Institute of Information Security

Available methods for predicting the onset and progression of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) are not yet ready for clinical application. We used a Japanese diabetes cohort study (J-DREAMS) to examine whether the Ahlqvist et al diabetes clustering is useful for stratifying DKD or ESKD outcomes independent of known risk factors in real-world settings. Data-driven cluster analysis using k-means was performed based on GAD antibody levels, age at diagnosis, BMI, HbA1c and HOMA2 estimates of beta cell function and insulin resistance in 12,093 individuals with type 1 or type 2 diabetes. The risk of developing DKD/ESKD was analysed using Kaplan-Meier analysis and the Cox proportional hazards model. Diabetes clustering classified individuals in the J-DREAMS cohort into five subtypes, the clinical characteristics of which were comparable to those of the previously reported five subtypes. Kaplan-Meier curve analysis showed that events for chronic kidney disease (CKD) stages 3b, 4 and 5 were highest in the severe insulin-resistant diabetes subtype. The Cox proportional hazards model showed that the severe insulin-resistant diabetes subtype had significant HRs after correction for multiple confounding factors. The Cox proportional hazards model showed that each subtype had a diverse combination of factors associated with CKD stage 3b and proteinuria events. Data-driven analysis provides diabetes subtyping, which can predict the probability of developing DKD/ESKD; each subtype has diverse combinations of factors predisposing to DKD development and progression. Data-driven diabetes subtyping to predict the likelihood of developing DKD/ESKD and mitigating predisposing factors may help personalise prevention strategies.

This paper examines the principal drivers and existing approaches for implementing information security controls, highlighting gaps in common guidance. To help information security professionals, a new approach is proposed as an adaptable ten-method model to support effective implementation and operationalisation of security controls. Two control implementation scenarios illustrate the model’s practical applicability.

Background/Objectives:Mycobacterium avium, a member of Mycobacterium avium complex (MAC), is an emerging opportunistic pathogen causing MAC-pulmonary disease (PD). Fluoroquinolones (FQs), along with ethambutol (EMB) and rifampicin, are recommended for macrolide-resistant MAC-PD; however, FQ-resistant M. avium have been reported worldwide. WQ-3810 is an FQ with high potency against FQ-resistant pathogens; however, its activity against M. avium has not yet been studied. Methods: In this study, we conducted a DNA supercoiling inhibitory assay to evaluate the inhibitory effect of WQ-3810 on recombinant wild-type (WT) and four mutant DNA gyrases of M. avium and compared the IC50s of WQ-3810 with those of ciprofloxacin (CIP), levofloxacin (LVX), and moxifloxacin (MXF). In addition, we examined WQ-3810's antimicrobial activity against 11 M. avium clinical isolates, including FQ-resistant isolates, with that of other FQs. Furthermore, we assessed the synergistic action of WQ-3810 with the combination of either EMB or isoniazid (INH). Results: In a DNA supercoiling inhibitory assay, WQ-3810 showed 1.8 to 13.7-fold higher efficacy than LVX and CIP. In the MIC assay, WQ-3810 showed 4 to 8-fold, 2 to 16-fold, and 2 to 4-fold higher antimicrobial activity against FQ-resistant isolates than CIP, LVX, and MXF, respectively. The combination of WQ-3810 and INH exhibited a synergistic relationship. Conclusions: The overall characteristics of WQ-3810 demonstrated greater effectiveness than three other FQs, suggesting that it is a promising option for treating FQ-resistant M. avium infections.

Plants are integral to human life, and so, plant health is important. Regularly monitoring of plant health and plant disease detections are important in property agriculture. In agriculture, the use of image processing techniques run by computers in solving agricultural problems is increasingly common, particularly in the classification and identification of crop disease. Such usage could preserve the technical and commercial well-being of agriculture. This study demonstrated the application of support vector machine and image processing-enabled approach to detect and classify Olive leaf disease. It comprises seven steps that begin with a presentation of a digital color picture of a sickly leaf, followed by the step of image denoising using mean function, image enhancing using CLAHE method, image segmentation using fuzzy C Means algorithm, image feature extraction using PCA, and disease detection and classification using PSO SVM, BPNN, and random forest algorithms. The results showed high accuracy of the proposed PSO SVM in Olive leaf disease classification and detection.

Increasing attention to digital identity and self-sovereign identity (SSI) is gaining momentum. SSI brings various benefits to natural persons, such as owning controls; conversely, digital identity systems in the real world require Sybil-resistance to comply with anti-money laundering (AML) and other needs. CanDID by Maram et al. proposed that decentralized digital identity systems may achieve Sybil-resistance and preserve privacy by utilizing multi-party computation (MPC), assuming a distributed committee of trusted nodes. Pass et al. proposed the formal abstraction of attested execution secure processors (AESPs) while equipping hardware-assisted security in mobile devices has become the norm. We first describe our proposal to utilize AESPs for building secure Sybil-resistant SSI systems, the architecture with a set of system protocols <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Pi ^{{\mathcal {G}}_{\mathtt {att}}}$ </tex-math></inline-formula>, which brings drastic flexibility and efficiency compared to existing systems. In addition, we propose a novel scheme that enables users (holders) to request verifiers to verify their credentials without AESPs, and it further achieves unlinkability among credentials created for public verification. Our scheme introduces a simplified format for computed claims and commitment-based anonymous identifiers. We also describe a technique to utilize zero-knowledge membership proofs, in particular, “One-Out-of-Many Proofs” <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Sigma $ </tex-math></inline-formula>-protocol by Groth and Kohlweiss, which can prove the existence of an expected credential without identifying it. Along with other techniques, such as utilizing the BBS+ signature scheme, we demonstrate how our scheme can achieve its goals with the extended anonymous and Sybil-resistant SSI system protocols <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Pi ^{{\mathcal {G}}_{\mathtt {att}}+}$ </tex-math></inline-formula>. Entitling unlinkability among derived credentials in the anonymous Sybil-resistant SSI results in proper privacy preservation.

Autonomous marine vehicles face escalating cyber-security threats as connectivity and automation increase attack surfaces. Cybercriminals have increased security intrusions targeting the marine industry by 400% during the COVID-19 epidemic. This research is a work-in-progress study investigating the feasibility of developing a modular battleship simulator and a simultaneous navigation and security artificial intelligence (AI) monitoring system with machine learning (ML) technologies. We used a simple but representative threat model based on GPS spoofing and integrity attacks against the weapons system and ICS network operations. Results indicate the feasibility of the modular battleship simulator and the associated AI monitoring system. The simulator utilizes maritime vehicle physics, rudimentary weapons systems operations, and ICS network operations. Most importantly, we demonstrate the ability of AI monitors to guard against navigation and network operation anomalies that would jeopardize a ship and/or its associated mission.

Graph edit distance (GED) is a fundamental graph similarity metric. GED computation is NP-hard [10], and exact GED computation is only feasible for small graphs. Therefore, many methods of approximate GED computation have been proposed in the literature. In this paper, we select the five representative GED approximation methods and compare their performance on two real-world datasets. We observe that non-heuristic algorithms such as LSa [1] are fast and accurate in computing true GED for small graphs, and heuristic algorithms such as GENN [4] are very effective in computing the estimated path cost. This effort helps us pinpoint suitable algorithms for different applications.