Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques

In response to escalating cyber threats and privacy issues within the Industrial Internet of Things (IIoT), this research presents FedGenID, an advanced Federated Generative Intrusion Detection System, to safeguard IIoT networks. Our approach introduces a three-model framework: (1) a federated generative model, incorporating a Conditional Generative Adversarial Network (cGANs) for data augmentation, emphasizing only generator model updates to be shared among clients. This model uses a Wasserstein loss function with Gradient Penalty to amplify sample diversity, indicative of varying cyber threats. Concurrently, we address the issues of imbalanced and distributed data and deploy a data curation technique to align generated data within specific constraints. (2) A secondary model fine-tunes local Critics for enhanced resilience and detection of various adversarial attacks. (3) The third model focuses on precise cyber threat identification, leveraging augmented data for improved training under a synthetic federated learning schema, bolstering detection capability, especially against zero-day threats. Our evaluation of FedGenID, utilizing a novel industrial cybersecurity dataset, highlights its efficacy in non-IID, multi-class cyber threat detection and its resilience to adversarial attacks. Furthermore, we demonstrate how FedGenID can mitigate the negative impact of differential privacy-enhanced FL on model performance. The findings underscore FedGenID's proficiency in detection accuracy, surpassing traditional FedID by 10% in the presence of zero-day attacks and high privacy regimes

Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from http://ieee-dataport.org/8939

The small heat shock protein B8 (HSPB8) confers resistance to bortezomib by promoting autophagic removal of misfolded proteins in multiple myeloma cells

Velcade is one of the inescapable drug to treat patient suffering from multiple myeloma (MM) and resistance to this drug represents a major drawback for patients. However, the mechanisms underlying velcade resistance remain incompletely understood. We derived several U266 MM cell clones that resist to velcade. U266-resistant cells were resistant to velcade-induced cell death but exhibited a similar sensitivity to various proapoptotic stimuli. Careful analysis of proteosomal subunits and proteasome enzymatic activities showed that neither the composition nor the activity of the proteasome was affected in velcade-resistant cells. Elimination of velcade-induced poly-ubiquitinated proteins and protein aggregates was drastically stimulated in the resistant cells and correlated with increased cell survival. Inhibition of the lysosomal activity in velcade-resistant cells resulted in an increase of cell aggregates and decrease survival, indicating that aggregates are eliminated through lysosomal degradation. In addition, pangenomic profiling of velcade-sensitive and resistant cells showed that the small heat shock protein HSPB8 was overexpressed in resistant cells. Finally, gain and loss of function experiment demonstrated that HSPB8 is a key factor for velcade resistance. In conclusion, HSPB8 plays an important role for the elimination of aggregates in velcade-resistant cells that contributes to their enhanced survival

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Multiple myeloma : from pathogenesis to chemoresistance

Le myélome multiple (MM) est un cancer hématologique qui se caractérise par une prolifération et une accumulation de cellules plasmocytaires malignes au niveau de la moelle osseuse (MO). Il représente 10% des hémopathies malignes et 2% de la mortalité par cancer dans le monde occidental. La principale conséquence de l’expansion plasmocytaire clonale médullaire est la sécrétion excessive d’une immunoglobuline (Ig) unique qui va être à l’origine du caractère multi-symptomatique de cette pathologie. Ainsi, les manifestations du MM se caractérisent par des lésions osseuses, une atteinte rénale, une anémie, une hypercalcémie et une immunodéficience humorale conduisant à des infections récidivantes. Son pronostic est mauvais avec une médiane de survie qui se situe entre cinq et sept ans sous chimiothérapie qui vise à éliminer les cellules plasmocytaires malignes.A partir de la lignée U266 de myélome, nous avons dérivé des clones résistants au bortézomib (R6). Grâce à une analyse par biopuces à ADN, nous avons identifié 160 gènes significativement régulés dans les cellules R6 par rapport à la lignée parentale U266. Nous avons établi, par une approche fonctionnelle, que la surexpression de la protéine HspB8 conduit, via l’activation de la dégradation autophagique, à l’élimination des agrégats protéiques et à compenser l’effet de l’inhibition du protéasome conférant ainsi la résistance des cellules de myélome aux inhibiteurs du protéasome.Dans un second temps, nous nous sommes intéressés à l’implication de la protéine Bcl-B (BCL2L10) dans la pathogenèse du MM. Nous avons confirmé que Bcl-B est impliqué dans la pathogénèse du MM (patients et modèle murin).Velcade is one of the inescapable drug to treat patient suffering from multiple myeloma (MM) and resistance to this drug represents a major drawback for patients. However, the mechanisms underlying velcade resistance remain incompletely understood. We derived several U266 MM cell clones that resist to velcade. We derived several U266 MM cell clones that resist to velcade. U266- resistant cells were resistant to velcade-induced cell death but exhibited a similar sensitivity to various proapoptotic stimuli. Careful analysis of proteosomal subunits and proteasome enzymatic activities showed that neither the composition nor the activity of the proteasome was affected in velcade-resistant cells.In addition, pangenomic profiling of velcade-sensitive and resistant cells showed that the small heat shock protein HSPB8 was overexpressed in resistant cells. Finally, gain and loss of function experiment demonstrated that HSPB8 is a key factor for velcade resistance. In conclusion, HSPB8 plays an important role for the elimination of aggregates in velcade-resistant cells that contributes to their enhanced survival. Multiple myeloma (MM) evolves from a premalignant condition known as monoclonal gammopathy of undetermined significance (MGUS). However, the factors underlying the malignant transformation of plasmocytes in MM are not fully characterized. We report an MM phenotype in transgenic mice with Eμ-directed expression of the Bcl-B protein. With age, Eμ-bcl-b transgenic mice develop the characteristic features of human MM. In addition, this MM-like disease is serially transplantable, underlying the tumoral origin of plasmocytes

Le myélome multiple : de la pathogenèse à la chimiorésistance

Velcade is one of the inescapable drug to treat patient suffering from multiple myeloma (MM) and resistance to this drug represents a major drawback for patients. However, the mechanisms underlying velcade resistance remain incompletely understood. We derived several U266 MM cell clones that resist to velcade. We derived several U266 MM cell clones that resist to velcade. U266- resistant cells were resistant to velcade-induced cell death but exhibited a similar sensitivity to various proapoptotic stimuli. Careful analysis of proteosomal subunits and proteasome enzymatic activities showed that neither the composition nor the activity of the proteasome was affected in velcade-resistant cells.In addition, pangenomic profiling of velcade-sensitive and resistant cells showed that the small heat shock protein HSPB8 was overexpressed in resistant cells. Finally, gain and loss of function experiment demonstrated that HSPB8 is a key factor for velcade resistance. In conclusion, HSPB8 plays an important role for the elimination of aggregates in velcade-resistant cells that contributes to their enhanced survival. Multiple myeloma (MM) evolves from a premalignant condition known as monoclonal gammopathy of undetermined significance (MGUS). However, the factors underlying the malignant transformation of plasmocytes in MM are not fully characterized. We report an MM phenotype in transgenic mice with Eμ-directed expression of the Bcl-B protein. With age, Eμ-bcl-b transgenic mice develop the characteristic features of human MM. In addition, this MM-like disease is serially transplantable, underlying the tumoral origin of plasmocytes.Le myélome multiple (MM) est un cancer hématologique qui se caractérise par une prolifération et une accumulation de cellules plasmocytaires malignes au niveau de la moelle osseuse (MO). Il représente 10% des hémopathies malignes et 2% de la mortalité par cancer dans le monde occidental. La principale conséquence de l’expansion plasmocytaire clonale médullaire est la sécrétion excessive d’une immunoglobuline (Ig) unique qui va être à l’origine du caractère multi-symptomatique de cette pathologie. Ainsi, les manifestations du MM se caractérisent par des lésions osseuses, une atteinte rénale, une anémie, une hypercalcémie et une immunodéficience humorale conduisant à des infections récidivantes. Son pronostic est mauvais avec une médiane de survie qui se situe entre cinq et sept ans sous chimiothérapie qui vise à éliminer les cellules plasmocytaires malignes.A partir de la lignée U266 de myélome, nous avons dérivé des clones résistants au bortézomib (R6). Grâce à une analyse par biopuces à ADN, nous avons identifié 160 gènes significativement régulés dans les cellules R6 par rapport à la lignée parentale U266. Nous avons établi, par une approche fonctionnelle, que la surexpression de la protéine HspB8 conduit, via l’activation de la dégradation autophagique, à l’élimination des agrégats protéiques et à compenser l’effet de l’inhibition du protéasome conférant ainsi la résistance des cellules de myélome aux inhibiteurs du protéasome.Dans un second temps, nous nous sommes intéressés à l’implication de la protéine Bcl-B (BCL2L10) dans la pathogenèse du MM. Nous avons confirmé que Bcl-B est impliqué dans la pathogénèse du MM (patients et modèle murin)

Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques

In response to escalating cyber threats and privacy issues within the Industrial Internet of Things (IIoT), this research presents FedGenID, an advanced Federated Generative Intrusion Detection System, to safeguard IIoT networks. Our approach introduces a three-model framework: 1) a federated generative model, incorporating a Conditional Generative Adversarial Network (cGANs) for data augmentation, emphasizing only generator model updates to be shared among clients. This model uses a Wasserstein loss function with Gradient Penalty to amplify sample diversity, indicative of varying cyber threats. Concurrently, we address the issues of imbalanced and distributed data and deploy a data curation technique to align generated data within specific constraints. 2) A secondary model fine-tunes local Critics for enhanced resilience and detection of various adversarial attacks. 3) The third model focuses on precise cyber threat identification, leveraging augmented data for improved training under a synthetic federated learning schema, bolstering detection capability, especially against zero-day threats. Our evaluation of FedGenID, utilizing a novel industrial cybersecurity dataset, highlights its efficacy in non-IID, multi-class cyber threat detection and its resilience to adversarial attacks. Furthermore, we demonstrate how FedGenID can mitigate the negative impact of differential privacy-enhanced FL on model performance. The findings underscore FedGenID’s proficiency in detection accuracy, surpassing traditional FedID by 10% in the presence of zero-day attacks and high privacy regimes