Cyber-security internals of a Skoda Octavia vRS: a hands on approach

The convergence of information technology and vehicular technologies are a growing paradigm, allowing information to be sent by and to vehicles. This information can further be processed by the Electronic Control Unit (ECU) and the Controller Area Network (CAN) for in-vehicle communications or through a mobile phone or server for out-vehicle communication. Information sent by or to the vehicle can be life-critical (e.g. breaking, acceleration, cruise control, emergency communication, etc … ). As vehicular technology advances, in-vehicle networks are connected to external networks through 3 and 4G mobile networks, enabling manufacturer and customer monitoring of different aspects of the car. While these services provide valuable information, they also increase the attack surface of the vehicle, and can enable long and short range attacks. In this manuscript, we evaluate the security of the 2017 Skoda Octavia vRS 4x4. Both physical and remote attacks are considered, the key fob rolling code is successfully compromised, privacy attacks are demonstrated through the infotainment system, the Volkswagen Transport Protocol 2.0 is reverse engineered. Additionally, in-car attacks are highlighted and described, providing an overlook of potentially deadly threats by modifying ECU parameters and components enabling digital forensics investigation are identified

Machine learning based IoT Intrusion Detection System:an MQTT case study (MQTT-IoT-IDS2020 Dataset)

The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets

BET bromodomain protein inhibition is a therapeutic option for medulloblastoma

Medulloblastoma is the most common malignant brain tumor of childhood, and represents a significant clinical challenge in pediatric oncology, since overall survival currently remains under 70%. Patients with tumors overexpressing MYC or harboring a MYC oncogene amplification have an extremely poor prognosis. Pharmacologically inhibiting MYC expression may, thus, have clinical utility given its pathogenetic role in medulloblastoma. Recent studies using the selective small molecule BET inhibitor, JQ1, have identified BET bromodomain proteins, especially BRD4, as epigenetic regulatory factors for MYC and its targets. Targeting MYC expression by BET inhibition resulted in antitumoral effects in various cancers. Our aim here was to evaluate the efficacy of JQ1 against preclinical models for high-risk MYC-driven medulloblastoma. Treatment of medulloblastoma cell lines with JQ1 significantly reduced cell proliferation and preferentially induced apoptosis in cells expressing high levels of MYC. JQ1 treatment of medulloblastoma cell lines downregulated MYC expression and resulted in a transcriptional deregulation of MYC targets, and also significantly altered expression of genes involved in cell cycle progression and p53 signalling. JQ1 treatment prolonged the survival of mice harboring medulloblastoma xenografts and reduced the tumor burden in these mice. Our preclinical data provide evidence to pursue testing BET inhibitors, such as JQ1, as molecular targeted therapeutic options for patients with high-risk medulloblastomas overexpressing MYC or harboring MYC amplifications

Decreased expression of the mitochondrial bcat protein correlates with improved patient survival in idh-wt gliomas

Background and research question: Gliomas represent 43% of all solid intracranial tumours, of which glioblastomas have the poorest prognosis. Recently, the human cytosolic branched-chain aminotransferase protein (hBCATc), which metabolises the branched-chain amino acids (BCAA), was identified as a biomarker and therapeutic target for glioblastomas carrying wild-type isocitrate dehydrogenase (IDH-WT) genes. However, the clinical utility of the mitochondrial isoform, hBCATm, which also metabolises BCAAs, was not determined nor its potential role in predicting patient survival.Methods: Glioblastomas, of grades II-IV, from 53 patients were graded by a neuropathologist, where the IDH and MGMT status were assessed. Tumours positive for hBCATm, hBCATc and BCKDC were characterised using immunohistochemistry and Western blot analysis using antibodies specific to these proteins.Results: Here, we report that in IDH-WT tumours, the expression of hBCATm is significantly increased (p=0.034) relative to IDH mutation gliomas, and significantly correlates with patient survival, on Kaplan-Meier analysis, where low hBCATm expression is a positive prognostic factor (p=0.003). Moreover, increased hBCATm expression in these glioblastomas correlated with tumour grade indicating their role as a predictive biomarker of glioma progression. Multiple banding was observed for the branched-chain α-keto acid dehydrogenase complex, which catalyses the committed step in BCAA metabolism, but a significant change in expression was absent (p=0.690). Conclusion: Until now, IDH-WT glioblastomas have a uniformly poor prognosis, however we demonstrate for the first time that relatively low hBCATm may select for a better performing subset within this group and may represent a therapeutic target in these hard to treat patients

Revalidation and electronic cataract surgery audit: a Scottish survey on current practice and opinion

PURPOSE: To determine current knowledge and opinion on revalidation, and methods of cataract surgery audit in Scotland and to outline the current and future possibilities for electronic cataract surgery audit. METHODS: In 2010 we conducted a prospective, cross-sectional, Scottish-wide survey on revalidation knowledge and opinion, and cataract audit practice among all senior NHS ophthalmologists. Results were anonymised and recorded manually for analysis. RESULTS: In all, 61% of the ophthalmologists surveyed took part. Only 33% felt ready to take part in revalidation, whereas 76% felt they did not have adequate information about the process. Also, 71% did not feel revalidation would improve patient care, but 85% agreed that cataract surgery audit is essential for ophthalmic practice. In addition, 91% audit their cataract outcomes; 52% do so continuously. Further, 63% audit their subspecialist surgical results. Only 25% audit their cataract surgery practice electronically, and only 12% collect clinical data using a hospital PAS system. Funding and system incompatibility were the main reasons cited for the lack of electronic audit setup. Currently, eight separate hospital IT patient administration systems are used across 14 health boards in Scotland. CONCLUSION: Revalidation is set to commence in 2012. The Royal College of Ophthalmologists will use cataract outcome audit as a tool to ensure surgical competency for the process. Retrospective manual auditing of cataract outcome is time consuming, and can be avoided with an electronic system. Scottish ophthalmologists view revalidation with scepticism and appear to have inadequate knowledge of the process. However, they strongly agree with the concept of cataract surgery audit. The existing and future electronic applications that may support surgical audit are commercial electronic records, web-based applications, centrally funded software applications, and robust NHS connections between community and hospital

Mitiq : a software package for error mitigation on noisy quantum computers

We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Mitiq is an extensible toolkit of different error mitigation methods, including zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression. The library is designed to be compatible with generic backends and interfaces with different quantum software frameworks. We describe Mitiq using code snippets to demonstrate usage and discuss features and contribution guidelines. We present several examples demonstrating error mitigation on IBM and Rigetti superconducting quantum processors as well as on noisy simulators

Internet of Things for Sustainability: Perspectives in Privacy, Cybersecurity, and Future Trends

In the sustainability IoT, the cybersecurity risks to things, sensors, and monitoring systems are distinct from the conventional networking systems in many aspects. The interaction of sustainability IoT with the physical world phenomena (e.g., weather, climate, water, and oceans) is mostly not found in the modern information technology systems. Accordingly, actuation, the ability of these devices to make changes in real world based on sensing and monitoring, requires special consideration in terms of privacy and security. Moreover, the energy efficiency, safety, power, performance requirements of these device distinguish them from conventional computers systems. In this chapter, the cybersecurity approaches towards sustainability IoT are discussed in detail. The sustainability IoT risk categorization, risk mitigation goals, and implementation aspects are analyzed. The openness paradox and data dichotomy between privacy and sharing is analyzed. Accordingly, the IoT technology and security standard developments activities are highlighted. The perspectives on opportunities and challenges in IoT for sustainability are given. Finally, the chapter concludes with a discussion of sustainability IoT cybersecurity case studies

The prevalence of adaptive immunity to COVID-19 and reinfection after recovery - a comprehensive systematic review and meta-analysis.

This study aims to estimate the prevalence and longevity of detectable SARS-CoV-2 antibodies and T and B memory cells after recovery. In addition, the prevalence of COVID-19 reinfection and the preventive efficacy of previous infection with SARS-CoV-2 were investigated. A synthesis of existing research was conducted. The Cochrane Library, the China Academic Journals Full Text Database, PubMed, and Scopus, and preprint servers were searched for studies conducted between 1 January 2020 to 1 April 2021. Included studies were assessed for methodological quality and pooled estimates of relevant outcomes were obtained in a meta-analysis using a bias adjusted synthesis method. Proportions were synthesized with the Freeman-Tukey double arcsine transformation and binary outcomes using the odds ratio (OR). Heterogeneity was assessed using the I and Cochran's Q statistics and publication bias was assessed using Doi plots. Fifty-four studies from 18 countries, with around 12,000,000 individuals, followed up to 8 months after recovery, were included. At 6-8 months after recovery, the prevalence of SARS-CoV-2 specific immunological memory remained high; IgG - 90.4% (95%CI 72.2-99.9, I = 89.0%), CD4+ - 91.7% (95%CI 78.2-97.1y), and memory B cells 80.6% (95%CI 65.0-90.2) and the pooled prevalence of reinfection was 0.2% (95%CI 0.0-0.7, I = 98.8). Individuals previously infected with SARS-CoV-2 had an 81% reduction in odds of a reinfection (OR 0.19, 95% CI 0.1-0.3, I = 90.5%). Around 90% of recovered individuals had evidence of immunological memory to SARS-CoV-2, at 6-8 months after recovery and had a low risk of reinfection