Reem Alkanhel¹, El-Sayed M. El-kenawy^2,3, D. L. Elsheweikh⁴, Abdelaziz A. Abdelhamid^5,6, Abdelhameed Ibrahim⁷, Doaa Sami Khafaga^8,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 427-442, 2023, DOI:10.32604/cmc.2023.032885 - 06 February 2023

Abstract Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm More >

Nga Nguyen Thi Thanh, Quang H. Nguyen^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107 - 20 January 2023

Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. More >

M. Vargheese^1,*, S. Vanithamani², D. Stalin David³, Ganga Rama Koteswara Rao⁴

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3479-3499, 2023, DOI:10.32604/iasc.2023.030948 - 17 August 2022

Abstract Wireless networks with no infrastructure arise as a result of multiple wireless devices working together. The Mobile Ad hoc Network (MANET) is a system for connecting independently located Mobile Nodes (MNs) via wireless links. A MANET is self-configuring in telecommunications, while MN produces non-infrastructure networks that are entirely decentralized. Both the MAC and routing layers of MANETs take into account issues related to Quality of Service (QoS). When culling a line of optical discernment communication, MANET can be an effective and cost-saving route cull option. To maintain QoS, however, more or fewer challenges must be overcome. This… More >

Guoqing Xu¹, Changsen Xia¹, Jun Qian¹, Guo Ran³, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066 - 28 March 2023

Abstract Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and More >

Yangjae Lee¹, Sung Hoon Baek², Jung Taek Seo³, Ki-Woong Park^1,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4911-4929, 2022, DOI:10.32604/cmc.2022.026657 - 21 April 2022

Abstract One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS. Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world. An adversary can collect information regarding senders, recipients, and prime-time slots through… More >

Ghalib H. Alshammri^1,2, Amani K. Samha³, Ezz El-Din Hemdan⁴, Mohammed Amoon^1,4, Walid El-Shafai^5,6,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3529-3548, 2022, DOI:10.32604/cmc.2022.025262 - 29 March 2022

Abstract Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process. In recent times, the most complex task in Software Defined Network (SDN) is security, which is based on a centralized, programmable controller. Therefore, monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment. Consequently, this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms: K-means, Farthest First, Canopy, Density-based algorithm, and Exception-maximization (EM), using the Waikato Environment for Knowledge Analysis (WEKA) software to compare… More >

Mustafa Al-Fayoumi¹, Mohammad Al-Fawa’reh², Shadi Nashwan^3,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3091-3111, 2022, DOI:10.32604/cmc.2022.025103 - 29 March 2022

Abstract The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home. Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach. This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet… More >

Daniyal M. Alghazzawi¹, Syed Hamid Hasan^1,*, Surbhi Bhatia²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3877-3897, 2022, DOI:10.32604/cmc.2022.024613 - 29 March 2022

Abstract Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet,… More >

Muhammad Basit Umair¹, Zeshan Iqbal¹, Muhammad Bilal², Jamel Nebhen⁴, Tarik Adnan Almohamad³, Raja Majid Mehmood^5,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 407-422, 2022, DOI:10.32604/cmc.2022.020727 - 03 November 2021

Abstract Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify… More >

M. Govindarajan^1,*, V. Chandrasekaran², S. Anitha³

Computer Systems Science and Engineering, Vol.40, No.3, pp. 851-863, 2022, DOI:10.32604/csse.2022.019298 - 24 September 2021

Abstract Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time. With the use of mobile devices, communication services generate numerous data for every moment. Given the increasing dense population of data, traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation. A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning (RKLSTM-CTMDSL) model is introduced for traffic prediction with superior accuracy and minimal time consumption. The RKLSTM-CTMDSL model performs attribute selection and classification processes… More >