Security Approach Research Articles

Implementasi Nilai Nilai Kebangsaan Berbasis NKRI Terhadap Pentingnya Manajemen Security di Era Digitalisasi

The era of digitalization has brought significant changes in various aspects of life, including in security management. The wider application of digital technology requires an adaptive and innovative security approach to deal with evolving threats. In the context of the Unitary State of the Republic of Indonesia (NKRI), the implementation of national values in digital security management is very important. These values include unity, integrity, justice, democracy and diversity, which are the foundations of NKRI. This research aims to analyze the importance of security management in the digitalization era based on the NKRI's national values.The method used in this research is descriptive qualitative, with a literature study approach and document analysis related to digital security policies in Indonesia. The analysis focused on how NKRI national values are implemented in security management strategies and practices to face digital security challenges. The results show that the integration of national values in digital security management plays an important role in strengthening national resilience in the digital space. The implementation of national values helps in building a digital security system that is not only technically robust, but also responsive to Indonesia's social and cultural dynamics. This includes the development of inclusive cybersecurity policies, digital security education for diverse communities, and strengthening collaboration between sectors to strengthen the national digital security ecosystem.

Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation-based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution-based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.

A Data Attack Detection Framework for Cryptography-Based Secure Aggregation Methods in 6G Intelligent Applications

Eagerly anticipated, 6G networks are attributed with a variety of characteristics by researchers. A pivotal characteristic of 6G networks is the deep integration of sensing and networking, along with intelligent network applications operating on top of this infrastructure. To optimally harness the data collected by sensors distributed across various locations, the training paradigm of the new generation of 6G intelligence applications aligns naturally with the federated-learning paradigm. The exposure of gradients in federated learning to inversion attacks is a critical concern. To address this, cryptography-based secure aggregation methods are commonly implemented to protect the privacy and confidentiality of gradients. However, the semantic meaninglessness of encrypted data makes it difficult to assess the correctness, availability, and source legitimacy of participants’ data. In this paper, we propose a data attack detection framework for cryptography-based secure aggregation methods in 6G intelligence applications that address the security vulnerabilities associated with encrypted data obscurity. We employ a suite of encrypted-data-auditing techniques to prevent data-aggregation errors, data poisoning, and illegal data sources. Additionally, we have compared a series of promising security methods, analyzed, and provided recommendations for the most suitable security approaches in specific 6G scenarios.

Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis

We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pretrained multimodal large language models such as GPT-4 to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks, customizing smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction tuned language-and-vision assistant. Our framework merges knowledge engineering with machine learning to integrate domain-specific expertise from larger to smaller multimodal models within this specialized field, greatly reducing the need for extensive human labeling. Our study presents a secure, cost-effective, and customizable approach for analyzing microscopy images, addressing the challenges of adopting proprietary models in semiconductor manufacturing.

Perioral dermatitis: nuances of the clinical picture and therapy in different age periods

Perioral dermatitis is a common dermatosis that predominantly affects women aged 25–40 but is also observed in children and adolescents. The distinct localization of this condition in the perioral, perinasal, and periocular zones contributes to apparent psycho-emotional distress, markedly impairing patients’ quality of life. Perturbations in the skin biotope, local immune dysregulation, and topical glucocorticosteroid use are among the primary etiological factors. Effective management involves addressing causative factors, discontinuation of topical steroids, adopting gentle skincare practices, and employing localized treatments like metronidazole, pimcrolimus, and activated zinc pyrithione. Maintenance therapy may be warranted in select cases. The purpose of this work is to review the current literature on the problem and to present original clinical observations concerning the utilization of activated zinc pyrithione in treating patients with this condition. The article presents the results of the use of topical activated zinc pyrithione in patients spanning various age groups and exhibiting diverse degrees of perioral dermatitis severity. Significant clinical evidence substantiates the remarkable efficacy and safety profile of activated zinc pyrithione in managing perioral dermatitis, both as a standalone therapeutic modality and as an integral component of combination therapy. The use of activated zinc pyrithione in the treatment of perioral dermatitis emerges as an efficacious and secure approach for treating mild to moderate forms of the condition.

Preserved structure and function of human serum albumin self-folded in the oxidative cytoplasm of Escherichia coli

Human serum albumin (HSA), a polypeptide featuring 17 disulfide bonds, acts as a crucial transport protein in human blood plasma. Its extended circulation half-life, mediated by FcRn (neonatal Fc receptor)-facilitated recycling, positions HSA as an excellent carrier for long-acting drug delivery. However, the conventional method of obtaining HSA from human blood faces limitations due to availability and potential contamination risks, such as blood-borne diseases. This study introduced SHuffle, an oxidative Escherichia coli (E. coli) expression system, for the production of recombinant HSA (rHSA) that spontaneously self-folds into its native conformation. This system ensures precise disulfide bond formation and correct folding of cysteine-rich rHSA, eliminating the need for chaperone co-expression or domain fusion of a folding enhancer. The purified rHSA underwent thorough physicochemical characterization, including mass spectrometry, circular dichroism spectroscopy, intrinsic fluorescence spectroscopy, esterase-like activity assay, and size exclusion chromatography, to assess critical quality attributes. Importantly, rHSA maintained native binding affinity to FcRn and the albumin-binding domain. Collectively, our analyses demonstrated a high comparability between rHSA and plasma-derived HSA. The expression of rHSA in E. coli with an oxidizing cytosol provides a secure and cost-effective approach, enhancing the potential of rHSA for diverse medical applications.

A Novel High-Precision Railway Obstacle Detection Algorithm Based on 3D LiDAR.

This article presents a high-precision obstacle detection algorithm using 3D mechanical LiDAR to meet railway safety requirements. To address the potential errors in the point cloud, we propose a calibration method based on projection and a novel rail extraction algorithm that effectively handles terrain variations and preserves the point cloud characteristics of the track area. We address the limitations of the traditional process involving fixed Euclidean thresholds by proposing a modulation function based on directional density variations to adjust the threshold dynamically. Finally, using PCA and local-ICP, we conduct feature analysis and classification of the clustered data to obtain the obstacle clusters. We conducted continuous experiments on the testing site, and the results showed that our system and algorithm achieved an STDR (stable detection rate) of over 95% for obstacles with a size of 15 cm × 15 cm × 15 cm in the range of ±25 m; at the same time, for obstacles of 10 cm × 10 cm × 10 cm, an STDR of over 80% was achieved within a range of ±20 m. This research provides a possible solution and approach for railway security via obstacle detection.

Requisites of Deradicalisation: Study on the De-Ideologisation of Indonesian Ex-Terrorists

Studies on terrorism in Indonesia heavily emphasize a security-first approach, which has had profound implications for counter-terrorism strategies, policies, and terrorist imprisonment. This approach, however, fails to properly grasp the internal “deep experience” of terrorists. There are currently 289 terrorist convicts serving their prison sentences in 113 Correctional Institutions throughout Indonesia. Until 2017, over 60 convicted terrorists have been documented for recidivist behaviour and repeating their crimes. This phenomenon raises the question of the possibility of de-ideologization of terrorists in incarceration and if Indonesia’s criminal justice system is truly effective in deradicalizing terrorists. Our study explores this question by interviewing three different groups: ex-terrorists who have served their prison sentence, terrorists who were still incarcerated in prison, and former sympathizers of ISIS who had been deported back from Syria to Indonesia. We explored the social elements and actors that constituted their radical view, their experiences in prison, and the factors that had led them to abandon terrorist activities or disavow their support for ISIS. We conclude that social-legal policies aiming to effectively reduce the ideological reproduction of terrorism cannot be carried out using a heavy-handed security approach, but sociological insights are required to grasp the allure and kernel of truth behind terrorist acts.

Consumer Confidence in Digital Finance: Trust Dynamics in Online Banking

Consumer Confidence in Digital Finance: Trust Dynamics in Online Banking

Enhanced image encryption using AES algorithm with CBC mode: a secure and efficient approach

Enhanced image encryption using AES algorithm with CBC mode: a secure and efficient approach

Security augmenting of optical cryptosystem based on linear canonical transform domain using a full phase encoding technique

For the purpose of alleviating the vulnerability of double random phase encryption system in the linear canonical transform domain, a novel approach for optical security and cryptographic systems is presented. This proposed system uses a fully phase encoding technique to augment the security of encryption system in the linear canonical transform. The first step in this system involves phase encoding of the initial amplitude image to be ciphered and then modulated by the phase masks. The decryption process of image is the reversal operation of the encryption method. The effectiveness and sensitivity of our proposed cryptosystem for the encryption secret keys are verified. The resistance of our method against occlusion attacks is investigated. Moreover, the results demonstrate that the fully phase-based optical cryptosystem is more secure and robust than the amplitude-based scheme in a linear canonical domain.

DNA methylation markers in esophageal cancer.

Esophageal cancer (EC) is a prevalent malignancy characterized by a low 5-year survival rate, primarily attributed to delayed diagnosis and limited therapeutic options. Currently, early detection of EC heavily relies on endoscopy and pathological examination, which pose challenges due to their invasiveness and high costs, leading to low patient compliance. The detection of DNA methylation offers a non-endoscopic, cost-effective, and secure approach that holds promising prospects for early EC detection. To identify improved methylation markers for early EC detection, we conducted a comprehensive review of relevant literature, summarized the performance of DNA methylation markers based on different input samples and analytical methods in EC early detection and screening. This review reveals that blood cell free DNA methylation-based method is an effective non-invasive method for early detection of EC, although there is still a need to improve its sensitivity and specificity. Another highly sensitive and specific non-endoscopic approach for early detection of EC is the esophageal exfoliated cells based-DNA methylation analysis. However, while there are substantial studies in esophageal adenocarcinoma, further more validation is required in esophageal squamous cell carcinoma. In conclusion, DNA methylation detection holds significant potential as an early detection and screening technology for EC.

Border-making as illiberal politics

Abstract Radical Right-wing populism frequently involves ‘divide and rule’ strategies as a means to attain and consolidate political power. In the cases of Viktor Orban’s political regime in Hungary and Donald Trump’s four-year presidency (and its aftermath), we find a pronounced attempt to create narrative hegemony of a sense of nation built upon Christian civilization and foundationalist understandings of national identity. Moreover, both cases reveal processes of social border-making that are reflected in norming and the creation of distinctions based on degrees of national authenticity. Applying an ontological security and critical borders studies approach, this paper will specifically focus on the different ways in which border-making processes, or bordering, are implicated in the exercise of illiberal political power in the Hungarian and US cases. Despite many similarities, the actual mobilization of popular support reflects local conditions and has resulted in rather different political outcomes.

Dynamic trust security approach for edge computing-based mobile IoT devices using artificial intelligence

The Industrial Internet of Things (IIoT) encompasses various applications such as smart cities, healthcare, and agriculture, which rely on a multitude of interconnected devices. These applications demand specific performance requirements, including low latency and sufficient bandwidth. Edge computing has emerged as a promising solution to address the challenges associated with data flow processing. Particularly, in scenarios like smart cities, where a massive number of IIoT devices are deployed, edge computing serves as the primary data processing layer. However, the limited resources of edge computing, compared to cloud computing, pose significant security concerns for IIoT applications. In fact, this paper proposes a novel security system to secure the relation between the IIoT devices and edge computing. Dynamic Trust Security Approach (DTSA) leverages Artificial Intelligence to analyze, evaluate and enhance the security and privacy aspects of the IIoT devices’ interaction with edge computing. DTSA improve the security level of the IIoT system and improve the performance with more than 45% compare to the traditional method.

Web Development for Bank Statement Analysis: Technologies, Challenges, and Innovations

The purpose of this project is to fulfill the requirements of a bachelor’s degree in Cloud Technology and Information Security. The project aims to design and develop a Bank Statement Analysis system that provides a more secure approach to managing bank customers' statements, strengthening the relationship between banks and their customers by offering solutions with multi-level security to improve customer satisfaction. Methodology The project utilizes a range of technologies, including: · Python Flask Framework · Computer Vision (Google's Tesseract OCR) · PDF to Image Python package (pdf2image) · Machine Learning (Scikit-Learn) to predict transaction categories · Azure SQL Database to store transaction data and user login details · Power-BI for data visualization · Docker and AWS EC2 for service hosting. Findings The Bank Statement Analysis system developed in this project offers a secure and efficient solution for managing bank customers' statements. The use of multi-level security measures, such as Azure SQL Database and Power-BI visualizations, enhances the overall security and user experience, strengthening the relationship between banks and their customers. Conclusion This project demonstrates the successful design and development of a Bank Statement Analysis system that leverages various technologies, including cloud computing, computer vision, and machine learning, to provide a more secure and user-friendly solution for managing bank customers' statements. Keywords Azure SQL Database, Power-BI Tool, AWS EC2 Service

A New Hybrid Approach for Image Security using Steganography and Encryption

In today's digital age, ensuring the security and confidentiality of image data is paramount, particularly with the proliferation of online communication and storage platforms. This project proposes a hybrid approach for enhancing image security by synergistically integrating encryption and steganography techniques. The combination of these two methods aims to fortify the protection of digital images against unauthorized access and tampering. Encryption ensures the confidentiality of image content by converting it into an unintelligible form using robust cryptographic algorithms. Concurrently, steganography conceals the encrypted data within the image itself, embedding it imperceptibly into the pixels or metadata. The synergy of encryption and steganography not only safeguards the integrity of image data but also adds an additional layer of concealment, making it arduous for adversaries to detect and decipher sensitive information. Through this project, the efficacy of the hybrid approach will be evaluated through experimentation and analysis, with the ultimate goal of providing a comprehensive solution for image security in various applications and contexts.

Preference and Associated Factors for Cesarean Delivery Among Pregnant Women: A Cross-Sectional Study.

A cesarean delivery is regarded as a comparatively favourable and secure approach to childbirth when contrasted with vaginal delivery. Over the past decade, its frequency has risen in both industrialized and developing nations. Maternal request for cesarean delivery has been explained for the escalating rate along with other factors like anxiety, fear of childbirth, previous cesarean delivery, previous negative vaginal birth experience, maternal age, maternal education, and socioeconomic factors. Hence, this study was undertaken to assess pregnant women's tendency to have a cesarean birth and to investigate the factors associated with the inclination for cesarean delivery. A hospital-based cross-sectional study was carried out in the Department of Obstetrics and Gynaecology of a tertiary care hospital, a systematic sampling procedure was utilized, and 368 antenatal mothers after 36 weeks of gestation, who do not have any specific medical reasons against vaginal delivery were included in the study. Data collection was done by questionnaire. The information regarding socio-demographic factors, preexisting comorbidities, current obstetric risk factors, emotional factors, previous delivery experience, and their information preference toward the mode of delivery were collected. Univariate and multivariate analysis were performed to identify the independent variables associated with preference for cesarean delivery. The preference for cesarean delivery and non-preference for cesarean delivery was 114 (30.9%) and 201 (54.6%), respectively whereas 53 (14.4%) participants remained neutral. The Chi-square analysis revealed a notable connection between the inclination towards a preference for cesarean delivery and factors such as obstetric score, parity, comorbidities, and among obstetric risk factors such as pregnancy after in-vitro fertilization (IVF), with a history of abortion, and having a prior history of cesarean delivery. Nevertheless, no meaningful association was observed between the preference for cesarean delivery and the remaining variables. On multivariate logistic analysis, independent variables like preexisting anxiety or depression, pregnancy through IVF, and having a history of previous cesarean delivery have increased the odds of preferring cesarean delivery. The independent variables like increasing gestational age, graduates, and unemployed have decreased the odds of preferring a cesarean delivery. In conclusion, the prevalence of cesarean delivery is influenced by a complex interplay of medical, cultural, socioeconomic, and healthcare system factors. While cesarean delivery is essential in cases of medical necessity, efforts should be made to avoid unnecessary cesarean delivery that does not provide clear benefits over vaginal delivery. Balancing the risks and benefits of cesarean delivery and promoting evidence-based obstetric practices are crucial for ensuring optimal maternal and infant outcomes.

Collaborative defense in the Arctic: Strengthening Norway's oil sector resilience through knowledge sharing and vigilance against drone threats

AbstractNorway's oil sector faces significant security challenges due to rapid technological advancements and heightened geopolitical tensions, particularly concerning threats from Russian drones. The sector maintains a vigilant approach, continually monitoring and adapting to evolving threats. This study investigates strategies employed by sector companies to mitigate challenges, emphasizing collaboration, effective network utilization, and learning from past incidents to enhance operational resilience. Our analysis aligns with three thematic areas from the Generic Resilience Management Guidelines developed under the EU‐funded H2020 project DARWIN: enhancing coordination in emergency response operations, managing adaptive capacities, and updating procedures and checklists. The study underscores the role of task forces, interdisciplinary teams, and collaborative efforts among industry operators in enhancing adaptability to threats. Findings indicated that although a willingness to draw insights from past incidents exists, a predominant reactive approach to learning prevails. Furthermore, a culture of secrecy prevalent in the sector inhibits knowledge sharing, potentially challenging overall resilience. In this complex context, proactive and transparent security approaches are imperative for Norway's oil sector to enhance resilience. Organizations should adopt a dynamic, technology‐integrated risk management approach, involving continuous policy revision and stakeholder engagement, as suggested in contemporary resilience enhancement strategies.

Advantages of robot-assisted resection of large mediastinal tumors: a single-center preliminary study.

Current study aims to assess the safety and efficacy of robot-assisted thoracoscopic surgery (RATS) for sizable mediastinal masses with a minimum diameter ≥6cm, compared with video-assisted thoracoscopic surgery (VATS) and open surgery. This study enrolled 130 patients with mediastinal tumors with no less than 6 cm diameter in Zhongnan Hospital, Wuhan University, including 33 patients who underwent RATS, 52 patients who underwent VATS and 45 patients who underwent open surgery. After classifying based on mass size and whether it has invaded or not, we compared their clinical characteristics and perioperative outcomes. There was no significant difference in age, gender, mass size, myasthenia gravis, mass location, pathological types (p>0.05) in three groups. Patients undergoing open surgery typically presenting at a more advanced stage (p<0.05). No obvious difference was discovered in the average postoperative length of stay, operation duration, chest tube duration and average postoperative day 1 drainage output between RATS group and VATS group (p>0.05), while intraoperative blood loss in RATS group was significantly lower than VATS group (p=0.046). Moreover, the postoperative length of stay, operation duration, chest tube duration and intraoperative blood loss in RATS group were significantly lower than open surgery group (p<0.001). RATS is a secure and efficient approach for removing large mediastinal masses at early postoperative period. In comparison with VATS, RATS is associated with lower intraoperative blood loss. Compared with open surgery, RATS is also associated with shorter postoperative length of stay, operation duration, chest tube duration and intraoperative blood loss.

A novel STPA approach to software safety and security in autonomous maritime systems

With higher autonomy in maritime systems, tasks and responsibilities are moved from the human operator to software, increasing the complexity and the importance of safe and reliable functionality. Software failures, however, may be introduced from the early life cycle phases intentionally or unintentionally, and these must therefore be mitigated by safe and secure design approaches. A challenge is that existing methods are not particularly well-suited for analyzing software risks. Thus, the objective of this paper is to propose a systematic and efficient software failure identification approach by extending the Systems-Theoretic Process Analysis (STPA) with a software failure taxonomy and the System Modeling Language (SysML). This enables the control structure in STPA to cover both the dynamic and static aspects of the software functions. Combined with an implementation platform independent questionnaire, this gives a more systematic and guided search for potential software failures than existing approaches. To demonstrate the proposed approach, a case study on a ferry's navigation system that operates in manual control or semi-autonomous mode is performed. In the case study, the focus is on creating an avoidance map data structure, including both moving and static obstacles to be avoided by the ferry, and the subsequent process of collision risk warning calculation. Software failures are identified and evaluated in collision scenarios where the ferry operates under foggy conditions. The paper shows that the proposed systematic approach provides an improved process for identifying and analyzing critical software failures. This facilitates enhanced risk mitigation in the design and testing phases contributing to autonomous systems' safety and security.