Identification Of Vulnerabilities Research Articles

Identification and assessment of the mudflat ecological vulnerability dominated by coastline evolution in Jiangsu

Coastlines have experienced profound alterations due to human disturbances and ocean dynamics against the backdrop of climate change and intensified anthropogenic activities. Significant modifications have occurred within the muddy coastal ecosystem, rendering it extremely susceptible to heightened levels of vulnerability. However, existing studies on coastal eco-environmental vulnerability have often overlooked crucial factors such as coastline erosion and siltation, particularly concerning coastline change. In this study, we assess coastline stability utilizing Landsat remote sensing imagery to investigate the vulnerability of coastal ecology alongside 24 economic and natural parameters, serving as indices for the Coastal Vulnerability Index (CVI) of the coastal eco-environment. Subsequently, we analyze the impact factors to vulnerability and predict trends in coastline change over the next 20 years to propose recommendations for sustainable development to safeguard the coastal eco-environment. Our findings reveal a trend towards coastline stabilization in Jiangsu, with significantly higher stability observed during 2010–2020 compared to 1985–2010. The overall CVI along the Jiangsu coast shows an downward trajectory over the past 15 years, primarily influenced by tidal action and coastal topography and geomorphology. Predictive modeling suggests Jiangsu coastline is bounded by the Sheyang estuary, with the coastline to the north dominated by stabilization and to the south dominated by siltation. Climate change, coupled with intensified human activities and future coastline alterations, may exacerbate vulnerability. Effective protection strategies for coastal ecosystems necessitate a comprehensive understanding of local natural conditions, resource availability, and socioeconomic dynamics. Governmental interventions should focus on reinforcing regulatory frameworks for coastal organism management, implementing coastal protection infrastructure, and enhancing supervision and management protocols for beachfront development, thereby fostering the sustainable management of muddy coastlines.

Identification of targetable epigenetic vulnerabilities in uveal melanoma.

Uveal melanoma (UM) is the most prevalent primary intraocular malignancy in adults, which preferentially metastasizes to the liver in approximately half of all cases. Metastatic UM is notoriously resistant to therapy and is almost uniformly fatal. UM metastasis is most strongly associated with mutational inactivation of the BAP1 tumor suppressor gene. Given the role of BAP1 in epigenetic regulation as the ubiquitin hydrolase subunit of the polycomb repressive deubiquitinase (PR-DUB) complex, we conducted high-throughput drug screening using a well-characterized epigenetic compound library to identify new therapeutic vulnerabilities. We identified several promising new lead compounds, in particular the extra-terminal domain protein (BET) inhibitor mivebresib (ABBV-075). Mivebresib significantly improved survival rates in a metastatic uveal melanoma xenograft mouse model and entirely prevented detectable metastases to the bones, spinal cord, and brain. RNA sequencing revealed a notable overlap between the genes and pathways affected by HDAC and BET inhibition, including the reversal of gene signatures linked to high metastatic risk and upregulation of genes associated with a neuronal phenotype. Together, we found that UM cells are particularly vulnerable to class I HDAC and BET inhibition, and highlight the BET inhibitor mivebresib as a promising candidate for further clinical evaluation.

A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies

The number of new vulnerabilities continues to rise significantly each year. Simultaneously, vulnerability databases have challenges in promptly sharing new security events with enough information to improve protections against emerging cyberattack vectors and possible exploits. In this context, several organizations adopt strategies to protect their data, technologies, and infrastructures from cyberattacks by implementing anticipatory and proactive approaches to their system security activities. To this end, vulnerability management systems play a crucial role in mitigating the impact of cyberattacks by identifying potential vulnerabilities within an organization and alerting cyber teams. However, the effectiveness of these systems, which employ multiple methods and techniques to identify weaknesses, relies heavily on the accuracy of published security events. For this reason, we introduce a discussion concerning existing vulnerability detection methods through an in-depth literature study of several research papers. Based on the results, this paper points out some issues related to vulnerability databases handling that impact the effectiveness of certain vulnerability identification methods. Furthermore, after summarizing the existing methodologies, this study classifies them into four approaches and discusses the challenges, findings, and potential research directions.

Examining the effects of the HIV-1 protein Tat and morphine on antiretroviral accumulation and distribution within the brain.

Despite combination antiretroviral therapy effectively suppressing HIV within the periphery, neuro-acquired HIV (neuroHIV) remains a significant problem and approximately half of people living with HIV will experience HIV-associated neurocognitive disorders (HAND). Concurrent opioid use exacerbates neuroHIV by promoting neuroinflammation, neuronal injury and synaptodendritic culling, viral replication, and potentially altering antiretroviral concentrations within the brain. The present study examined the effects of HIV and morphine co-exposure on the accumulation and spatial distribution of antiretroviral drugs across multiple regions within the brain in an HIV-1 Tat transgenic mouse model by using infrared-matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI MSI). Morphine exposure uniquely decreased antiretroviral concentrations in anterior cerebral (primary motor and somatosensory) cortices, corpus collosum (anterior forceps), caudoputamen, nucleus accumbens, and posterior regions including the hippocampus, corpus callosum (main body), cerebral cortex (somatosensory and auditory cortices), thalamus, and hypothalamus. Interestingly, male mice experienced greater morphine-associated decreases in antiretroviral concentrations than females. The study also assessed whether changes in antiretroviral concentrations were linked with inflammation in astroglia, assessed through the measurement of astroglial activation using glial fibrillary acidic protein (GFAP) as a marker. Alterations in antiretroviral concentrations co-registering with areas of astroglial activation exhibited sex-specific treatment differences. This study highlights the intricate interplay between HIV, opioids, and antiretroviral drugs within the CNS, elucidating distinct regional and sex variations in responsiveness. Our findings emphasize the identification of vulnerabilities within the neural landscape and underscore the necessity of carefully monitoring opioid use to maintain the efficacy of antiretroviral therapies.

Enhancement of Triple-Negative Breast Cancer-Specific Induction of Cell Death by Silver Nanoparticles by Combined Treatment with Proteotoxic Stress Response Inhibitors.

Metal nanoparticles have been tested for therapeutic and imaging applications in pre-clinical models of cancer, but fears of toxicity have limited their translation. An emerging concept in nanomedicine is to exploit the inherent drug-like properties of unmodified nanomaterials for cancer therapy. To be useful clinically, there must be a window between the toxicity of the nanomaterial to cancer and toxicity to normal cells. This necessitates identification of specific vulnerabilities in cancers that can be targeted using nanomaterials without inducing off-target toxicity. Previous studies point to proteotoxic stress as a driver of silver nanoparticle (AgNPs) toxicity. Two key cell stress responses involved in mitigating proteotoxicity are the heat shock response (HSR) and the integrated stress response (ISR). Here, we examine the role that these stress responses play in AgNP-induced cytotoxicity in triple-negative breast cancer (TNBC) and immortalized mammary epithelial cells. Furthermore, we investigate HSR and ISR inhibitors as potential drug partners to increase the anti-cancer efficacy of AgNPs without increasing off-target toxicity. We showed that AgNPs did not strongly induce the HSR at a transcriptional level, but instead decreased expression of heat shock proteins (HSPs) at the protein level, possibly due to degradation in AgNP-treated TNBC cells. We further showed that the HSR inhibitor, KRIBB11, synergized with AgNPs in TNBC cells, but also increased off-target toxicity in immortalized mammary epithelial cells. In contrast, we found that salubrinal, a drug that can sustain pro-death ISR signaling, enhanced AgNP-induced cell death in TNBC cells without increasing toxicity in immortalized mammary epithelial cells. Subsequent co-culture studies demonstrated that AgNPs in combination with salubrinal selectively eliminated TNBCs without affecting immortalized mammary epithelial cells grown in the same well. Our findings provide additional support for proteotoxic stress as a mechanism by which AgNPs selectively kill TNBCs and will help guide future efforts to identify drug partners that would be beneficial for use with AgNPs for cancer therapy.

Abstract A031: Identification of therapeutic vulnerabilities in the subset of pancreatic ductal adenocarcinoma (PDAC) with homologous recombination deficiency

Abstract The genetic landscape of pancreatic ductal adenocarcinoma (PDAC) is characterized by mutations in the KRAS, TP53, CDKN2A and SMAD4 genes alongside multiple low-frequency mutations, which may offer opportunities for precision medicine. The success of poly-ADP-ribose polymerase (PARP) inhibitors in metastatic PDAC characterized by germline mutations in the Breast Cancer (BRCA) genes has led to the hypothesis that mutations in other BRCAness genes may sensitize PDAC to PARP inhibitors. To test this hypothesis, we used KPC mice as a backbone (Pdx1-Cre; LSL-KrasG12D/+; LSL-Trp53R172H/+) to generate models of PDAC driven by the loss of Ataxia Telangiectasia mutated (Atm, KPCA) or Brca1 (KPCB). Loss of Atm or Brca1 resulted in homologous recombination deficiency and genomic instability in vivo. In vitro cultures of KPCA and KPCB PDAC cells had higher numbers of micronuclei and were more radiosensitive than KPC cells. Next, we performed bulk and single-cell RNA sequencing to identify therapeutic vulnerabilities associated with Atm or Brca1 loss. We found that loss of Atm upregulated ATR signaling, suggesting ATR as a therapeutic target in KPCA mice. In agreement, ATR inhibition improved the survival of KPCA mice, with evidence of tumor growth delay. However, though PARP inhibition significantly improved the survival of KPCB mice, with delayed tumor growth, it was ineffective in KPCA mice. In addition, transcriptomic analysis showed an enrichment of immune cells in KPCB PDAC compared to KPC and KPCA PDAC. Notably, we found greater levels of STING signaling, a more diverse and abundant cytokine profile and more CD8 T cells in KPCB PDAC, suggesting sensitivity to immune checkpoint blockade (ICB). However, ICB was ineffective in KPCB mice as a single agent or in combination with PARP inhibition. Based on our transcriptomic data, we hypothesized that boosting antigen priming using a CD40 agonist (CD40a) in combination with FLT3-L may improve ICB efficacy in our mouse models of BRCAness. Immunotherapy consisting of combined ICB, CD40a and FLT-3L had little efficacy in either KPCA or KPCB mice. However, ATR inhibition improved the efficacy of combined ICB, CD40a and FLT-3L in KPCA mice, as indicated by increased CD8 T cell infiltration in the tumor, delayed tumor growth and longer survival. In contrast, we found that PARP inhibition combined with ICB, CD40a and FLT-3L was ineffective in KPCB mice. In summary, we present evidence to suggest that PDAC characterized by BRCAness is not a homogeneous group with respect to sensitivity to PARP inhibition, composition of the tumor microenvironment and response to immunotherapy. We highlight ATM loss as a potential predictive biomarker of ATR inhibitor efficacy and immunotherapy in PDAC. Future directions include the evaluation of inflammation as a therapeutic vulnerability in murine PDAC with loss of Brca1. Ultimately, we aim to inform clinical trials to refine precision medicine of PDAC characterized by BRCAness. Citation Format: Mathias Tesson, Kay Kong, Peter Repsicak, Aldo Bader, Ya-Ching Hsieh, Craig Nourse, Kristina Kirschner, Crispin Miller, Ed Roberts, David Chang, Jen Morton. Identification of therapeutic vulnerabilities in the subset of pancreatic ductal adenocarcinoma (PDAC) with homologous recombination deficiency [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A031.

Securing industrial control systems: Developing a SCADA/IoT test bench and evaluating lightweight cipher performance on hardware simulator

This paper addresses the critical need for enhancing security in Supervisory Control and Data Acquisition (SCADA) networks within Industrial Control Systems (ICSs) to protect the industrial processes from cyber-attacks. The purpose of our work is to propose and evaluate lightweight security measures to safeguard critical infrastructure resources. The scope of our effort involves simulating a secure SCADA/IoT-based hardware test bench for ICSs, utilizing Modbus and MQTT communication protocols. Through case studies in remote servo motor control, water distribution systems, and power system voltage level indicators, vulnerabilities such as Denial of Service (DoS) and Man-in-The-Middle (MiTM) attacks are identified, and security recommendations are provided. To execute our work, we deploy lightweight ciphers such as Prime Counter & Hash Chaining (PCHC) and Ascon algorithm with Compression Rate (ACR) for secure information exchange between the plant floor and the control center. Evaluation of these ciphers on Raspberry Pi focuses on execution speed and memory utilization. Additionally, a comparison with the AGA-12 protocol standard for SCADA networks is conducted to underscore the efficacy of the proposed security measures. Our findings include the identification of SCADA network vulnerabilities and the proposal of lightweight security measures to mitigate risks. Performance evaluation of the proposed ciphers on Raspberry Pi demonstrates their effectiveness, emphasizing the importance of deploying such measures to ensure resilience against cyber threats in SCADA environments.

Screening of a kinase inhibitor library identified novel targetable kinase pathways in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly invasive breast cancer subtype that is challenging to treat due to inherent heterogeneity and absence of estrogen, progesterone, and human epidermal growth factor 2 receptors. Kinase signaling networks drive cancer growth and development, and kinase inhibitors are promising anti-cancer strategies in diverse cancer subtypes. Kinase inhibitor screens are an efficient, valuable means of identifying compounds that suppress cancer cell growth in vitro, facilitating the identification of kinase vulnerabilities to target therapeutically. The Kinase Chemogenomic Set is a well-annotated library of 187 kinase inhibitor compounds that indexes 215 kinases of the 518 in the known human kinome representing various kinase networks and signaling pathways, several of which are understudied. Our screen revealed 14 kinase inhibitor compounds effectively inhibited TNBC cell growth and proliferation. Upon further testing, three compounds, THZ531, THZ1, and PFE-PKIS 29, had the most significant and consistent effects across a range of TNBC cell lines. These cyclin-dependent kinase (CDK)12/CDK13, CDK7, and phosphoinositide 3-kinase inhibitors, respectively, decreased metabolic activity in TNBC cell lines and promote a gene expression profile consistent with the reversal of the epithelial-to-mesenchymal transition, indicating these kinase networks potentially mediate metastatic behavior. These data identified novel kinase targets and kinase signaling pathways that drive metastasis in TNBC.

APPROACH TO THE ANALYSIS OF SUCCESS CRITERIA AS A COMPONENT OF PROBABILISTIC PHYSICAL PROTECTION ANALYSIS OF NUCLEAR FACILITIES

An approach to adapting the methodology for analyzing success criteria from traditional probabilistic safety analysis (PSA) of nuclear power plants (NPPs) to probabilistic physical protection analysis (PPA) of nuclear facilities is demonstrated using the hypothetical nuclear facility SNRI as an example. Generally speaking, using the traditional PSA methodology for assessing the physical protection of nuclear facilities allows for the identification of new vulnerabilities in the physical protection systems of nuclear facilities, the development of recommendations to enhance physical protection efficiency, the performance of more substantiated cost-benefit analyses, and more.

Identification of digital device hardware vulnerabilities based on scanning systems and semi-natural modeling

Objectives. The development of computer technology and information systems requires the consideration of issues of their security, various methods for detecting hardware vulnerabilities of digital device components, as well as protection against unauthorized access. An important aspect of this problem is to study existing methods for the possibility and ability to identify hardware errors or search for errors on the corresponding models. The aim of this work is to develop approaches, tools and technology for detecting vulnerabilities in hardware at an early design stage, and to create a methodology for their detection and risk assessment, leading to recommendations for ensuring security at all stages of the computer systems development process.Methods. Methods of semi-natural modeling, comparison and identification of hardware vulnerabilities, and stress testing to identify vulnerabilities were used.Results. Methods are proposed for detecting and protecting against hardware vulnerabilities: a critical aspect in ensuring the security of computer systems. In order to detect vulnerabilities in hardware, methods of port scanning, analysis of communication protocols and device diagnostics are used. The possible locations of hardware vulnerabilities and their variations are identified. The attributes of hardware vulnerabilities and risks are also described. In order to detect vulnerabilities in hardware at an early design stage, a special semi-natural simulation stand was developed. A scanning algorithm using the Remote Bitbang protocol is proposed to enable data to be transferred between OpenOCD and a device connected to the debug port. Based on scanning control, a verification method was developed to compare a behavioral model with a standard. Recommendations for ensuring security at all stages of the computer systems development process are provided.Conclusions. This paper proposes new technical solutions for detecting vulnerabilities in hardware, based on methods such as FPGA system scanning, semi-natural modeling, virtual model verification, communication protocol analysis and device diagnostics. The use of the algorithms and methods thus developed will allow developers to take the necessary measures to eliminate hardware vulnerabilities and prevent possible harmful effects at all stages of the design process of computer devices and information systems.

Identification and Mapping of Flood Vulnerability in the Meninting Watershed, West Lombok

Flooding is the inundation of a flat area as a result of overflowing water from the surrounding area or an overflowing river. The largest river that crosses West Lombok Regency is the Meninting River. The Meninting watershed area as a catchment area has an area of 12,307.75 hectares. This research aims to identify and map flood-vulnerability areas in the Meninting Watershed. Data collection was carried out through a geographic information system (GIS) using overlay and scoring methods. The variables used to assess the level of flood vulnerability were land slope, soil type, rainfall, land cover, and geological aspects. The level of flood vulnerability is divided into three classes, namely not vulnerable, moderately vulnerable, and very vulnerable. The results show that of the 41 villages/sub-districts in the Meninting watershed, 34 villages/urban- villages with a coverage area of 8.21% were very vulnerable to flooding. The four villages that have the largest flood-vulnerable areas were the villages of North Ampenan (120.16 hectares), Midang (87.43 hectares), Dasan Griya (79.68 hectares), and Sesela (77.32 hectares). Around 56.24% of all villages/urban villages in the Meninting watershed were quite vulnerable to flooding and 35.55% of the area was not vulnerable.

Optimizing drug combinations for T-PLL: restoring DNA damage and P53-mediated apoptotic responses

AbstractT-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, that is, the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL’s pathobiology fostered the identification of actionable vulnerabilities: (1) altered epigenetics, (2) defective DNA damage responses, (3) aberrant cell-cycle regulation, and (4) deregulated prosurvival pathways, including T-cell receptor and JAK/STAT signaling. To further develop related preclinical therapeutic concepts, we studied inhibitors of histone deacetylases ([H]DACs), B-cell lymphoma 2 (BCL2), cyclin-dependent kinase (CDK), mouse double minute 2 (MDM2), and classical cytostatics, using (1) single-agent and combinatorial compound testing in 20 well-characterized and molecularly profiled primary T-PLL (validated by additional 42 cases) and (2) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single agents and combinations (in vitro and in mice) included cladribine, romidepsin ([H]DAC), venetoclax (BCL2), and/or idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance toward MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activation and downstream signals (including enhanced accessibility of target-gene chromatin regions), in particular synergy with insults by cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

PROACTIVE RISK IDENTIFICATION IN THE CHANGING AUTOMOTIVE INDUSTRY: A COMPREHENSIVE FRAMEWORK

Advancements in technology, shifts in consumer preferences, and revised regulations are all propelling rapid growth in the automotive industry. Traditional approaches to risk management are insufficient when confronted with the emerging risks presented by this transition. Adopting a proactive approach to detecting hazards is essential for successfully navigating this constantly evolving world. The present work presents a comprehensive framework for the proactive identification and management of risks within the ever-changing automotive sector. The essay commences with providing an overview of the industry's historical context and current advancements, followed by an analysis of the inadequacy of conventional risk management methods facing emerging risks. Furthermore, it proposes a complete framework specifically tailored to tackle the unique challenges faced by the automotive sector. Implementing a comprehensive approach to risk management, this framework integrates assessments of technology, markets, legislation, cybersecurity, and supply chain vulnerabilities. The analysis proceeds to discuss potential issues associated with implementing this methodology and offers remedies to overcome them. In order to enhance their decision-making and resilience in the face of industry-wide complexity, stakeholders in the automobile sector might adopt a pre-emptive approach to risk management. This study contributes to our knowledge of proactive risk management strategies and enhances our understanding of future risk identification in the automobile industry. Methodology: By employing this approach, these enterprises can enhance their ability to make well-informed judgements on their risk management plans. The technique facilitates the identification of critical vulnerabilities, therefore enabling the implementation of preventive measures to mitigate the effects of specific risks on net income. Moreover, the model possesses the capability to undertake data analysis from multiple companies within the same industry, thereby offering potential insights that can be extrapolated to other similar organisations. This has the potential to result in enhanced risk management strategies within India's automobile industry.

Enhancing cybersecurity protocols in the era of big data and advanced analytics

In the modern digital landscape, the exponential growth of big data and the proliferation of advanced analytics present both unprecedented opportunities and significant challenges for cybersecurity. This review explores the imperative of enhancing cybersecurity protocols to safeguard sensitive information and ensure the integrity of digital infrastructures in an era characterized by vast data generation and sophisticated analytical techniques. As organizations across various sectors leverage big data to drive innovation and gain competitive advantages, they simultaneously face heightened risks from cyber threats. Advanced analytics, including machine learning and artificial intelligence, offer potent tools for detecting and mitigating these threats. However, the integration of such technologies into cybersecurity frameworks demands a comprehensive and forward-thinking approach. Key to this enhancement is the development of robust data governance policies that ensure data integrity, confidentiality, and availability. These policies must address the complexities introduced by diverse data sources, varied data formats, and the velocity at which data is generated and processed. Additionally, the implementation of machine learning algorithms can significantly improve threat detection capabilities by identifying patterns and anomalies indicative of cyber threats, thus enabling proactive defense mechanisms. Moreover, enhancing cybersecurity protocols involves the adoption of encryption techniques and secure communication channels to protect data both at rest and in transit. Continuous monitoring and real-time analytics are crucial for maintaining situational awareness and promptly responding to potential breaches. The utilization of big data analytics also facilitates the identification of vulnerabilities and the assessment of risk profiles, allowing for the prioritization of security measures based on threat severity and impact. Despite the technological advancements, challenges such as data privacy concerns, algorithmic biases, and the need for skilled cybersecurity professionals persist. Addressing these challenges requires a multi-faceted strategy encompassing regulatory compliance, ethical considerations, and ongoing education and training. In conclusion, enhancing cybersecurity protocols in the era of big data and advanced analytics is essential for protecting critical digital assets and maintaining trust in digital ecosystems. By integrating cutting-edge analytical tools and establishing comprehensive data governance frameworks, organizations can effectively mitigate cyber risks and leverage the full potential of big data for sustainable growth and innovation.

Automated Security Testing in DevOps Environments Using AI and ML

The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automated security testing within DevOps environments, offering a comprehensive approach to identifying, predicting, and mitigating security vulnerabilities in real time. Automated security testing leverages AI and ML algorithms to analyze code, detect anomalies, and predict potential security threats. These technologies enable the continuous monitoring of codebases, allowing for the early identification of vulnerabilities before they are exploited. By incorporating AI-driven security testing into the CI/CD pipeline, organizations can ensure that security is not an afterthought but a continuous process integrated into every stage of the software development lifecycle. AI and ML models can be trained to recognize patterns associated with security risks, such as code injection, unauthorized access, and data leakage. These models continuously learn from new data, improving their accuracy over time and adapting to evolving threats. The dynamic nature of AI-driven security testing makes it particularly suited for DevOps environments, where frequent code changes and updates can introduce new vulnerabilities. Moreover, AI and ML can assist in automating complex tasks, such as threat modeling, risk assessment, and the prioritization of security issues, enabling security teams to focus on higher-order tasks that require human expertise.

Fitting Machine Learning Models for the Identification of Social Vulnerability in the Event of Political Instability in Nigeria

Fitting Machine Learning Models for the Identification of Social Vulnerability in the Event of Political Instability in Nigeria

Enhancing Cybersecurity Posture through Dynamic Vulnerability Matching and Threat Intelligence Integration Precious

As the digital landscape continues to evolve, organizations face increasingly sophisticated cyber threats that challenge traditional cybersecurity measures. In response, this paper proposes a novel approach to bolstering cybersecurity posture by integrating dynamic vulnerability matching and threat intelligence. The proposed framework combines proactive identification of vulnerabilities within an organization's network with real-time threat intelligence feeds. Leveraging advanced analytics and machine learning algorithms, the system dynamically matches vulnerabilities to relevant threat intelligence, allowing for prioritized remediation efforts. This dynamic matching ensures that resources are allocated efficiently, focusing on mitigating the most imminent threats to the organization's security. Furthermore, the integration of threat intelligence enriches the vulnerability management process by providing contextual information about emerging threats, attack vectors, and adversary tactics. This contextual awareness enables organizations to anticipate and proactively defend against potential cyber attacks, thereby reducing the window of vulnerability and minimizing the impact of security breaches. Through empirical evaluation and case studies, we demonstrate the efficacy of the proposed framework in enhancing cybersecurity posture across diverse organizational environments. By empowering organizations to adaptively respond to evolving cyber threats, this approach enables them to stay ahead of adversaries and effectively safeguard their critical assets and data.

Security Analysis of Low-Budget IoT Smart Home Appliances Embedded Software and Connectivity

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances’ embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers’ cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information.

Mitoferrin2 is a synthetic lethal target for chromosome 8p deleted cancers

BackgroundSomatic copy number alterations are a hallmark of cancer that offer unique opportunities for therapeutic exploitation. Here, we focused on the identification of specific vulnerabilities for tumors harboring chromosome 8p deletions.MethodsWe developed and applied an integrative analysis of The Cancer Genome Atlas (TCGA), the Cancer Dependency Map (DepMap), and the Cancer Cell Line Encyclopedia to identify chromosome 8p-specific vulnerabilities. We employ orthogonal gene targeting strategies, both in vitro and in vivo, including short hairpin RNA-mediated gene knockdown and CRISPR/Cas9-mediated gene knockout to validate vulnerabilities.ResultsWe identified SLC25A28 (also known as MFRN2), as a specific vulnerability for tumors harboring chromosome 8p deletions. We demonstrate that vulnerability towards MFRN2 loss is dictated by the expression of its paralog, SLC25A37 (also known as MFRN1), which resides on chromosome 8p. In line with their function as mitochondrial iron transporters, MFRN1/2 paralog protein deficiency profoundly impaired mitochondrial respiration, induced global depletion of iron-sulfur cluster proteins, and resulted in DNA-damage and cell death. MFRN2 depletion in MFRN1-deficient tumors led to impaired growth and even tumor eradication in preclinical mouse xenograft experiments, highlighting its therapeutic potential.ConclusionsOur data reveal MFRN2 as a therapeutic target of chromosome 8p deleted cancers and nominate MFNR1 as the complimentary biomarker for MFRN2-directed therapies.

WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer

As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.