While the Industrial Internet of Things (IIoT) brings convenience to industry, it also brings security problems. Due to the massive amount of data generated by the surge of IIoT devices, it is impossible to ensure whether these data contain attack or untrustworthy data, so how to ensure the security and trustworthiness of IIoT devices becomes an urgent problem to be solved. Here, we design a new hinge classification algorithm based on mini-batch gradient descent with an adaptive learning rate and momentum (HCA-MBGDALRM) to minimize the effects of security attacks. The algorithm significantly improves the performance of deep network training compared with traditional neural networks, decision trees and logistic regression in terms of scale and speed. In addition, we have solved the data skew problem in the shuffle phase, and we implement a parallel framework for HCA-MBGDALRM to accelerate the processing speed of very large traffic datasets.

The emergence of artificial intelligence technology has promoted the development of the Internet of Things (IoT). However, this promising cyber technology can encounter serious security problems while accessing the internet. A malicious website can disguise itself as a normal website, and obtain users' private information. Thus, it is very important to detect malicious websites using tools such as machine learning algorithms, as these algorithms can help us to identify abnormal information hidden in the mass traffic more easily. Accordingly, many feature engineering tasks must be performed from memory, as a strong machine learning model is greatly improved with good features. In this paper, we propose an unsupervised learning algorithm that learns URL embedding. We also explore some key parameters regarding a domain embedding model to obtain a good effect on domain features.

Deep learning has recently gained more and more popularity, because of its high accuracy and wide range of coverage. In particular, deep learning is widely used in the medical field. Because in the field of image classification and biological applications, the accuracy of deep learning is very high. Unfortunately, even under the collaborative deep learning, there is still serious risk of information leakage. Moreover, the risk of information leakage in the medical field is greater and the harm is even greater. For example, medical treatment data may be leaked to third-party organizations. When these important medical data is illegally used by for-profit organizations or obtained by criminals, it will not only lead to the disclosure of personal privacy information, but also cause serious economic losses to the victims. However, the victim cannot delete the leaked information by itself or limit the scope and use of the information that has been leaked. Therefore, the adverse effects are unimaginable. This paper mainly studies the information protection methods under GAN model attack, in order to find a better way to prevent attacks and effectively protect information.

JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%.

Android malware poses a significant challenge for mobile platforms. To evade detection, contemporary malware variants use API substitution or obfuscation techniques to hide malicious activities and mask their shallow semantic characteristics. However, existing research lacks analysis of the hierarchical semantic associated with Android apps. To address this problem, we propose ArchSentry, an enhanced Android malware detection via hierarchical semantic extraction. First, we select entities and their relationships relevant to Android software behavior through the software architecture and represent them using a heterogeneous graph. Then, we structure meta-paths to represent rich semantic information to achieve semantic enhancement and improve efficiency. Next, we design a meta-path semantic selection method based on KL Divergence to identify and eliminate redundant features. To achieve a comprehensive representation of the overall software semantics and improve performance, we construct a feature fusion approach based on Restricted Boltzmann Machines (RBM) and AutoEncoder (AE) during the pre-training phase, while preserving the probability distribution characteristics of various meta-paths. Finally, Deep Neural Networks (DNN) process fusion features for comprehensive feature sets. Experimental results on real-world application samples indicate that ArchSentry achieves a remarkable 99.2% detection rate for Android malware, with a low false positive rate below 1%. These results surpass the performance of current state-of-the-art approaches.

Smart factory is a typical application scene of Internet of Things (IoT) and wireless terminal devices natu-rally compose a three-dimensional industrial wireless net-work. A primary requirement in the network is delivering packets from source node to destination node. Most geo-graphic routing algorithms are designed for planar networks and they do not suit 3D networks. In this paper, we extend Greedy Perimeter Stateless Routing algorithm (GPSR) into three dimensions named GPSR-3D. In GPSR-3D, each node decides next hop of a packet by cooperating with only local neighbors and hence this algorithm is totally distributed. GPSR-3D comprises two packet forwarding patterns named Greedy Forwarding Pattern (GFP) and Surface Forwarding Pattern (SFP). In GFP, a node always sends the packet to a neighbor closest to destination and when it fails, SFP is em-ployed for recovery. In SFP, we first divide the whole network space into a set of subspaces based on a novel 3D geometric structure. Then, a parallel polyhedron traverse algorithm is proposed to recover local minima. A flowchart of GPSR-3D is given to clearly present the process of delivering a packet based on GFP and SFP. Simulation results show that GPSR-3D is of great reliability, energy-efficiency and storage-effi-ciency. Specifically, data transmission amount in GPSR-3D is about 67% and 71% to that of MDT and GDSTR-3D in av-erage. Moreover, GPSR-3D performs much better than MDT and GDSTR-3D in terms of average storage cost and the av-erage storage space in GPSR-3D is about 48% and 26% to that of MDT and GDSTR-3D, respectively.