Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift partial computational and storage overhead from end users to chosen fog nodes. Furthermore, the basic LFGS system is improved to support conjunctive keyword search and attribute update to avoid returning irrelevant search results and illegal accesses. The formal security analysis shows that the LFGS system can resist Chosen-Keyword Attack (CKA) and Chosen-Plaintext Attack (CPA), and the simulation using a real-world dataset demonstrates that the LFGS system is efficient and feasible in practice.
Secure sensor localization is a prerequisite for many sensor networks to retrieve trustworthy data. However, most of existing node positioning systems were studied in trust environment and are therefore vulnerable to malicious attacks. In this work, we develop a robust node positioning mechanism(ROPM) to protect localization techniques from position attacks. Instead of introducing countermeasures for every possible internal or external attack, our approach aims at making node positioning system attack-tolerant by removing malicious beacons. We defeat internal attackers and external attackers by applying different strategies, which not only achieves robustness to attacks but also dramatically reduces the computation overhead. Finally, we provide detailed theoretical analysis and simulations to evaluate the proposed technique.
In recent years, the successful applications of deep learning technology bring serious privacy issues. Current countermeasures can achieve privacy protection by introducing differential privacy mechanisms to convolutional deep belief network, which will inevitably bring huge computational complexity in convolutional kernels. In this paper, we focus on designing a lightweight security privacy protection framework LPDBN, a novel Local differential Privacy binary pattern Deep Belief Network. We use the local binary pattern to extract texture information from the images instead of convolutional kernels, which greatly reduces the time complexity and data dimension. Meanwhile, the proposed framework can improve recognition performance under the same privacy protection intensity. The theorem analysis and experiments show the security and efficiency respectively.
Deep learning methods have become the preferred solution for encrypted traffic classification. However, the application of neural networks in encrypted traffic classification has encountered the following limitations: 1) Deep learning models have dependencies on large-scale and well-labeled datasets. 2) most deep learning models have high hardware requirements and require a large amount of CPU and GPU for computation. These limitations seriously hinder the development of encrypted traffic research. In this paper, we propose a new lightweight semi-supervised learning classifier to solve these problems. To reduce the dependence of the model on CPU and GPU, we have designed a lightweight encrypted traffic classifier based on CNN(Convolutional Neural Networks). It can run on raspberry pi with low hardware requirements. Then we combine the classifier with the Mean Teacher framework, which we call MT-CNN. By using the semi-supervised learning framework, we successfully reduced the number of labeled samples during model training. To fully preserve traffic information, we convert traffic data into grayscale images as input. We used a small-scale dataset for experiments on raspberry pi. The experimental results showed that the accuracy of MT-CNN still reached 96.83% even when only 5% of the labeled data was used.
TV guided missile is a kind of missile which is composed of TV camera and guidance system. In view of the problem that the target feature information is not obvious and the target and background are not distinguishable. The paper puts forward a method based on Top-Hat transformation to detect the sea level moving target, which can differentiate the target and the background effectively, and extract the typical characteristic value of the target. Simulation results prove the effectiveness of the proposed method.
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