Those tools and techniques will provide insights to incident response teams and digital investigation professionals. In order to stop hackers in their tracks, we need to equip cyber security professionals with the knowledge and skills necessary to detect and respond to malware attacks. Additionally, the authors discuss what ransomware is, how it infects systems, and how to prevent infection. It will also examine some different variations of ransomware, as well as explore some cases of ransomware.

Diferentes abordagens e perspectivas têm sido utilizadas nos modelos teóricos de retenção de estudantes nas últimas oito décadas, com foco nas altas taxas da evasão de estudante no ensino superior. Muitas dessas abordagens e perspectivas parecem ser pouco conhecidas dos educadores, administradores e formuladores de políticas das instituições de ensino em função das suas complexidades. Alguns pesquisadores, preocupados com as questões de evasão escolar, estão saindo de uma abordagem com perspectiva pontual para uma abordagem mais abrangente e integrativa, preocupada com soluções mais construtivas. Esse estudo tem como objetivo identificar e compreender as perspectivas dos modelos teóricos de retenção e fornecer aos educadores, administradores e formuladores de políticas uma visão abrangente dos principais modelos teóricos de retenção de estudantes, considerando as abordagens e perspectivas para auxiliar nas questões relativas à evasão de estudante.A pesquisa resultou em vinte e três modelos teóricos de retenção com suas respectivas abordagens e perspectivas.

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.