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Internet of Things (IoT) has become one of the fastest-growing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks’ environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this article, we propose a novel ensemble deep learning based web attack detection system (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a real-word dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates. [ABSTRACT FROM AUTHOR]

In the paper entitled "Optimization Strategy of Multiarea Interconnected Integrated Energy System Based on Consistency Theory" by Yu et al., they study a collaborative optimization strategy of an integrated energy system aiming at improving the energy efficiency for the cluster optimization of an integrated energy system (IES). In the paper entitled "Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network" by Shen et al., they propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve the prediction efficiency. Furthermore, to overcome the problems of traditional IP multicast and software-defined multicasting approaches, they propose an implementable multicast mechanism that delivers data along multicast trees but uses unicast sessions. [Extracted from the article]

The prosperity of social IoT data brings revolutionary changes to our daily lives and greatly increases the existing data volume. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising accuracy. The authors empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. [Extracted from the article]

Potential topics include but are not limited to the following: (1) Security machine learning modelling and architecture (2) Secure multiparty computation techniques for machine learning (3) Attacks against machine learning (4) Machine learning threat intelligence (5) Machine learning for cybersecurity (6) Machine learning for intrusion detection and response (7) Machine learning for multimedia data security (8) Machine learning for public safety After a thorough review process, this special issue has selected a set of eight papers to provide new insights on the abovementioned research areas. Machine learning (ML) has recently gained a renewed interest as the technology powering it has become more widely available and accessible to organizations of all sizes. [Extracted from the article]

Malicious traffic detection is one of the most important parts of cyber security. The approaches of using the flow as the detection object are recognized as effective. Benefiting from the development of deep learning techniques, raw traffic can be directly used as a feature to detect malicious traffic. Most existing work usually converts raw traffic into images or long sequences to express a flow and then uses deep learning technology to extract features and classify them, but the generated features contain much redundant or even useless information, especially for encrypted traffic. The packet header field contains most of the packet characteristics except the payload content, and it is also an important element of the flow. In this paper, we only use the fields of the packet header in the raw traffic to construct the characteristic representation of the traffic and propose a novel flow-vector generation approach for malicious traffic detection. The preprocessed header fields are embedded as field vectors, and then a two-layer attention network is used to progressively generate the packet vectors and the flow vector containing context information. The flow vector is regarded as the abstraction of the raw traffic and is used to classify. The experiment results illustrate that the accuracy rate can reach up to 99.48% in the binary classification task and the average of AUC-ROC can reach 0.9988 in the multi-classification task. • We proposed an approach to gradually construct flow vectors from the field vector. • Extract information irrelevant to the payload from the raw traffic as input. • Unique field value representation makes the embedded vector more effective. • The adjustable number of packets in-flow makes the model more flexible. [ABSTRACT FROM AUTHOR]

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defending against malware. Numerous approaches employ supervised learning techniques, specifically Convolutional Neural Networks (CNNs), to train feature extractors. However, acquiring a substantial quantity of labeled samples incurs significant expenses, and relying solely on CNNs as feature extractors may result in restricted local receptive fields, consequently compromising the preservation of crucial features. In order to address these constraints, we propose an effective malware classification approach, denoted as MalSort, which leverages the masked self-supervised framework with Swin Transformer. Initially, each instance of malware is transformed into a color image. Furthermore, the Swin Transformer self-supervised framework is utilized to extract multi-scale key feature vectors from a randomly masked partial color image, while the prediction module is employed to predict the masked image. Ultimately, the pre-trained encoder is fine-tuned using the malware dataset to effectively carry out a malware classification task. Our MalSort exhibits a reduced reliance on labeled data samples during the training phase, thereby obviating the necessity for extensive amounts of labeled data. Consequently, the MalSort conserves hardware resources and improve its training efficiency. The experimental results indicate that the MalSort outperforms existing models by achieving a classification accuracy of 97.85%, a recall of 97.63%, a precision of 97.85%, and an F1-score of 97.85% on the BIG2015 dataset. Similarly, on the Malimg dataset, the model achieves percentages of 98.28%, 98.18%, 98.19%, and 98.28% for classification accuracy, recall, precision, and F1-score, respectively. • An asymmetric structure extracts features to handle labeled samples and feature loss. • The fine-tune incorporates class re-balancing to handle imbalance and overfitting. • A designed lightweight module improves convergence and decreases resource dependence. [ABSTRACT FROM AUTHOR]

• CNN_Text_Word2vec is a microblog (Chinese Twitter) emotional classification model. • The word vectors are used to learn microblog text features. • Different semantic units impact the accuracy of CNN_Text_Word2vec. Traditional text emotion analysis methods are primarily devoted to studying extended texts, such as news reports and full-length documents. Microblogs are considered short texts that are often characterized by large noises, new words, and abbreviations. Previous emotion classification methods usually fail to extract significant features and achieve poor classification effect when applied to processing of short texts or micro-texts. This study proposes a microblog emotion classification model, namely, CNN_Text_Word2vec, on the basis of convolutional neural network (CNN) to solve the above-mentioned problems. CNN_Text_Word2vec introduces a word2vec neural network model to train distributed word embeddings on every single word. The trained word vectors are used as input features for the model to learn microblog text features through parallel convolution layers with multiple convolution kernels of different sizes. Experiment results show that the overall accuracy rate of CNN_Text_Word2vec is 7.0% higher than that achieved by current mainstream methods, such as SVM, LSTM and RNN. Moreover, this study explores the impact of different semantic units on the accuracy of CNN_Text_Word2vec, specifically in processing of Chinese texts. The experimental results show that comparing to using feature vectors obtained from training words, feature vector obtained from training Chinese characters yields a better performance. [ABSTRACT FROM AUTHOR]