With Android’s dominant position within the current smartphone OS, increasing number of malware applications pose a great threat to user privacy and security. Classification algorithms that use a single feature usually have weak detection performance. Although the use of multiple features can improve the detection effect, increasing the number of features increases the requirements of the operating environment and consumes more time. We propose a fast Android malware detection framework based on the combination of multiple features: FAMD (Fast Android Malware Detector). First, we extracted permissions and Dalvik opcode sequences from samples to construct the original feature set. Second, the Dalvik opcodes are preprocessed with the N-Gram technique, and the FCBF (Fast Correlation-Based Filter) algorithm based on symmetrical uncertainty is employed to reduce feature dimensionality. Finally, the dimensionality-reduced features are input into the CatBoost classifier for malware detection and family classification. The dataset DS-1, which we collected, and the baseline dataset Drebin were used in the experiment. The results show that the combined features can effectively improve the detection accuracy of malware that can reach 97.40% on Drebin dataset, and the malware family classification accuracy can achieve 97.38%. Compared with other state-of-the-art works, our framework achieves higher accuracy and lower time consumption.

Medical images contain a large amount of patients’ private information. The theft and destruction of medical images will cause irreparable losses to patients and medical institutions. In order to detect the region of interest(ROI) accurately, avoid leakage of ROI position information, and realize lossless recovery of transform domain encryption, we propose a novel lossless medical image encryption scheme based on game theory with optimized ROI parameters and hidden ROI position. In the encryption process, the ROI is a pixel-level transformed to achieve the lossless decryption of medical images and protect medical image information from loss. At the same time, the position information of the ROI is effectively hidden, and leakage of the position information during transmission is avoided. In addition, the quantum cell neural network(QCNN) hyperchaotic system generates random sequence to scramble and diffuse the ROI. Most important of all, the quantitative analysis method of ROI parameters is given, and the optimal balance between encryption speed and encryption security performance is achieved by using game theory. Simulation experiments and numerical analysis verify that the scheme achieves optimized and lossless encryption and decryption of images, and can flexibly and reliably protect the medical images of different types and structures against various attacks.

With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work. However, malware such as viruses, Trojans, and extortion applications have introduced threats to personal privacy and social security. Malware of the Android operating system has a great variety and updates rapidly. Android malware detection is faced with the problems of high feature dimension and unsatisfied detection accuracy of single classification algorithms. In this work, an Android malware detection framework GA-StackingMD is presented, which employs Stacking to compose five different base classifiers, and Genetic Algorithm is applied to optimize the hyperparameters of the framework. Experiments show that Stacking could effectively improve malware detection accuracy compared with single classifiers. The presented GA-StackingMD achieves 98.43% and 98.66% accuracies on CIC-AndMal2017 and CICMalDroid2020 data sets, which shows the effectiveness and feasibility of the proposed method.

Network attack identification effectively secures Internet of Things (IoT) application scenarios. However, dynamic scene changes, attack feature reliance, high data dimensions, and challenges with spatio-temporal feature fusion frequently pose limitations to attack traffic identification in IoT contexts. Definitive intelligent IoT attack identification enables intelligent algorithms to extract attack features for application scenarios with fixed topological environments but cannot construct the intricate changes of IoT application scenarios. Through the dynamic acquisition, feature awareness, and deep learning, intelligent digital twin-based attack detection can address these issues and enhance attack identification for IoT threats. Thus, this paper proposed an intelligent digital twin method based on spatio-temporal feature fusion for IoT attack behavior identification. Firstly, feature subsets are selected based on information gain to reduce the dimensionality of IoT data with high traffic; Secondly, a parallel spatio-temporal feature extraction model is designed unlike the existing tandem model, which uses a simplified Convolutional Neural Networks (CNN) model to learn the spatial features of the attack, a Bi-directional Long Short-Term Memory (BiLSTM) model to learn the temporal features of the attack, an attention mechanism to fuse the temporal and spatial features, and the (Deep Neural Networks) DNN to learn the combined features; Finally, the virtual instance space and topology of the attack scenario are simulated using digital twin (DT) to build a digital version of the complex system for IoT applications and tested in a simulation environment. Based on experimental results using the UNSW-NB15 and CICIDS2017 datasets, this paper shows that the proposed method can extract spatio-temporal features from network attack traffic and has a 5% improvement in test accuracy.

In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.

Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.