The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe *** a security-sensitive operation is unchecked,a missing-check issue *** check is a ...
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The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe *** a security-sensitive operation is unchecked,a missing-check issue *** check is a class of severe bugs in software programs especially in operating system kernels,which may cause a variety of security issues,such as out-of-bound accesses,permission bypasses,and privilege *** to the lack of security specifications,how to automatically identify security-sensitive operations and their required security checks in the Linux kernel becomes a challenge for missing-check *** this paper,we present an accurate missing-check analysis method for Linux kernel,which can automatically infer possible security-sensitive ***,we first automatically identify all possible security check functions of *** according to their callsites,a two-direction analysis method is leveraged to identify possible security-sensitive operations.A missing-check bug is reported when the security-sensitive operation is not protected by its corresponding security *** have implemented our method as a tool,named AMCheX,on top of the LLVM(Low Level Virtual Machine)framework and evaluated it on the Linux *** reported 12 new missing-check bugs which can cause security *** of them have been confirmed by Linux maintainers.
Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors...
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1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor...
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1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memory access pattern which leads to a poor locality in the memory access stream[1].
Radio frequency energy harvesting offers a promising solution to provide low power Internet of Things (IoT) devices with convenient and perpetual energy supply. In this work, we investigate the reliable performance of...
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A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specifi...
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Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development...
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In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has ...
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Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to mod...
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Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.
This brief presents a novel filtering single-pole-double-throw (SPDT) switch with continuously tunable center frequency and insertion phase. It is comprised of six varactor-loaded microstrip resonators and three tunab...
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Ordinal regression aims at solving the classification problem, where the categories are related in a natural order. Due to the difficulty in distinguishing between highly relevant categories, label noise is frequently...
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