Confidential information such as words, pictures, audio and visual data can be concealed in the cover image. The primary objective is to hide words or images, within the images using the technique known as Significant...
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We introduce the framework of performative control, where the policy chosen by the controller affects the underlying dynamics of the control system. This results in a sequence of policy-dependent system state data wit...
Biometric features are advantageous for human authentication systems over traditional methods. Fingerprints are a widespread biometric feature for user verification. Fingerprint-based systems usually rely on minutiae ...
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A newly designed small, almost square shaped Ultra Wideband antenna is presented in this paper. Free space simulations provided promising results with a wider bandwidth and high efficiency up to 97.68% within the UWB ...
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BlindSpace is an innovative application aimed at improving the lives of visually challenged individuals. Leveraging cutting-edge image captioning and object detection technologies, the app allows users to capture imag...
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The paper explores the challenges posed by ransomware attacks in the healthcare sector within the context of the digital transformation of healthcare. It examines real-world incidents, such as those at AIIMS hospital,...
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The identification of plant diseases has become a key topic in smart agriculture research in recent years. The productivity of agricultural products will undoubtedly increase with early plant disease identification. F...
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The rapidly evolving landscape of malware necessitates robust cross-platform detection solutions. This study addresses a critical gap by investigating the efficacy of Machine Learning (ML) and heuristic methods combin...
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ISBN:
(数字)9798350359688
ISBN:
(纸本)9798350359688
The rapidly evolving landscape of malware necessitates robust cross-platform detection solutions. This study addresses a critical gap by investigating the efficacy of Machine Learning (ML) and heuristic methods combined for superior malware detection across platforms. Traditional signature-based methods struggle with this ever-evolving threat landscape, particularly due to modern malware's obfuscation techniques. Our research proposes a novel approach leveraging Deep Learning models (Convolutional Neural Network (CNN) and Deep Neural Network (DNN)) to overcome these limitations. We compare these models with established ML algorithms on a comprehensive malware dataset (CICAndMal2017) for both binary and multi-class classification. The findings demonstrate significant advancements: Deep Learning models consistently outperform traditional ML approaches, achieving superior accuracy, precision, and recall. Additionally, combining Deep Learning with heuristics yields even better detection performance, highlighting the value of incorporating domain knowledge through heuristics for improved feature selection and classification. Furthermore, the study emphasizes the importance of dynamic features, capturing malware's runtime behavior, to bypass obfuscation techniques. Traditional static analysis often struggles with such obfuscation, highlighting the need for a combined approach that analyzes both static and dynamic features for a more comprehensive understanding of malware behavior. This research significantly contributes to cross-platform malware detection by validating the superiority of Deep Learning models, particularly when combined with heuristics. It emphasizes the crucial role of dynamic analysis and promotes a combined detection approach leveraging both Deep Learning and heuristics for superior malware classification and mitigation. Future work directions include expanding data collection with diverse features and investigating unsupervised or reinforcement learnin
Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementatio...
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IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision...
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