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

Individual motile CD4+ T cells can participate in efficient multikilling through conjugation to multiple tumor cells

T cells genetically modified to express a CD19-specific chimeric antigen receptor (CAR) for the investigational treatment of B-cell malignancies comprise a heterogeneous population, and their ability to persist and participate in serial killing of tumor cells is a predictor of therapeutic success. We implemented Timelapse Imaging Microscopy in Nanowell Grids (TIMING) to provide direct evidence that CD4+CAR+ T cells (CAR4 cells) can engage in multikilling via simultaneous conjugation to multiple tumor cells. Comparisons of the CAR4 cells and CD8+CAR+ T cells (CAR8 cells) demonstrate that, although CAR4 cells can participate in killing and multikilling, they do so at slower rates, likely due to the lower granzyme B content. Significantly, in both sets of T cells, a minor subpopulation of individual T cells identified by their high motility demonstrated efficient killing of single tumor cells. A comparison of the multikiller and single-killer CAR+ T cells revealed that the propensity and kinetics of T-cell apoptosis were modulated by the number of functional conjugations. T cells underwent rapid apoptosis, and at higher frequencies, when conjugated to single tumor cells in isolation, and this effect was more pronounced on CAR8 cells. Our results suggest that the ability of CAR+ T cells to participate in multikilling should be evaluated in the context of their ability to resist activation-induced cell death. We anticipate that TIMING may be used to rapidly determine the potency of T-cell populations and may facilitate the design and manufacture of next-generation CAR+ T cells with improved efficacy. Cancer Immunol Res; 3(5); 473–82. ©2015 AACR

Kyste synovial intraosseux du scaphoïde carpien bilatéral révélé par une fracture pathologique: à propos d'un cas et revu de la littérature Case report Open Access

Abstract Nous rapportons l'observation d'un jeune patient qui présente un kyste synovial intraosseux (KSIO) du scaphoïde révélé par une fracture pathologique. Le kyste synovial intraosseux du scaphoïde constitue une étiologie très rare des douleurs du poignet encore plus des fractures et la forme bilatérale associe à une fracture demeure une entité exceptionnelle, non décrite dans la littérature

SecureFalcon: The Next Cyber Reasoning System for Cyber Security

Software vulnerabilities leading to various detriments such as crashes, data loss, and security breaches, significantly hinder the quality, affecting the market adoption of software applications and systems. Although traditional methods such as automated software testing, fault localization, and repair have been intensively studied, static analysis tools are most commonly used and have an inherent false positives rate, posing a solid challenge to developer productivity. Large Language Models (LLMs) offer a promising solution to these persistent issues. Among these, FalconLLM has shown substantial potential in identifying intricate patterns and complex vulnerabilities, hence crucial in software vulnerability detection. In this paper, for the first time, FalconLLM is being fine-tuned for cybersecurity applications, thus introducing SecureFalcon, an innovative model architecture built upon FalconLLM. SecureFalcon is trained to differentiate between vulnerable and non-vulnerable C code samples. We build a new training dataset, FormAI, constructed thanks to Generative Artificial Intelligence (AI) and formal verification to evaluate its performance. SecureFalcon achieved an impressive 94% accuracy rate in detecting software vulnerabilities, emphasizing its significant potential to redefine software vulnerability detection methods in cybersecurity

Revolutionizing Cyber Threat Detection With Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT/IIoT Devices

The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures. As the frequency and diversity of cybersecurity attacks continue to rise, the importance of incident detection has significantly increased. IoT devices are expanding rapidly, resulting in a growing need for efficient techniques to autonomously identify network-based attacks in IoT networks with both high precision and minimal computational requirements. This paper presents SecurityBERT, a novel architecture that leverages the Bidirectional Encoder Representations from Transformers (BERT) model for cyber threat detection in IoT networks. During the training of SecurityBERT, we incorporated a novel privacy-preserving encoding technique called Privacy-Preserving Fixed-Length Encoding (PPFLE). We effectively represented network traffic data in a structured format by combining PPFLE with the Byte-level Byte-Pair Encoder (BBPE) Tokenizer. Our research demonstrates that SecurityBERT outperforms traditional Machine Learning (ML) and Deep Learning (DL) methods, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), in cyber threat detection. Employing the Edge-IIoTset cybersecurity dataset, our experimental analysis shows that SecurityBERT achieved an impressive 98.2% overall accuracy in identifying fourteen distinct attack types, surpassing previous records set by hybrid solutions such as GAN-Transformer-based architectures and CNN-LSTM models. With an inference time of less than 0.15 seconds on an average CPU and a compact model size of just 16.7MB, SecurityBERT is ideally suited for real-life traffic analysis and a suitable choice for deployment on resource-constrained IoT devices

Antibody Fc engineering improves frequency and promotes kinetic boosting of serial killing mediated by NK cells Key Points

Key Points • Fc-engineered mAb promotes NK cell ADCC via better activation, serial killing, and kinetic boosting at higher target cell densities. • Enhanced target killing also increased frequency of NK cell apoptosis, but this effect is donor-dependent. The efficacy of most therapeutic monoclonal antibodies (mAbs) targeting tumor antigens results primarily from their ability to elicit potent cytotoxicity through effectormediated functions. We have engineered the fragment crystallizable (Fc) region of the immunoglobulin G (IgG) mAb, HuM195, targeting the leukemic antigen CD33, by introducing the triple mutation Ser293Asp/Ala330Leu/Ile332Glu (DLE), and developed Time-lapse Imaging Microscopy in Nanowell Grids to analyze antibody-dependent cellmediated cytotoxicity kinetics of thousands of individual natural killer (NK) cells and mAb-coated target cells. We demonstrate that the DLE-HuM195 antibody increases both the quality and the quantity of NK cell-mediated antibody-dependent cytotoxicity by endowing more NK cells to participate in cytotoxicity via accrued CD16-mediated signaling and by increasing serial killing of target cells. NK cells encountering targets coated with DLE-HuM195 induce rapid target cell apoptosis by promoting simultaneous conjugates to multiple target cells and induce apoptosis in twice the number of target cells within the same period as the wild-type mAb. Enhanced target killing was also associated with increased frequency of NK cells undergoing apoptosis, but this effect was donor-dependent. Antibody-based therapies targeting tumor antigens will benefit from a better understanding of cell-mediated tumor elimination, and our work opens further opportunities for the therapeutic targeting of CD33 in the treatment of acute myeloid leukemia. (Blood. 2014;124(22):3241-3249