Security and Communication Networks

This article highlights the importance of learning models which prevent the resilient attack of robotic technology with a subset of trajectories. Many complement models are introduced in the field of path planning robots without any knowledge of representation procedures, so robotic data are subject to different attacks from several users. During such attacks, the data will be misplaced and commands specified to robots will be disorganized so a new training data set has to be incorporated which is a difficult task. Therefore, to prevent probability of data failure time-dependent binary probability prototypes are introduced with low training data. Furthermore, a regularized boosting procedure (RBP) has been applied with different weights to switch multiple robots with discrete knowledge representation. Then a high space block is incorporated for maximizing coverage areas during loss functions and this is implicit as an innovative technique as compared with existing procedures. To validate the effectiveness of proposed learning techniques in robots, four scenarios are considered which include accuracy and success rate of detection. Subsequently, the outcomes prove that the robotic path with learning models are highly effective for an average percentile of 86% as compared to conventional techniques.

Deep neural network (DNN) has replaced humans to make decisions in many security-critical senses such as face recognition and automatic drive. Essentially, researchers try to teach DNN to simulate human behavior. However, many evidences show that there is a huge gap between humans and DNN, which has raised lots of security concern. Adversarial sample is a common way to show the gap between DNN and humans in recognizing objects with similar appearance. However, we argue that the difference is not limited to adversarial samples. Hence, this paper explores such differences in a new way by generating fooling samples in 3D point cloud domain. Specifically, the fooling point cloud is hardly recognized by human vision but is classified to the target class by the victim 3D point cloud DNN (3D DNN) with more than confidence. Furthermore, to search for the optimal fooling point cloud, a new evolutionary algorithm named Multielites Harris Hawk Optimization (MEHHO) with enhanced exploitation ability is designed. On one hand, our experiments demonstrate that: (1) 3D DNN tends to learn high-level features of one object; (2) 3D DNN that makes decisions relying on more points is more robust; and (3) the gap is hardly learned by 3D DNN. On the other hand, the comparison experiments show that the designed MEHHO outperforms the SOTA evolutionary algorithms w.r.t. statistics and convergence results.

Reversible data hiding in encrypted image (RDHEI) has become a research hotspot, which can effectively protect image content privacy. An RDHEI scheme based on the joint encoding of multiple MSBs (most significant bits) and pixel difference is proposed in this paper. A block-based image encryption method is adopted on the content owner side, which can securely protect the image contents while retaining the spatial correlation within each block. By the joint encoding strategy of multiple MSB and pixel difference, the redundancy within the bit plane is sufficiently compressed to accommodate more additional data; thus, a high embedding rate can be achieved. According to different kinds of available keys, image decryption and data extraction can be separably conducted on the receiver side. Experimental results show that our scheme can achieve a higher embedding rate than some state-of-the-art schemes.

This article investigates the projective synchronization problem for a class of master-slave complex dynamical networks with multiple time-varying delays. A class of the delayed impulsive controller is designed; sufficient criterions for the projective synchronization of complex dynamical networks are derived. The nonlinear term and the coupled term have nonidentical time-varying delays, which increases our research difficulties. Two numerical simulations are presented to verify the effectiveness of our result.

With the development of IT technologies, an increasing number of industrial control systems (ICSs) can be accessed from the public Internet (with authentication). In such an open environment, cyberattacks become a serious threat to both ICS system integrity and data privacy. As a countermeasure, anomaly detection systems are often deployed to analyze the network traffic. However, due to privacy regulation, the network packages cannot be directly processed in plaintext in many countries. In this work, we present a privacy-preserving anomaly detection platform for ICS. The platform consists of three nodes running low-latency MPC protocols to evaluate the live network packages using decision trees on the fly with privacy assurance. Our benchmark result shows that the platform can process thousands of packages every ten seconds.

Since the continuous authentication (CA) system based on smartphone sensors has been facing the challenge of the low-data regime under some practical scenarios, which leads to low accuracy of CA, it needs to be solved urgently. To this end, currently, the generative adversarial networks (GAN) provide a powerful method to train the result generative model that could generate very convincing verisimilar data. The framework of the GAN and its variants shed much light on improving the performance of CA. Therefore, in this article, we propose a continuous authentication system on smartphones based on a Wasserstein generative adversarial network (WGAN) for sensor data augmentation, which utilizes accelerometers, gyroscopes, and magnetometers of smartphone sensors to sense phone movements caused by user operation behavior. Specifically, based on sensor data under different user activities, the WGAN is used to create additional data in training data for data augmentation. With the augmented data, we design a convolutional neural network to learn and extract deep features from sensor data, and then use four classifiers of RF, OCSVM, DT, and KNN to train these features. Finally, we train and test on the HMOG dataset, and the results show that the EER of the authentication system is between 3.68% and 6.39% on the sensor data with a time window of 2 s.