Towards 5G Security Analysis against Null Security Algorithms Used in Normal CommunicationRead the full article
Security and Communication Networks provides a prestigious forum for the R&D community in academia and industry working at the interdisciplinary nexus of next generation communications technologies for security implementations in all network layers.
Chief Editor Dr Di Pietro is a full professor of cybersecurity at the HBKU College of Science and Engineering, Qatar. His research interests include distributed systems security, cloud security, and wireless security.
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Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network
With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.
Hybrid Encryption Scheme for Hospital Financial Data Based on Noekeon Algorithm
The previous encryption methods of hospital financial data have the problem of overburden. Therefore, a research study on hybrid encryption of hospital financial data based on Noekeon algorithm is proposed. From the basic principles of the Noekeon algorithm and the application and implementation of the Noekeon algorithm, a hybrid encryption scheme for hospital financial data based on the Noekeon algorithm is designed. In order to improve the security of the encryption system, the RSA algorithm is used to encrypt the encrypted content twice. The hybrid algorithm realizes the hybrid encryption of the hospital's financial data. Finally, a hybrid encryption system for hospital financial data based on Noekeon algorithm is designed. Experimental results show that this method has a higher success rate and better comprehensive performance. It not only improves the encryption efficiency of hospital financial data but also enhances the security of hospital financial data, which has greater application value.
PF : Website Fingerprinting Attack Using Probabilistic Topic Model
Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.
An Autonomous Cyber-Physical Anomaly Detection System Based on Unsupervised Disentangled Representation Learning
Cyber-Physical Systems (CPS) in heavy industry are a combination of closely integrated physical processes, networking, and scientific computing. The physical production process is monitored and controlled by the CPS in question, through advanced real-time networking systems, where high-precision feedback loops can be changed when the overgrid of cooperative computing and communication components that make up the industrial process is required. These CPS operate independently but integrate interaction capabilities as well as with the external environment, creating the connection of the physical with the digital world. The outline is that the most effective modeling and development of high-reliability CPS are directly related to the maximization of the production process, extroversion, and industrial competition. In this paper, considering the high importance of the operational status of CPS for heavy industry, an innovative autonomous anomaly detection system based on unsupervised disentangled representation learning is presented. It is a temporal disentangled variational autoencoder (TDVA) which, mimicking the process of rapid human intuition, using high- or low-dimensional reasoning, finds and models the useful information independently, regardless of the given problem. Specifically, taking samples from the real data distribution representation space, separating them appropriately, and encoding them as separate disentangling dimensions create new examples that the system has not yet dealt with. In this way, first, it utilizes information from potentially inconsistent sources to learn the right representations that can then be broken down into subspace subcategories for easier and simpler categorization, and second, utilizing the latent representation of the model, it performs high-precision estimates of how similar or dissimilar the inputs are to each other, thus recognizing, with great precision and in a fully automated way, the system anomalies.
pKAS: A Secure Password-Based Key Agreement Scheme for the Edge Cloud
For the simplicity and feasibility, password-based authentication and key agreement scheme has gradually become a popular way to protect network security. In order to achieve mutual authentication between users and edge cloud servers during data collection, password-based key agreement scheme has attracted much attention from researchers and users. However, security and simplicity are a contradiction, which is one of the biggest difficulties in designing a password-based key agreement scheme. Aimed to provide secure and efficient key agreement schemes for data collecting in edge cloud, we propose an efficient and secure key agreement in this paper. Our proposed scheme is proved by rigorous security proof, and the proposed scheme can be protected from various attacks. By comparing with other similar password-based key agreement schemes, our proposed scheme has lower computational and communication costs and has higher security.
LightSEEN: Real-Time Unknown Traffic Discovery via Lightweight Siamese Networks
With the increase in the proportion of encrypted network traffic, encrypted traffic identification (ETI) is becoming a critical research topic for network management and security. At present, ETI under closed world assumption has been adequately studied. However, when the models are applied to the realistic environment, they will face unknown traffic identification challenges and model efficiency requirements. Considering these problems, in this paper, we propose a lightweight unknown traffic discovery model LightSEEN for open-world traffic classification and model update under practical conditions. The overall structure of LightSEEN is based on the Siamese network, which takes three simplified packet feature vectors as input on one side, uses the multihead attention mechanism to parallelly capture the interactions among packets, and adopts techniques including 1D-CNN and ResNet to promote the extraction of deep-level flow features and the convergence speed of the network. The effectiveness and efficiency of the proposed model are evaluated on two public data sets. The results show that the effectiveness of LightSEEN is overall at the same level as the state-of-the-art method and LightSEEN has even better true detection rate, but the parameter used in LightSEEN is of the baseline and its average training time is of the baseline.