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Transport Layer
Published in Jerry D. Gibson, The Communications Handbook, 2018
In recent years, Internet traffic has grown both in quantity and variety, resulting in increasing utilization, delays, and often congestion. This has prompted the development of retransmission and windowing policies to reduce network congestion, mechanisms to reduce processing time for transport protocol messages, etc. However, this performance work is still governed by the premise, motivated by fault tolerance, that a transport protocol should have minimal knowledge of the network state.
An adaptive PI active queue management algorithm based on queue length
Published in Amir Hussain, Mirjana Ivanovic, Electronics, Communications and Networks IV, 2015
Hongcheng Huang*, Fan Yang, Shiwei Wang, Gaofei Xue
With the explosive growth of the Internet, the Internet traffic has increased quickly, so that network congestion occurs frequently. Network congestion will directly lead to the degradation of the entire network performance, such as the decrease of network throughput, and the increase of packet loss rate and end-to-end delay. The crash of the network will be caused by serious network congestion.
An Optimal Reinforced Deep Belief Network for Detection of Malicious Network Traffic
Published in IETE Journal of Research, 2023
The 5G IoT device will become the upcoming trend that faces the domains of energy, transportation, retail, manufacturing, etc [5]. In 5G, the Software Defined Networking (SDN) and Network Function Virtualization (NFV) facilitate the services of orchestration and dynamic creation as needed based on network slicing in the general physical infrastructure and offer flexibility, elasticity, scalability, programable and dynamic configuration. Therefore, 5G IoT has the capability of programmability and notarization that allows IoT to create new entities [6]. Network traffic research is the method that disrupts and inspects the messages for detecting the information from models. The study of network traffic is an important system for efficient problem solving to help the network facilities and network traffic processing is an automatic technique for determining the dynamic network traffic behavior using random processes [7]. The classification of network traffic is essential in the fast development of present computer networks [8]. Around 80% of internet traffic refers to peer-to-peer uses and the classification of network traffic can improve security, quality, network licensing, and accounting.
Enhancing website security against bots, spam and web attacks using lCAPTCHA
Published in International Journal of Computers and Applications, 2023
S. Vaithyasubramanian, D. Lalitha, C. K. Kirubhashankar
A statistical survey in 2016 on ‘Internet Traffic Analysis’ reflects approximately 44% of total Internet traffic is caused by manual consumers, while the rest are caused by spammers, scrapers, and automated systems [1]. A similar survey on March 2017 reveals that 49.7% of the global population consumes Internet usage services [2,3]. To provide various services in this digital technology, there are more than 1 billion web resources. Users, who require the service of a website, will have to solve a Turing test challenge offered by that website. It is malicious software defense scheme to differentiate human utilization of web services from robotic activity [4,5]. The method to establish whether or not a computer is able to think like a human being is the Turing test. CAPTCHA is a program that generates tests which require end-users to submit results for validation. To control the access of the resources web designers often use CAPTCHA. A key to network attacks – ‘CAPTCHA’ was found in 1997. VonAhn in 2003 initiated the concept of a CAPTCHA to be used in a wide manner [6,7].
Observer-based passive control of non-homogeneous Markov jump systems with random communication delays
Published in International Journal of Systems Science, 2020
Yun Chen, Zhangping Chen, Zhenyu Chen, Anke Xue
In the past decades considerable attention has been paid to Markov jump systems, since they can be applied to model those systems with abrupt variation of structures and parameters (Chen & Zheng, 2015; Krasovskii & Lidskii, 1961; Mariton, 1990; Wu et al., 2010). But in many actual applications, the transition probabilities/rates of Markov jump systems are not unchanging but are varying as time goes on. Taking the agriculture as an example, it is reasonable to estimate the declining numbers of cotton warehouses in Oklahoma, USA, by two dynamic Markov chain models, since the time-related non-stationary transition models is more desirable (Salkin et al., 1976). Another representative example of Markov jump systems with piecewise-constant transition probabilities is the networked control systems based on Internet. The Markov chains to model the data dropouts and communication delays in Internet are generally varying as time evolves (Internet Traffic Report, 2008). Thus, during the whole running time of the networked control systems, the transition probabilities are also time-related changing (Zhang, 2009). For such Markov jump systems, if the transition probabilities are slowly changing and can be simplified as piecewise constant, then they can be seen as jump systems whose transition probabilities are subject to certain constrained deterministic switchings (Colaneri, 2009; Zhang, 2009).