Risk Of Breach Research Articles

Automated Identification of Sensitive Financial Data Based on the Topic Analysis

Data governance is an extremely important protection and management measure throughout the entire life cycle of data. However, there are still data governance issues, such as data security risks, data privacy breaches, and difficulties in data management and access control. These problems lead to a risk of data breaches and abuse. Therefore, the security classification and grading of data has become an important task to accurately identify sensitive data and adopt appropriate maintenance and management measures with different sensitivity levels. This work started from the problems existing in the current data security classification and grading work, such as inconsistent classification and grading standards, difficult data acquisition and sorting, and weak semantic information of data fields, to find the limitations of the current methods and the direction for improvement. The automatic identification method of sensitive financial data proposed in this paper is based on topic analysis and was constructed by incorporating Jieba word segmentation, word frequency statistics, the skip-gram model, K-means clustering, and other technologies. Expert assistance was sought to select appropriate keywords for enhanced accuracy. This work used the descriptive text library and real business data of a Chinese financial institution for training and testing to further demonstrate its effectiveness and usefulness. The evaluation indicators illustrated the effectiveness of this method in the classification of data security. The proposed method addressed the challenge of sensitivity level division in texts with limited semantic information, which overcame the limitations on model expansion across different domains and provided an optimized application model. All of the above pointed out the direction for the real-time updating of the method.

Privacy-Preserving Neural Networks for Smart Manufacturing

Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by enabling the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, big data also attracts cyberattacks and makes manufacturing systems vulnerable due to the inherent value of sensitive information. The increasing integration of artificial intelligence (AI) within smart factories also exposes manufacturing equipment susceptible to cyber threats, posing a critical risk to the integrity of smart manufacturing systems. Cyberattacks targeting manufacturing data can result in considerable financial losses and severe business disruption. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence, this paper presents the development of a new approach called mosaic neuron perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, balancing the trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the computer numerical control (CNC) turning process, demonstrate that the proposed method significantly improves the ability to prevent inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.

Security in cyber physical systems: Transformation and challenges

<p>Network technology has significantly improved due to the growing use of Cyber-Physical Systems (CPS) in various industries, including healthcare, transportation, and communication. The efficiency of these domains has increased overall due to the transmission of sensor data to the cloud and its use by various apps. However, increased data transfer increases the risk of unauthorized modification and data breaches. The degree of risk varies per domain, and security is a crucial area of concentration to mitigate these concerns. Significant developments in network technology have resulted from the growing use of Cyber-Physical Systems (CPS) in various industries, including healthcare, transportation, and communication. The efficiency of these fields has increased overall due to sensor data being sent to the cloud and used by various applications. However, data breaches and unauthorized alteration are risks that come with increased data flow. Depending on the domain, the risk level varies, and security is a key concern in addressing these concerns.</p>

Pervasive User Data Collection from Cyberspace: Privacy Concerns and Countermeasures

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks.

Sustainability in Technological Advancements

The rapid proliferation of technology has ushered in an era of remarkable efficiency, greatly enhancing the ease and affordability of human life. Across diverse domains, ranging from agriculture to space exploration, manufacturing, marketing, research, and education, substantial changes and rapid advancements have become the norm. Scientific innovation has been instrumental in addressing pressing global challenges, encompassing climate change, water purification, E-waste management, global warming, and healthcare, all of which contribute significantly to human well-being. However, the exponential growth of artificial intelligence (AI) and machine learning presents profound concerns about the future of human existence. Beyond the risks of data privacy breaches, there is a growing apprehension that our reliance on these technologies may stifle human creativity, leading to a deficiency in originality and authenticity, particularly as AI-generated content becomes pervasive. These ethical and privacy quandaries underscore the pressing need to cultivate sustainable technology solutions that not only prioritize human welfare but also safeguard data and privacy while encouraging creativity and proactive engagement in our endeavors rather than inducing passivity.

Assessing Strategies to Overcome Barriers for Drone Usage in Last-Mile Logistics: A Novel Hybrid Fuzzy MCDM Model

Effective last-mile (LM) delivery is critical to the efficient functioning of supply chains. In addition to speed and the cost of delivery, environmental and social sustainability are increasingly important factors in last-mile logistics (LML), especially in urban areas. Sustainable solutions such as drones attract special attention from researchers due to their high potential. The future of drone logistics is uncertain due to many barriers. This study analyzes, evaluates and ranks barriers to identify those that most significantly hinder broader drone adoption in LML, and proposes and ranks strategies to overcome them. This type of issue requires the involvement of multiple stakeholders with conflicting goals and interests. Therefore, the study employs a novel hybrid multi-criteria decision-making (MCDM) model that combines fuzzy Delphi-based fuzzy factor relationship (Fuzzy D-FARE) and fuzzy comprehensive distance-based ranking (Fuzzy COBRA) methods. The results indicate that the main obstacle to drone implementation in LM is the lack of aviation regulations. The risks of unauthorized access, data misuse, privacy breaches, and data security represent significant challenges. They are followed by ambiguously defined or burdensome requirements for insurance and liability for drone owners. The main contributions of this study are the establishment of a novel hybrid model, identification and ranking of barriers for broader application of drones in LML, and strategies for overcoming them.

Dynamic Security Policies for Cloud Infrastructures: An AI-Based Framework

The rapid expansion of cloud computing has introduced significant challenges in maintaining robust security policies due to the dynamic and scalable nature of cloud environments. This research presents an AI-based framework for developing and implementing dynamic security policies in cloud infrastructures. The proposed framework leverages machine learning algorithms to analyze and predict potential security threats, enabling the real-time adaptation of security measures. By continuously monitoring cloud resources and utilizing intelligent threat detection mechanisms, the framework ensures a proactive approach to cloud security. Case studies demonstrate the effectiveness of the AI-driven framework in enhancing the security posture of cloud infrastructures, reducing vulnerabilities, and minimizing the risk of data breaches. The results indicate that the integration of AI in cloud security policy management offers substantial improvements in response times and threat mitigation capabilities.

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system’s accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.

Inner Ear Breaches from Vestibular Schwannoma Surgery: Revisiting the Incidence of Otologic Injury from Retrosigmoid and Middle Cranial Fossa Approaches.

To assess the rate of iatrogenic injury to the inner ear in vestibular schwannoma resections. Retrospective case review. Multiple academic tertiary care hospitals. Patients who underwent retrosigmoid or middle cranial fossa approaches for vestibular schwannoma resection between 1993 and 2015. Diagnostic with therapeutic implications. Drilling breach of the inner ear as confirmed by operative note or postoperative computed tomography (CT). 21.5% of patients undergoing either retrosigmoid or middle fossa approaches to the internal auditory canal were identified with a breach of the vestibulocochlear system. Because of the lack of postoperative CT imaging in this cohort, this is likely an underestimation of the true incidence of inner ear breaches. Of all postoperative CT scans reviewed, 51.8% had an inner ear breach. As there may be bias in patients undergoing postoperative CT, a middle figure based on sensitivity analyses estimates the incidence of inner ear breaches from lateral skull base surgery to be 34.7%. A high percentage of vestibular schwannoma surgeries via retrosigmoid and middle cranial fossa approaches result in drilling breaches of the inner ear. This study reinforces the value of preoperative image analysis for determining risk of inner ear breaches during vestibular schwannoma surgery and the importance of acquiring CT studies postoperatively to evaluate the integrity of the inner ear.

Filtering and Detection of Real-Time Spam Mail Based on a Bayesian Approach in University Networks

With the advent of digital technologies as an integral part of today’s everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited and often irrelevant messages. This relentless influx of spam not only disrupts user productivity but also raises security concerns, as it frequently serves as a vehicle for phishing attempts, malware distribution, and other cyber threats. The prevalence of spam is fueled by its low-cost dissemination and its ability to reach a wide audience, exploiting vulnerabilities in email systems. This paper marks the inception of an in-depth investigation into the viability and potential implementation of a robust spam filtering and prevention system tailored explicitly to university networks. With the escalating threat of email-based hacking attacks and the incessant deluge of spam, the need for a comprehensive and effective defense mechanism within academic institutions becomes increasingly imperative. In exploring potential solutions, this study delves into the applicability and efficacy of Bayesian filters, a class of probabilistic classifiers renowned for their aptitude in distinguishing between legitimate emails and spam messages. Bayesian filters utilize statistical algorithms to analyze email content, learning patterns and features to accurately categorize incoming emails.

Balancing User Privacy and Legal Demands while Conducting Businesses on the Blockchain

Blockchain technology offers a promising way to improve business processes by providing a secure and transparent transaction platform. However, using this technology brings its own set of challenges, especially when trying to balance user privacy with legal and regulatory needs. This article explores the challenges of keeping user information private, adhering to regulatory frameworks, and fulfilling legal requirements on the blockchain. A key point in this research is the challenge of keeping or maintaining confidentiality while being transparent. The article also discusses the issues of applying legal rules to a system not controlled by one central authority, the risks of privacy and security breaches, and the need to follow data protection laws. The article highlights how some blockchain-based companies have tackled these challenges, mainly through smart blockchain management and innovative technology, by looking at real-world examples from major companies like IBM, Bitpay, Ripple, and Coinbase. The systematic literature review (SLR) methodology involved reviewing literature from the past 15 years (2008-2023) from trusted sources like Google Scholar, ACM Digital Library, IEEE, Springer, and Science Direct. The findings indicate that cutting-edge technologies prioritizing privacy, such as zero-knowledge proofs, ring signatures, and encryption methods, would enable Bitcoin (BTC) platform operations to maintain or balance privacy and transparency. Furthermore, the study indicates the importance of clear privacy guidelines, adhering to relevant regulations, working closely with regulators and law enforcement, and educating users. In summary, it is crucial to approach blockchain carefully, prioritizing user privacy while meeting all legal and regulatory requirements.

Distributed cryptosystem for service-oriented smart manufacturing

Advanced sensing and cloud systems propel the rapid advancements of service-oriented smart manufacturing. As a result, there is widespread generation and proliferation of data in the interest of manufacturing analytics. The sheer amount and velocity of data have also attracted a myriad of malicious parties, unfortunately resulting in an elevated prevalence of cyber-attacks whose impacts are only gaining in severity. Therefore, this article presents a new distributed cryptosystem for analytical computing on encrypted data in the manufacturing environment, with a case study on manufacturing resource planning. This framework harmonizes Paillier cryptography with the Alternating Direction Method of Multipliers (ADMM) for decentralized computation on encrypted data. Security analysis shows that the proposed Paillier-ADMM system is resistant to attacks from external threats, as well as privacy breaches from trusted-but-curious third parties. Experimental results show that smart allocation is more cost-effective than the benchmarked deterministic and stochastic policies. The proposed distributed cryptosystem shows strong potential to leverage the distributed data for manufacturing intelligence, while reducing the risk of data breaches.

Blockchain-based access control architecture for multi-domain environments

Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.

Extended Fuzzy Neutrosophic Classifier for Accurate Intrusion Detection and Classification

Intrusion Detection is crucial in contemporary cybersecurity landscapes to proactively thwart and identify possible threats. The risk of data breaches, malicious activities, and unauthorized access escalates as organizations increasingly rely on interconnected systems. Intrusion Detection Systems (IDS) are imperative for the continuous monitoring of system and network activities, quickly identifying patterns or anomalies indicative of cyber threats. IDS acts as a frontline defense mechanism with the ability to identify abnormal behaviors and known attack signatures. Prompt recognition allows for safeguarding sensitive data, timely response, fortifying the overall resilience of IT infrastructures, and reducing the effect of security incidents. The implementation of robust IDS is vital in an era marked by evolving cyber threats to ensure the confidentiality, availability, and integrity of digital assets. This study develops an improved Arithmetic Optimization Algorithm with an Extended Fuzzy Neutrosophic Classifier technique (AOA-EFNSC) for Accurate Intrusion Detection and Classification. The main goal of proposing this model is to recognize the presence of intrusions effectually. A min-max scalar is applied to normalize the input data before using the improved AOA as a feature selection method. For intrusion detection, the proposed model uses the FNSC technique for the recognition and classification of the intrusions. A sequence of experimentations was involved to validate the superior performance of the proposed model. The experimental value pointed out that our proposed approach outperforms the previous models and enhances the intrusion detection results.

Implementation of risk management in library information system at Surabaya City Library

Libraries may be at risk of data security issues. The threat of data loss or data damage is prone to occur in libraries. Information technology embedded in libraries has developed Artificial Intelligence (AI) for information retrieval systems. The application of information technology in the Surabaya City Library has potential risks to data security and force majeure aspects so that need a disaster mitigation plant. This study aimed to implement information system risk management at Surabaya City Libraries as a disaster mitigation plan. The research method was descriptive qualitative. Based on the risk analysis, the recommendations for applying risk to information technology Surabaya City Library were elimination and engineering. The manifestation of these two recommendations made the data access protocol use multiple authentications to avoid data theft attempts due to infiltration, hacking, and cracking. Study results recommended a protocol for each data access. The protocol would save every change, deletion, and system login in the log history. Implementing data authentication twice per login can avoid data theft caused by the intrusion. This activity log is used for activity tracking when the system is infiltrated. The limitation of this study lies in the scope of the research object, namely the library, which has limitations on the system or application used and does not impact the wider community. Conclusion of research, the library can implement risk management strategy with elimination and engineering models through recording system login activities, which mitigate the risk of data breaches originating from insiders.

Accuracy and risk factors of Magerl screw placement in robot-assisted atlantoaxial surgery: A case series study.

To assess the accuracy of robot-assisted Magerl screw placement and explore the factors affecting the accuracy. A retrospective analysis of patients who underwent robot-assisted Magerl screw placement was performed. The accuracy of Magerl screw placement was evaluated according to the Gertzbein and Robbins scale. 47 Magerl screws were placed in 24 consecutive patients. 32 Magerl screws were narrower than the C2 isthmus height and 26 of them were grade A. 15 Magerl screws were wider than the C2 isthmus height and all of them were grade B. Temporary fixation after decompression and a smaller difference between the C2 isthmus height and screw diameter were associated with a higher probability of cortical breach. The accuracy of robot-assisted Magerl screw placement was excellent. Temporary fixation after decompression and a smaller difference between the C2 isthmus height and screw diameter increased the risk of cortical breach.

Financial Tools – A Way to Approach the Rule of Law?

The rule of law is the hot topic of these days. Especially Poland and Hungary have been for the past eight years proving that they are not afraid to backslide from the values on which the Union is founded, the rule of law among them. The lengthy process on determining whether there exists a ‘clear risk of serious breach’ of the fundamental values by these two Member States, while having a few judgments of the Court of Justice of the EU, proves that the political sanctions envisaged in Article 7 TEU are not an effective tool in dealing with such Member States. Accordingly, this article intends to show the ineffectiveness of political tools and to inquire whether the financial tools contained in the Conditionality Regulation, Common Provision Regulation, and Resilience and Recovery Regulation could be a more effective solution in this regard.

Исследование проблематики методик определения типа контента во входящем трафике

Content filtering in the context of cybersecurity and trusted environments is an important tactic used to ensure network security and functionality. It works by restricting access to certain websites, emails, files or other content that may contain harmful elements or pose a significant risk of infection. Content filtering ensures the security not only of an individual user's data, but also of an entire network of organizations and institutions, helping to minimize the risk of malicious security breaches. The study of methods for determining the type of content in incoming traffic is a relevant and important area in the field of information security and network analytics. In today's Internet space, a significant amount of data is transmitted through networks, and one of the key tasks is the classification of this traffic to ensure security and effective network management. Methods for determining the type of content in incoming traffic are a set of algorithms and approaches that allow you to automatically determine what type of data is transmitted over the network. In the course of studying the problems of methods for determining the type of content in incoming traffic, data on network traffic is collected, a data set is selected for training the model, we consider classifier algorithms and focus on metrics for assessing classification efficiency. The results of the study can be used to create effective systems for detecting malicious or unwanted content, filtering data, or optimizing the operation of network resources. The study of methods for determining the type of content in incoming traffic is of practical importance and can be applied in various fields, including information security, network analytics, monitoring of network resources and optimization of network processes.

Effects of Internal and External Factors on Hospital Data Breaches: Quantitative Study.

Health care data breaches are the most rapidly increasing type of cybercrime; however, the predictors of health care data breaches are uncertain. This quantitative study aims to develop a predictive model to explain the number of hospital data breaches at the county level. This study evaluated data consolidated at the county level from 1032 short-term acute care hospitals. We considered the association between data breach occurrence (a dichotomous variable), predictors based on county demographics, and socioeconomics, average hospital workload, facility type, and average performance on several hospital financial metrics using 3 model types: logistic regression, perceptron, and support vector machine. The model coefficient performance metrics indicated convergent validity across the 3 model types for all variables except bad debt and the factor level accounting for counties with >20% and up to 40% Hispanic populations, both of which had mixed coefficient directionality. The support vector machine model performed the classification task best based on all metrics (accuracy, precision, recall, F1-score). All the 3 models performed the classification task well with directional congruence of weights. From the logistic regression model, the top 5 odds ratios (indicating a higher risk of breach) included inpatient workload, medical center status, pediatric trauma center status, accounts receivable, and the number of outpatient visits, in high to low order. The bottom 5 odds ratios (indicating the lowest odds of experiencing a data breach) occurred for counties with Black populations of >20% and <40%, >80% and <100%, and >40% but <60%, as well as counties with ≤20% Asian or between 80% and 100% Hispanic individuals. Our results are in line with those of other studies that determined that patient workload, facility type, and financial outcomes were associated with the likelihood of health care data breach occurrence. The results of this study provide a predictive model for health care data breaches that may guide health care managers to reduce the risk of data breaches by raising awareness of the risk factors.

Pentingnya Kepatuhan Keamanan Informasi Dalam Mengurangi Risiko Data Breach

This research aims to find out how vital information security compliance is in reducing the risk of Data Breach. The method used in this research is Library Research (Library) which is the method used in this writing, and in its use, this method uses books, and journals both in written form and online. Based on the results obtained from this research, it can be concluded that a Data Breach is an incident where sensitive data or important information becomes vulnerable or is accessed by unauthorized parties. To prevent a Data Breach, organizations need to take various security measures, policies, and practices, such as encryption, access management, physical security, network security, security training and awareness, security policies, and monitoring and auditing. Sensitive data protection helps maintain the confidentiality, integrity, and availability of data and protects the reputation and trust of customers and business partners. By implementing good sensitive data protection, organizations can reduce the risk of data breaches which can be financially detrimental and damage the company's image.