In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenge...
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Currently, the registration of non-rigid 3D objects remains a challenging task. This paper proposes an unsupervised non-rigid point cloud registration network based on local correspondence relationships, namely the LC...
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This paper presents an in-depth analysis of the architecture and development of an innovative application designed to virtualize transaction execution on the Solana blockchain. This virtualization system facilitates t...
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Precise measurement and control of the depth of anesthesia (DoA) is essential for anesthesiologists to ensure the success of surgery and the safety of patients. Electroencephalogram (EEG) signals, captured by electrod...
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Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in di...
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We demonstrate an improved deep residual network model that can recover the distorted chirp signals of the microwave photonic receiving systems. The method can achieve high-precision recovery of distorted signals by l...
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In this era of the internet, malware poses a severe threat to cyber security and modern communication networks. It is very difficult to get protected from the exponential increase of malware threats and its variants w...
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Malware detection is one of the most challenging tasks in the domain of Cybersecurity. The use of machine learning models as detectors significantly improved the performance. At the same time, adversarial modeling of ...
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ISBN:
(纸本)9783031804076;9783031804083
Malware detection is one of the most challenging tasks in the domain of Cybersecurity. The use of machine learning models as detectors significantly improved the performance. At the same time, adversarial modeling of malware can introduce a small noise into the malware data to generate adversarial malware, which can evade the machine learning based detector. In this work, we have proposed a detection framework for adversarial malware. We have considered SLIPNER and MalGAN datasets to generate adversarial malware using an adversarial-based attack algorithm. Our findings show that our approach is extremely effective in detecting and eliminating adversarial malware ! - Query ID="Q1" Text="Please check and confirm if the authors given and family names have been correctly identified." -.
Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content i...
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New specifications for 6G mobile networks will surpass the performance targets of 5G networks. As more 5G networks are deployed, their limits become obvious, encouraging the exploration of networks using 6G as the fut...
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