In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

The Time-based One-Time Password (TOTP) algorithm is commonly used for two-factor authentication. In this algorithm, a shared secret is used to derive a One-Time Password (OTP). However, in TOTP, the client and the server need to agree on a shared secret (i.e., a key). As a consequence, an adversary can construct an OTP through the compromised key if the server is hacked. To solve this problem, Kogan et al. proposed T/Key, an OTP algorithm based on a hash chain. However, the efficiency of OTP generation and verification is low in T/Key. In this article, we propose a novel and efficient Merkle tree-based One-Time Password (MOTP) algorithm to overcome such limitations. Compared to T/Key, this proposal reduces the number of hash operations to generate and verify the OTP, at the cost of small server storage and tolerable client storage. Experimental analysis and security evaluation show that MOTP can resist leakage attacks against the server and bring a tiny delay to two-factor authentication and verification time.

With the rapid development and widespread application of cloud computing, cloud computing open networks and service sharing scenarios have become more complex and changeable, causing security challenges to become more severe. As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic and their classification accuracy needs to be improved. To fill this gap, this paper proposes an anomaly network traffic detection model integrating temporal and spatial features (ITSN) using a three-layer parallel network structure. ITSN learns the temporal and spatial features of the traffic and fully fuses these two features through feature fusion technology to improve the accuracy of network traffic classification. On this basis, an improved method of raw traffic feature extraction is proposed, which can reduce redundant features, speed up the convergence of the network, and ease the imbalance of the datasets. The experimental results on the ISCX-IDS 2012 and CICIDS 2017 datasets show that the ITSN can improve the accuracy of anomaly network traffic detection while enhancing the robustness of the detection system and has a higher recognition rate for positive samples.