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OBJECTIVES: Healthcare ransomware cyberattacks have been associated with major regional hospital disruptions, but data reporting patient-oriented outcomes in critical conditions such as cardiac arrest (CA) are limited. This study examined the CA incidence and outcomes of untargeted hospitals adjacent to a ransomware-infected healthcare delivery organization (HDO). DESIGN SETTING AND PATIENTS: This cohort study compared the CA incidence and outcomes of two untargeted academic hospitals adjacent to an HDO under a ransomware cyberattack during the pre-attack (April 3-30, 2021), attack (May 1-28, 2021), and post-attack (May 29, 2021-June 25, 2021) phases. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Emergency department and hospital mean daily census, number of CAs, mean daily CA incidence per 1,000 admissions, return of spontaneous circulation, survival to discharge, and survival with favorable neurologic outcome were measured. The study evaluated 78 total CAs: 44 out-of-hospital CAs (OHCAs) and 34 in-hospital CAs. The number of total CAs increased from the pre-attack to attack phase (21 vs. 38; p = 0.03), followed by a decrease in the post-attack phase (38 vs. 19; p = 0.01). The number of total CAs exceeded the cyberattack month forecast (May 2021: 41 observed vs. 27 forecasted cases; 95% CI, 17.0-37.4). OHCA cases also exceeded the forecast (May 2021: 24 observed vs. 12 forecasted cases; 95% CI, 6.0-18.8). Survival with favorable neurologic outcome rates for all CAs decreased, driven by increases in OHCA mortality: survival with favorable neurologic rates for OHCAs decreased from the pre-attack phase to attack phase (40.0% vs. 4.5%; p = 0.02) followed by an increase in the post-attack phase (4.5% vs. 41.2%; p = 0.01). CONCLUSIONS: Untargeted hospitals adjacent to ransomware-infected HDOs may see worse outcomes for patients suffering from OHCA. These findings highlight the critical need for cybersecurity disaster planning and resiliency.

Ransomware is a significant security threat that poses a serious risk to the security of smartphones, and its impact on portable devices has been extensively discussed in a number of research papers. In recent times, this threat has witnessed a significant increase, causing substantial losses for both individuals and organizations. The emergence and widespread occurrence of diverse forms of ransomware present a significant impediment to the pursuit of reliable security measures that can effectively combat them. This constitutes a formidable challenge due to the dynamic nature of ransomware, which renders traditional security protocols inadequate, as they might have a high false alarm rate and exert significant processing demands on mobile devices that are restricted by limited battery life, CPU, and memory. This paper proposes a novel intelligent method for detecting ransomware that is based on a hybrid multi-solution binary JAYA algorithm with a single-solution simulated annealing (SA). The primary objective is to leverage the exploitation power of SA in supporting the exploration power of the binary JAYA algorithm. This approach results in a better balance between global and local search milestones. The empirical results of our research demonstrate the superiority of the proposed SMO-BJAYA-SA-SVM method over other algorithms based on the evaluation measures used. The proposed method achieved an accuracy rate of 98.7%, a precision of 98.6%, a recall of 98.7%, and an F1 score of 98.6%. Therefore, we believe that our approach is an effective method for detecting ransomware on portable devices. It has the potential to provide a more reliable and efficient solution to this growing security threat. [ABSTRACT FROM AUTHOR]

This article explores the importance of establishing a strong third-party risk management program within a connected ecosystem. It acknowledges the growing complexities of third-party risks and the potential consequences of disruptions in the supply chain. The article outlines four steps to develop an effective program, including expanding the coverage of the third-party ecosystem, broadening the scope of risk assessment, improving risk assessments, and enhancing reporting. It emphasizes the need to consider supply chain third parties and identifies various risk domains that organizations should evaluate. The article also discusses the challenges of conducting risk assessments and suggests strategies to streamline the process. Overall, it offers valuable insights for organizations seeking to mitigate emerging threats and build a resilient supply chain. The article also highlights the risks associated with third-party relationships in supply chains, such as operational and supply disruption, financial viability, subcontractor, third-party concentration, and geographic concentration risks. It underscores the importance of risk assessment and monitoring in managing these risks, emphasizing the need for data-driven insights and effective reporting to measure the success of third-party risk management programs. The article concludes by stressing the importance of organizational agility and adapting risk management programs to the evolving risk landscape. [Extracted from the article]

Nowadays, the network intrusion and cyberattack have emerged as the two main issues with Internet of Things (IoT) applications. The existing methods for preventing and detecting intrusions are limited in many ways, making it impossible to accurately identify any kind of attack occurring within network traffic. A number of machine learning-based methods that attains poor performance in the multiple class categorization accuracy are provided by the researchers. This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. The input data is taken from TON_IoT Dataset. The data balancing of the training dataset is enhanced using Class decomposition and synthetic minority class oversampling method (CDSMOTE). Then, the data is pre-processed using Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) of noise removal and data enhancement. The preprocessed output is given into feature extraction to extract features by using Dual-Tree Biquaternion Wavelet Transform (DTBWT). The extracted features are fed into Bayesian Regularization-Back Propagation Neural Network (BRBPNN) which detects the intrusion as Ransomware, Password attack, Scanning, Denial of Service (DoS), Distributed Denial of Service (DDoS), Data injection, Backdoor, Cross-Site Scripting (XSS), and Man-In-The-Middle (MITM). In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. The proposed DIDS-BRBPNN-BBWOA-IoT method is implemented using Python. The performance of the DIDS-BRBPNN-BBWOA-IoT approach is examined using performance metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, and ROC. The proposed SAPVAEGAN-LCC-IR approach attains 18.44%, 26% ,and 29% greater accuracy; 26.55%, 24.12%, and 27.22% greater recall compared with existing MIDS-MIoT, AID-SDN-IoT, and IID-LW-IoT techniques. [ABSTRACT FROM AUTHOR]

Ransomware is malicious software that targets computers, mobile phones, tablets, and other digital devices. These types of software typically encrypt files on the target device, blocking access, and then demand a ransom. TorrentLocker attacks have become particularly popular in recent years, emerging as prominent threats in the realm of cybersecurity. TorrentLocker poses a serious threat to the digital data of users and organizations, exacerbating the financial and reputational damages stemming from cyberattacks. This study provides a framework to understand the target audience, attack strategies, and operations of TorrentLocker ransomware. Conducted through a real case analysis, this examination sheds light on the TorrentLocker attack strategy and elucidates the tracing and identification of the attacker post-attack. The aim of this study is to raise awareness among cybersecurity professionals, organizations, and individual users about TorrentLocker ransomware attacks, aiming to prevent such attacks and track down traces left by the attacker's post-incident. This detailed analysis of TorrentLocker ransomware attacks serves as a crucial resource to enhance protection against future ransomware attacks and contributes to the body of work in this field. [ABSTRACT FROM AUTHOR]

Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware, which is a form of malware that aims to steal user's money. Ransomware is a form of malware that encrypts a victim's files. The attacker then demands a ransom from the victim to restore access to the data upon a large payment. Ransomware is a way of stealing money in which a user's files are encrypted and the decrypted key is held by the attacker until a ransom amount is paid by the victim. This systematic literature review (SLR) highlights recent papers published between 2020 and 2024. This paper examines existing research on early ransomware detection methods, focusing on the signs, frameworks, and techniques used to identify and detect ransomware before it causes harm. By analyzing a wide range of academic papers, industry reports, and case studies, this review categorizes and assesses the effectiveness of different detection methods, including those based on signatures, behavior patterns, and machine learning (ML). It also looks at new trends and innovative strategies in ransomware detection, offering a classification of detection techniques and pointing out the gaps in current research. The findings provide useful insights for cybersecurity professionals and researchers, helping guide future efforts to develop strong and proactive ransomware detection systems. This review emphasizes the need for ongoing improvements in detection technologies to keep up with the constantly changing ransomware threat landscape. [ABSTRACT FROM AUTHOR]

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Crypto ransomware attacks pose a significant threat by encrypting users' data and demanding ransom payments, causing permanent data loss if not detected and mitigated before encryption occurs. The existing studies have faced challenges in the pre-encryption phase due to elusive attack patterns, insufficient data, and the lack of comprehensive information, often confusing the current detection techniques. Selecting appropriate features that effectively indicate an impending ransomware attack is a critical challenge. This research addresses this challenge by introducing an Enhanced Joint Mutual Information (EJMI) method that effectively assigns weights and ranks features based on their relevance while conducting contextual data analysis. The EJMI method employs a dual ranking system—TF for crypto APIs and TF-IDF for non-crypto APIs—to enhance the detection process and select the most significant features for training various Machine Learning (ML) classifiers. Furthermore, grid search is utilized for optimal classifier parameterization, aiming to detect ransomware efficiently and accurately in its pre-encryption phase. The proposed EJMI method has demonstrated a 4% improvement in detection accuracy compared to previous methods, highlighting its effectiveness in identifying and preventing crypto-ransomware attacks before data encryption occurs. [ABSTRACT FROM AUTHOR]

Digital transformation brings changes to the business world by increasing efficiency, convenience, security, certainty and operational speed. CV Elang Java Mandiri experienced the positive impact of this by creating software to increase operational efficiency. However, the use of direct communication between the desktop application and the database causes vulnerabilities. The lack of a bridge between the desktop application and the database also indicates a lack of flexibility when adding other applications. Ransomware attacks on desktop applications cause losses and limitations in development. This research focuses on backend updates that use API as an integrator with the API-first method. OpenAPI standards and OWASP security principles are used to increase resilience against security threats. These steps were tested with OWASP ZAP and http test. The goal is to provide solutions to company problems, meet the need for more secure applications, and make application development easier. [ABSTRACT FROM AUTHOR]

Ransomware is a growing-in-popularity type of malware that restricts access to the victim's system or data until a ransom is paid. Traditional detection methods rely on analyzing the malware's content, but these methods are ineffective against unknown or zero-day malware. Therefore, zero-day malware detection typically involves observing the malware's behavior, specifically the sequence of application programming interface (API) calls it makes, such as reading and writing files or enumerating directories. While previous studies have used machine learning (ML) techniques to classify API call sequences, they have only considered the API call name. This paper systematically compares various subsets of API call features, different ML techniques, and context-window sizes to identify the optimal ransomware classifier. Our findings indicate that a context-window size of 7 is ideal, and the most effective ML techniques are CNN and LSTM. Additionally, augmenting the API call name with the operation result significantly enhances the classifier's precision. Performance analysis suggests that this classifier can be effectively applied in real-time scenarios. [ABSTRACT FROM AUTHOR]

There are growing concerns about security as the usage of computers in academic settings continues to increase. This research aims to investigate the level of awareness among university students regarding security threats associated with ransomware. This study examines students' behaviour and preventive motivation for ransomware attacks, along with the measures taken to mitigate these security threats. The study model combines the theory of planned behaviour (TPB) and preventive motivation theory (PMT) with additional threat awareness (TA) variables. The research findings indicate a high level of awareness regarding the dangers. TA has a positive influence on other factors, as indicated by the significant t-values (perceived severity (PS)=4.479, perceived vulnerability (PV)=3.251, response efficacy (RE)=14.344, and self-efficacy (SE)=8.034). This research also demonstrates that subjective norm (SN) and affective responses (AR) have a key impact on behavioural intention (BI). Moreover, two of the preventive motivation factors, PS and PV, significantly contribute to BI, while the other two (RE and SE) did not show a significant contribution to BI. [ABSTRACT FROM AUTHOR]

The British Library hack and its response serve as a clear example of the vulnerability of institutions of higher learning to such attacks and the importance of maintaining an open dialogue with the public during recovery. This open dialogue is currently lacking as universities attempt to move on and cover up these attacks quickly. This paper aims to start the conversation by providing three examples of institutions that went through a hack that left their services down for a significant period, how it affected those they serve, how they responded, and what information was made public. [ABSTRACT FROM AUTHOR]

This research reviews into the critical interplay between big data applications and cybersecurity risks within the accounting sector. Aimed at understanding how big data can mitigate these risks, the study develops a novel theoretical model using differential equations. This model, rooted in a thorough empirical approach, undergoes validation through logistic regression analysis of responses from 200 participants. The analysis particularly focuses on how demographic and socio-economic factors influence cybersecurity perceptions. Data Breach Consistency emerges as a key factor, evidenced by a coefficient of 1.204 and an odds ratio of 3.331, indicating a substantial link between the recognition of data breaches and increased cybersecurity concerns. Malware and Ransomware concerns demonstrate a notable impact, with a coefficient of 0.907 and an odds ratio of 2.477, underscoring the gravity of these threats. Results further highlight the mitigating influence of Big Data Mitigation on cybersecurity risks, marked by a coefficient of 0.491. The robustness of the model is affirmed by an Area Under the Curve (AUC) score of 0.843, attesting to its efficacy in predicting cybersecurity concerns. The findings highlight the vital role of big data in formulating effective cybersecurity strategies. This study not only contributes to the academic discourse on the intersection of big data and cybersecurity in accounting but also offers practical insights for enhanced decision-making and policy formulation in the evolving digital business environment. [ABSTRACT FROM AUTHOR]

This paper attempts to examine the extent of cybersecurity awareness among college students and propose some effective measures to prevent them from cyber-attacks. In light of the pervasive integration of cutting-edge technologies in education and our daily life, College students faces various on-line threats. Given their active internet usage, college students are particularly susceptible to cyber-attacks through activities such as exploring e-content, engaging in social media platform, participating in online learning platforms, digital polls, quizzes, Kahoot, online debates and research work. This study holds significance for college students, who are particularly vulnerable. To protect them from cyber threats, understanding their level of cybersecurity consciousness regarding phishing scams, malware, ransomware, and other computer threats, as well as their knowledge of preventive measures, is extremely crucial. The escalating number of hackers and organized cybercriminals underscores the need for heightened cybersecurity awareness. These cybercriminals continually seek innovative techniques for cyber theft, including obtaining sensitive and confidential information from college students for potential blackmail or selling to others. This study involves an efficient online survey conducted among college students to gauge their cybersecurity awareness and preventive measures knowledge. Based on the survey findings, recommendations are provided to strengthen cybersecurity and encourage safer online practices among college students. [ABSTRACT FROM AUTHOR]

With the advent and subsequent explosion of the internet, global connectivity has been achieved, and is on the rise. This provides a host of advantages such as connectivity and communication, information broadcast and transmission, amongst others. This however introduces a new set of challenges: the safety and protection of these communication channels amongst them. Information has always been power, and the widespread mature of information only results in the widespread attempts to procure it, sometimes via illegal channels. In view of this, this research aims at detecting Crypto-ransomware and locker ransomware. Data was collected from an open repository and cleaned. The cleaned data was then split into tests, train sets and validation which was used to train a number of ML models based on the: Random Forest algorithm, Support Vector Machine (SVM) and Gradient boosting algorithm. Ransomware is one of the well-known ways and frequent use which cyber-attackers use in infecting their victims, either through phishing or drive download. Attackers will create an email pretending to be from a genuine resource and send it to their targeted victims. However, this research illustrated how to combat crypto-ransomware and locker ransomware. Implementing the machine learning algorithm, the system can detect ransomware under 30’s, giving computer users over 90% assurance of their system for ransomware free.