Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system secur...
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Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.
The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote monitoring of human actions and their activities has increased a lot. In...
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Animation is a widespread artistic expression that holds a special place in people's hearts. Traditionally, animation creation has relied heavily on manual techniques, demanding skilled drawing abilities and a sig...
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Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challe...
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Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challenges in early breast cancer detection due to poor image quality, traditional segmentation, and feature extraction. Therefore, this work addresses these issues and proposes an attention-based backpropagation convolutional neural network (ABB-CNN) to detect breast cancer from mammogram images more accurately. The proposed work includes image enhancement, reinforcement learning-based semantic segmentation (RLSS), and multiview feature extraction and classification. The image enhancement is performed by removing noise and artefacts through a hybrid filter (HF), image scaling through a pixel-based bilinear interpolation (PBI), and contrast enhancement through an election-based optimization (EO) algorithm. In addition, the RLSS introduces intelligent segmentation by utilizing a deep Q network (DQN) to segment the region of interest (ROI) strategically. Moreover, the proposed ABB-CNN facilitates multiview feature extraction from the segmented region to classify the mammograms into normal, malignant, and benign classes. The proposed framework is evaluated on the collected and the digital database for screening mammography (DDSM) datasets. The proposed framework provides better outcomes in terms of accuracy, sensitivity, specificity, precision, f-measure, false-negative rate (FNR) and area under the curve (AUC). This work achieved (99.20%, 99.35%), (99.56%, 99.66%), (98.96%, 98.99%), (99.05%, 99.12%), (0.44%, 0.34%), (99.31%, 99.39%) and (99.27%, 99.32%) of accuracy, sensitivity, specificity, precision, FNR, f-measure and AUC on (collected, DDSM datasets), respectively. This research addresses the prevalent challenges in breast cancer identification and offers a robust and highly accurate solution by integrating advanced deep-learning techniques. The evaluated re
Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least t...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.
Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to peo...
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Plaintext-checkable encryption (PCE) can support searches over ciphertext by directly using plaintext. The functionality of a search is modeled by a specific check algorithm that takes a pair of target plaintext and c...
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Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these val...
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Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, ...
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Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specime...
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