A Review of Attacks, Vulnerabilities, and Defenses in Industry 4.0 with New Challenges on Data Sovereignty Ahead

Research collaboration data platform ensuring general data protection

Translational data is of paramount importance for medical research and clinical innovation. It has the potential to benefit individuals and organizations, however, the protection of personal data must be guaranteed. Collecting diverse omics data and electronic health records (EHR), re-using the minimized data, as well as providing a reliable data transfer between different institutions are mandatory steps for the development of the promising field of big data and artificial intelligence in medical research. This is made possible within the proposed data platform in this research project. The established data platform enables the collaboration between public and commercial organizations by data transfer from various clinical systems into a cloud for supporting multi-site research while ensuring compliant data governance.

Current landscape of hospital information systems in gynecology and obstetrics in Germany: a survey of the commission Digital Medicine of the German Society for Gynecology and Obstetrics

PURPOSE

Hospital information systems (HIS) play a critical role in modern healthcare by facilitating the management and delivery of patient care services. We aimed to evaluate the current landscape of HIS in the specialty of gynecology and obstetrics in Germany.

METHODS

An anonymous questionnaire was distributed via the German Society of Gynecology and Obstetrics newsletter in December 2022. The questionnaire covered the domains baseline demographic information, satisfaction with daily use, satisfaction with implementation, and degree of digitization.

RESULTS

Ninety-one participants completed the survey. Median age was 34 years; 67.4% (60 of 89) were female, and 32.6% (29 of 89) were male. Of the survey participants, 47.7% (42 of 88) were residents, 26.1% (23 of 91) senior physicians, and 9.1% (8 of 88) medical directors. The degree of digitization of clinical documentation is mainly mixed digital and paper-based (64.0%, 57 of 89) while 16.9% (15 of 89) operate mainly paper-based. The current HIS has been in use on average for 9 years. The median number of different software systems used in daily routine is 4. About 33.7% (30 of 89) would likely or very likely recommend their current HIS to a colleague.

CONCLUSIONS

The current landscape of HIS in gynecology and obstetrics in Germany is characterized by a high heterogeneity of systems with low interoperability and long service life; thus, many healthcare professionals are not satisfied. There is both a need to enhance and an interest in modernizing the technological infrastructure to meet today's requirements for patient care.

Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance

The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learning algorithm models under the perspective of cross-border data flow governance. In this study, four machine learning algorithms, namely, logistic regression, decision tree, random forest, and GBDT, are used to build an outbound data assessment and evaluation model. The confusion matrix is used to classify the outbound data legitimacy dichotomously. The recall, precision, and F1 scores are evaluated to compare the empirical results of each model. Based on this, a logistic regression-based outbound data risk scoring model is introduced to quantify the outbound data risk at a deeper level and to classify the outbound data risk level for the reference of regulators to make more scientific and reasonable decisions. The experimental results show that the machine learning models can meet the needs and applications of practical work and make accurate predictions of outbound data risks.

A Hardware Security-Monitoring Architecture Based on Data Integrity and Control Flow Integrity for Embedded Systems

As technology evolves, embedded systems access more networks and devices, which means more security threats. Existing security-monitoring methods with a single parameter (data or control flow) are not effective in detecting attackers tampering with the data or control flow of an embedded system. However, simply overlaying multiple security methods will result in excessive performance overhead for embedded systems. In this paper, we propose a novel hardware security-monitoring architecture that extracts DI (data integrity) digests and CFI (control flow integrity) tags to generate reference information when the program is offline. To monitor the indirect jumping behavior, this paper maps the legal target addresses into the bitmap, thus saving the search time. When the program is loaded, the reference information and the bitmap are safely loaded into the on-chip memory. The hardware monitoring module designed in this paper will check the DI summary and CFI tags in real time while executing the program. The architecture proposed in this paper has been implemented on the Xilinx Virtex 5 FPGA platform. Experimental results show that, compared with existing protection methods, the proposed approach in this paper can effectively detect multiple tampering-type attacks on the data and control flow of the embedded system, with a performance overhead of about 6%.

IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories

The smart factory environment has been transformed into an Industrial Internet of Things (IIoT) environment, which is an interconnected and open approach. This has made smart manufacturing plants vulnerable to cyberattacks that can directly lead to physical damage. Most cyberattacks targeting smart factories are carried out using malware. Thus, a solution that efficiently detects malware by monitoring and analyzing network traffic for malware attacks in smart factory IIoT environments is critical. However, achieving accurate real-time malware detection in such environments is difficult. To solve this problem, this study proposes an edge computing-based malware detection system that efficiently detects various cyberattacks (malware) by distributing vast amounts of smart factory IIoT traffic information to edge servers for deep learning processing. The proposed malware detection system consists of three layers (edge device, edge, and cloud layers) and utilizes four meaningful functions (model training and testing, model deployment, model inference, and training data transmission) for edge-based deep learning. In experiments conducted on the Malimg dataset, the proposed malware detection system incorporating a convolutional neural network with image visualization technology achieved an overall classification accuracy of 98.93%, precision of 98.93%, recall of 98.93%, and F1-score of 98.92%.

An Intelligent System to Detect Advanced Persistent Threats in Industrial Internet of Things (I-IoT)

The Industrial Internet of Things (I-IoT) is a manifestation of an extensive industrial network that interconnects various sensors and wireless devices to integrate cyber and physical systems. While I-IoT provides a considerable advantage to large-scale industrial enterprises, it is prone to significant security challenges in the form of sophisticated attacks such as Advanced Persistent Threat (APT). APT is a serious security challenge to all kinds of networks, including I-IoT. It is a stealthy threat actor, characteristically a nation-state or state-sponsored group that launches a cyber attack intending to gain unauthorized access to a computer network and remain undetected for a longer period. The latest intrusion detection systems face several challenges in detecting such complex cyber attacks in multifarious networks of I-IoT, where unpredictable and unexpected cyber attacks of such sophistication can lead to catastrophic effects. Therefore, these attacks need to be accurately and promptly detected in I-IoT. This paper presents an intelligent APT detection and classification system to secure I-IoT. After pre-processing, several machine learning algorithms are applied to detect and classify complex APT signatures accurately. The algorithms include Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, Bagging, Extreme Gradient Boosting and Adaboost, which are applied on a publicly available dataset KDDCup99. Moreover, a comparative analysis is conducted among ML algorithms to select the appropriate one for the targeted domain. The experimental results indicate that the Adaboost classifier outperforms the others with 99.9% accuracy with 0.012 s execution time for detecting APT attacks. Furthermore, results are compared with state-of-the-art techniques that depict the superiority of the proposed system. This system can be deployed in mission-critical scenarios in the I-IoT domain.