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Karolína Bílá
We attempt here to review recent studies focusing on droughts and hydrology in the ?umava Mts. The main question is can bark beetles affect water regimes in forest and what kind of measures might be taken ? if any ? to prevent bark beetle attacks. We com...
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Marcin Aftowicz, Ievgen Kabin, Zoya Dyka and Peter Langendörfer
While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption, o...
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Chinyang Henry Tseng, Woei-Jiunn Tsaur and Yueh-Mao Shen
In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrus...
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Saikat Das, Mohammad Ashrafuzzaman, Frederick T. Sheldon and Sajjan Shiva
The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastru...
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Zvi Mendel, Hillary Voet, Ilan Nazarian, Svetlana Dobrinin and Dana Ment
The red palm weevil (Rhynchophorus ferrugineus) inflicts widespread damage in date palm plantations and urban settings, leading to stand loss and safety concerns, intensified by the economic and ecological burdens of synthetic preventive treatments. A no...
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Woonghee Lee and Younghoon Kim
This study introduces a deep-learning-based framework for detecting adversarial attacks in CT image segmentation within medical imaging. The proposed methodology includes analyzing features from various layers, particularly focusing on the first layer, a...
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Hao An, Ruotong Ma, Yuhan Yan, Tailai Chen, Yuchen Zhao, Pan Li, Jifeng Li, Xinyue Wang, Dongchen Fan and Chunli Lv
This paper aims to address the increasingly severe security threats in financial systems by proposing a novel financial attack detection model, Finsformer. This model integrates the advanced Transformer architecture with the innovative cluster-attention ...
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Yussuf Ahmed, Muhammad Ajmal Azad and Taufiq Asyhari
In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating p...
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Jin Wang, Peng Zhao, Zhe Zhang, Ting Yue, Hailiang Liu and Lixin Wang
The upset state is an unexpected flight state, which is characterized by an unintentional deviation from normal operating parameters. It is difficult for the pilot to recover the aircraft from the upset state accurately and quickly. In this paper, an ups...
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Meng Bi, Xianyun Yu, Zhida Jin and Jian Xu
In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP metho...
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