With the rapid development and application of Internet of Things (IoT) technology, a large number of various types of IoT devices with security vulnerabilities have been connected to the public network. Attackers only need to use malware to successfully infect a few types of IoT devices to generate large-scale propagation in the network and capture a large number of broilers, which in turn lays the foundation for subsequent attacks. In this study, an innovative malware propagation model [i.e., IoT-susceptible-latent-propagated-recovered (IoT-SLPR)] based on epidemiological theory is developed for malware propagation in multiple IoT heterogeneous devices. The proposed model incorporates the propagation mechanism of malware in real IoT networks, and considers heterogeneous devices with different infection rates and recovery rates. This study calculates and proves the basic reproductive rate of malware and the stability of equilibrium points for different types of devices. In addition, this study designs a dynamic recovery rate optimal defense strategy, which comprehensively considers the cost and the overall infection rate of the device. The experimental results show that the proposed propagation model can effectively reflect the propagation dynamics of malware in multiple heterogeneous devices, and the achieved dynamic recovery rate defense strategy performs better than the static strategy regarding the overall results.

The family homology determination of malware has become a research hotspot as the number of malware variants are on the rise. However, existing studies on malware visualization only determines homology based on the global structure features of executable, which leads creators of some malware variants with the same structure intentionally set to misclassify them as the same family. We sought to develop a homology determination method using the fusion of global structure features and local fine-grained features based on malware visualization. Specifically, the global structural information of the malware executable file was converted into a bytecode image, and the opcode semantic information of the code segment was extracted by the n-gram feature model to generate an opcode image. We also propose a dual-branch convolutional neural network, which features the opcode image and bytecode image as the final family classification basis. Our results demonstrate that the accuracy and F-measure of family homology classification based on the proposed scheme are 99.05% and 98.52% accurate, respectively, which is better than the results from a single image feature or other major schemes.

In this paper, a novel Mobile-Coverage scheme is proposed to improve the coverage and lifetime of hybrid sensor networks, and thus the target region can be completely covered with minimum number of mobile sensor nodes and the energy consumption can be reduced in hybrid sensor networks at the same time. Our scheme is applied to a hybrid network comprising of both static and mobile sensor nodes, which are randomly deployed in a rectangle area. We derived the minimum number of sensor nodes as well as the mobile nodes required to cover the target region completely. Compared to the existing works, the minimum number of mobile sensor nodes required in this paper is significantly smaller, which implies that the movement distance and energy consumption of mobile nodes in our scheme is reduced. Moreover, we proved that the construction of our scheme based on the Virtual Force and Voronoi Graph (VFVG) is correct, and then derived the optimal trade-off between the coverage of the target region and energy consumption of mobile nodes. Our scheme can achieve the maximum coverage which is 98.7% by minimum number of mobile sensor nodes. Meanwhile, a connectivity rate of 76.84% can also be achieved by our connectivity evaluation method. Finally, a new topology construction algorithm-Topology Construction and Maintenance based on Trust (TCMT) is created to further increase the lifetime of the sensor network. We also evaluated the performance of our scheme in terms of coverage and lifetime, and carried out extensive experiments to compare the coverage and energy consumption in our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated.

Physical layer security (PLS) is considered one of the most promising solutions to solve the security problems of massive Internet of Things (IoTs) devices because of its lightweight and high efficiency. Significantly, the recent physical layer key generation (PLKG) scheme based on transmission delay proposed by Huang et al. (2021) does not have any restrictions on communication methods and can extend the traditional physical layer security based on wireless channels to the whole Internet scene. However, the secret-sharing strategy adopted in this scheme has hidden dangers of collusion attack, which may lead to security problems such as information tampering and privacy disclosure. By establishing a probability model, this article quantitatively analyzes the relationship between the number of malicious collusion nodes and the probability of key exposure, which proves the existence of this security problem. In order to solve the problem of collusion attack in Huang et al.’s scheme, this article proposes an anti-collusion attack defense method, which minimizes the influence of collusion attack on key security by optimizing parameters including the number of the middle forwarding nodes, the random forwarding times, the time delay measurement times and the out-of-control rate of forwarding nodes. Finally, based on the game model, we prove that the defense method proposed in this article can reduce the risk of key leakage to zero under the scenario of the “Careless Defender” and “Cautious Defender” respectively.