Can Wavelet Transform Detect LDDoS Abnormal Traffic in Multipath TCP Transmission System?

With the rapid development of mobile Internet technology and multihost terminal devices, multipath transmission protocol has been widely concerned. Among them, multipath TCP (MPTCP) has become a hot research protocol in recent years because of its good transmission performance and Internet compatibility. Due to the increasing power of Low-Rate Distributed Denial of Service (LDDoS) attack, the network security situation is becoming increasingly serious. The robustness of MPTCP network has become an urgent performance index to improve. Therefore, it is very necessary to detect LDDoS abnormal traffic timely and effectively in the transmission system based on MPTCP. This paper tries to use wavelet transform technology to decompose and reconstruct network traffic and find a detection method of LDDoS abnormal traffic in the MPTCP transmission system. The experimental results show that in the MPTCP transmission system, the signal processing technology based on wavelet transform can realize the identification of LDDoS abnormal traffic. It indicates a direction worth further exploration for the detection and defense of the LDDoS attack.

Resource Allocation Algorithm Based on Power Control and Dynamic Transmission Protocol Configuration for HAPS-IMT Integrated System

The high altitude platform station (HAPS) system is an essential component of the air-based network. It can shorten transmission delay and make a better user experience compared with satellite networks, and it can also be easily deployed and cover a larger area compared with international mobile telecommunications (IMT). In order to meet the needs of users with asymmetric and random data flow, the spectrum sharing and dynamic time division duplexing (TDD) mode are used in HAPS-IMT heterogeneous network. However, the cross-link interference brought by TDD mode will lead to the degradation of system performance. In this paper, a resource allocation algorithm based on power control and dynamic transmission protocol configuration is proposed. Firstly, a specific timeslot, “low power almost-bank subframe (LP-ABS)”, is introduced into the frame structure of the HAPS physical layer. The transmission protocol designing could mitigate inter-layer interference efficiently by power control in “LP-ABS”. Secondly, the utilization function is adopted for assessing the system performance, which gives attention to both diversified requirements on the quality of services (QoS) and the throughput of the HAPS-IMT system. Simulation results show that power control and resource allocation technologies proposed in this paper can effectively improve system performance and user satisfaction.

Research on Privacy Data Protection Based on Trusted Computing and Blockchain

Aiming at the problems of low protection accuracy and long time consumption in traditional privacy data protection methods, a privacy data protection method based on trusted computing and blockchain is proposed. Set up the Internet node secure transmission protocol through the trusted node uplink transmission protocol and the downlink transmission protocol, and according to the transmission protocol, combined with the blockchain technology, the ECC elliptic curve encryption algorithm is used to encrypt the amount of data existing in the blockchain, and the AES symmetric encryption algorithm is used to encrypt the private data that exists in the nonblockchain, thereby completing the protection of network private data. The simulation experiment results show that the privacy data protection accuracy of the proposed method is higher and the work efficiency is faster.

A Novel Model for Large-Scale Online College Learning in Postpandemic Era: AI-Driven Approach

COVID-19 is a pandemic with a wide reach and explosive magnitude, and the world has been bracing itself for impact. Many have lost their jobs and savings, and many are homeless. For better or worse, COVID-19 has permanently changed our lives. For college students, the pandemic means giving up most of the on-campus experience in the postpandemic era and performing online learning instead. Virtual lessons may become a permanent part of college education. Large-scale online learning typically utilizes interactive live video streaming. In this study, we analyzed a codec and video streaming transmission protocol using artificial intelligence. First, we studied an intraframe prediction optimization algorithm for the H.266 codec based on long short-term memory networks. In terms of video streaming transmission protocols, real-time communication optimization based on Quick UDP Internet connections and Luby Transform codes is proposed to improve the quality of interactive live video streaming. Experimental results demonstrate that the proposed strategy outperforms three benchmarks in terms of video streaming quality, video streaming latency, and average throughput.

Holographic photonic neuron

The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ, which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a neuromorphic quantum-photonic processor that mimics the brain

Optimization and Performance Assessment of a Logic Selectivity Solution Based on LoRa Communication

Ensuring service continuity has become a fundamental issue for companies involved in electricity distribution; in particular, isolating the smallest possible portion of the network as a result of faults has long been a primary objective. To this aim, solutions based on logic selectivity have been defined and implemented for an efficient search for the network branch affected by the fault and its subsequent isolation. The authors have recently presented a proposal for the implementation of logic selectivity that exploits the LoRa transmission protocol, an ideal solution in the case of areas not reachable by the currently exploited communication technologies. The present paper, instead, deals with the optimization of some LoRa parameters, which made it possible to exploit network configurations in terms of coverage range, sensitivity and signal-to-noise ratio. The performance of the new configuration has been assessed through a number of tests conducted in the laboratory and on-field, highlighting promising results in terms of both intervention times and reliability. In particular, tests conducted in both rural and urban areas have assured fault isolation times as low as 33 ms (fully compliant with the current regulations) in the presence of the most challenging fault condition.

Design of transceiver based on functional programming

This paper discusses the setting of transceiver based on functional programming, and decouples the differences between communication protocol and network transmission protocol from the specific business implementation. In the actual application, the service implementation is set up. When the communication protocol changes, the function can be realized only by modifying the communication protocol, which improves the readability of the program and reduces its maintenance overhead

Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective

In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sensing devices, their energy and computational constraints, and their collective (collaborative) dimension. These constraints have a fundamental impact on the final activity recognition models as the quality of the data, its availability, and its reliability, among other things, are not ensured during model deployment in real-world configurations. Current approaches for activity recognition rely on the activity recognition chain which defines several steps that the sensed data undergo: This is an inductive process that involves exploring a hypothesis space to find a theory able to explain the observations. For activity recognition to be effective and robust, this inductive process must consider the constraints at all levels and model them explicitly. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity, dynamicity, or stochastic effects, it is essential to understand their substantial impact on the quality of the data and ultimately on activity recognition models. This study highlights the need to exhibit the different types of biases arising in real situations so that machine learning models, e.g., can adapt to the dynamicity of these environments, resist sensor failures, and follow the evolution of the sensors’ topology. We propose a metamodeling approach in which these biases are specified as hyperparameters that can control the structure of the activity recognition models. Via these hyperparameters, it becomes easier to optimize the inductive processes, reason about them, and incorporate additional knowledge. It also provides a principled strategy to adapt the models to the evolutions of the environment. We illustrate our approach on the SHL dataset, which features motion sensor data for a set of human activities collected in real conditions. The obtained results make a case for the proposed metamodeling approach; noticeably, the robustness gains achieved when the deployed models are confronted with the evolution of the initial sensing configurations. The trade-offs exhibited and the broader implications of the proposed approach are discussed with alternative techniques to encode and incorporate knowledge into activity recognition models.

Wireless monitoring algorithm of intelligent greenhouse based on fuzzy control

The existing greenhouse monitoring algorithm has a long delay time, so it is unable to carry out effective remote greenhouse monitoring, therefore, a new wireless monitoring algorithm based on the fuzzy control technolog was put forward, which was able to remotely monitor the greenhouse temperature, humidity and illumination data in real time. Firstly, the overall framework of greenhouse monitoring algorithm was built, including fuzzy clustering algorithm and sensing layer devices. Secondly, the temperature-humidity sensors and light sensitivity sensors in the sensing layer devices were used to deeply mine and optimize the parameters of temperature, humidity and light intensity in current greenhouse, so as to ensure the stability of subsequent transmission. Meanwhile, the corresponding perceptual recognition layer and broadband access method were designed, and GPRS technology was used to feed back the data information to the monitoring data layer through temperature-humidity sensors and light sensitivity sensors. Moreover, UDP protocol was taken as the data core transmission protocol, and the adaptive protection design algorithm was proposed to ensure the most reasonable transmission of monitoring data, get the current monitoring data of temperature, humidity and illuminance. The experimental results show that the maximum delay time of the algorithm is 46 s, which is far lower than the traditional algorithm, and the delay time of temperature monitoring is also lower than the traditional algorithm. It is results show that the response delay of remote intelligent greenhouse monitoring algorithm is low and the overall monitoring effect is ideal. The purpose of monitoring temperature, humidity and illumination can be achieved.