An Efficient Hyperparameter Control Method for a Network Intrusion Detection System Based on Proximal Policy Optimization

The complexity of network intrusion detection systems (IDSs) is increasing due to the continuous increases in network traffic, various attacks and the ever-changing network environment. In addition, network traffic is asymmetric with few attack data, but the attack data are so complex that it is difficult to detect one. Many studies on improving intrusion detection performance using feature engineering have been conducted. These studies work well in the dataset environment; however, it is challenging to cope with a changing network environment. This paper proposes an intrusion detection hyperparameter control system (IDHCS) that controls and trains a deep neural network (DNN) feature extractor and k-means clustering module as a reinforcement learning model based on proximal policy optimization (PPO). An IDHCS controls the DNN feature extractor to extract the most valuable features in the network environment, and identifies intrusion through k-means clustering. Through iterative learning using the PPO-based reinforcement learning model, the system is optimized to improve performance automatically according to the network environment, where the IDHCS is used. Experiments were conducted to evaluate the system performance using the CICIDS2017 and UNSW-NB15 datasets. In CICIDS2017, an F1-score of 0.96552 was achieved and UNSW-NB15 achieved an F1-score of 0.94268. An experiment was conducted by merging the two datasets to build a more extensive and complex test environment. By merging datasets, the attack types in the experiment became more diverse and their patterns became more complex. An F1-score of 0.93567 was achieved in the merged dataset, indicating 97% to 99% performance compared with CICIDS2017 and UNSW-NB15. The results reveal that the proposed IDHCS improved the performance of the IDS by automating learning new types of attacks by managing intrusion detection features regardless of the network environment changes through continuous learning.

Various Routing Protocols of Vehicular Ad hoc Network: A Survey

A vehicular ad hoc network is a type of network divided by an area where car nodes can join or leave the network. Due to the flexibility of the network environment active contractual routes are used in the development of the route from the source to the destination. Various active and active protocols are updated in this paper. Active routing protocols are those that use current network information in the development of the route from the source to the destination. Active routing protocols are those that use the pre-defined network information in the route setting. Active route agreements use route tables in route development. In this review paper, a literature survey was conducted on VANET route protocols. It is analyzed that an effective route protocol offers higher performance compared to active router protocols. Keywords: VANET, Reactive, Proactive Routing

Duty-Cycle-Based Pre-Emption Protocol for Emergency Networks

This paper proposes a new duty-cycle-based protocol for transmitting emergent data with high priority and low latency in a sensor network environment. To reduce power consumption, the duty cycle protocol is divided into a listen section and a sleep section, and data can only be received when the receiving node is in the listen section. In this paper, high-priority transmission preempts low-priority transmission by distinguishing between high-priority preamble and low-priority preamble. However, even when a high priority transmission preempts a low priority transmission such that the high priority transmission is received first, if the sleep period is very long, the delay may be large. To solve this problem, the high priority short preamble and high priority data reduce receiver sensitivity and increase coverage through repeated transmission. If there are several receiving nodes within a wide coverage, the receiving node that wakes up first can receive the transmission, thus reducing the delay. The delay can also be further reduced by alternately reducing the sleep cycle of one node among the receiving nodes that can receive it. This paper shows that emergent data can be transmitted effectively and reliably by reducing the delay of high-priority data to a minimum through the use of preemption, coverage extension, and an asymmetric sleep cycle.

METHODOLOGY OF NETWORK ENVIRONMENT TESTING FOR IOT DEVICES

The article describes the basics of testing: writing test documentation (an example based on a report defect was proposed) and some testing methods. A performance test was also developed to test the load. Received basic knowledge of testing theory, as well as skills of writing and using bash scripts for performance tests.

A fusion data security protection scheme for sensitive E-documents in the open network environment

E-documents are carriers of sensitive data, and their security in the open network environment has always been a common problem with the field of data security. Based on the use of encryption schemes to construct secure access control, this paper proposes a fusion data security protection scheme. This scheme realizes the safe storage of data and keys by designing a hybrid symmetric encryption algorithm, a data security deletion algorithm, and a key separation storage method. The scheme also uses file filter driver technology to design a user operation state monitoring method to realize real-time monitoring of user access behavior. In addition, this paper designs and implements a prototype system. Through the verification and analysis of its usability and security, it is proved that the solution can meet the data security protection requirements of sensitive E-documents in the open network environment.

Anchor voiceprint recognition in live streaming via RawNet-SA and gated recurrent unit

With the sharp booming of online live streaming platforms, some anchors seek profits and accumulate popularity by mixing inappropriate content into live programs. After being blacklisted, these anchors even forged their identities to change the platform to continue live, causing great harm to the network environment. Therefore, we propose an anchor voiceprint recognition in live streaming via RawNet-SA and gated recurrent unit (GRU) for anchor identification of live platform. First, the speech of the anchor is extracted from the live streaming by using voice activation detection (VAD) and speech separation. Then, the feature sequence of anchor voiceprint is generated from the speech waveform with the self-attention network RawNet-SA. Finally, the feature sequence of anchor voiceprint is aggregated by GRU to transform into a deep voiceprint feature vector for anchor recognition. Experiments are conducted on the VoxCeleb, CN-Celeb, and MUSAN dataset, and the competitive results demonstrate that our method can effectively recognize the anchor voiceprint in video streaming.

Optimization Method of Art Design Resource Scheduling for 6G Network Environment in Colleges and Universities

In order to solve the problem of low flexibility margin of traditional art and design resource scheduling in colleges and universities, an optimization method for art and design resource scheduling in the 6G network environment has been designed. By determining the flexibility margin index of university art and design resource scheduling, the scheduling optimization model is established, the scheduling communication parameters are set for the 6G network environment, the delay of university art and design resource scheduling is perceived, the period of insufficient flexibility is searched, and elimination measures are taken to realize the optimization of university art and design resource scheduling. The experimental results show that the margin of the designed scheduling method is always higher than that of the experimental control group in the same scheduling period, which can solve the problem of low scheduling flexibility margin of traditional methods.

Research on Task-Service Network Node Matching Method Based on Multi-Objective Optimization Model in Dynamic Hyper-Network Environment

In order to reduce the cost of manufacturing and service for the Cloud 3D printing (C3DP) manufacturing grid, to solve the problem of resources optimization deployment for no-need preference under circumstance of cloud manufacturing, consider the interests of enterprises which need Cloud 3D printing resources and cloud platform operators, together with QoS and flexibility of both sides in the process of Cloud 3D printing resources configuration service, a task-service network node matching method based on Multi-Objective optimization model in dynamic hyper-network environment is built for resource allocation. This model represents interests of the above-mentioned two parties. In addition, the model examples are solved by modifying Mathematical algorithm of Node Matching and Evolutionary Solutions. Results prove that the model and the algorithm are feasible, effective and stable.