Bowen Zhang^1,2,3, Yanbo Shi⁴, Jianming Zhao^1,2,3,*, Tianyu Wang^1,2,3, Kaidi Wang⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2703-2717, 2023, DOI:10.32604/iasc.2023.033762 - 15 March 2023

Abstract Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the… More >

Younghoon Ban, Jeong Hyun Yi, Haehyun Cho^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555 - 09 February 2023

Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their… More >

Maha Sabir¹, Jawad Ahmad^2,*, Daniyal Alghazzawi¹

Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277 - 20 January 2023

Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based… More >

Chen Chen^1,2, Xingqiu Li^2,3,*, Kai Huang⁴, Zhongwei Xu¹, Meng Mei¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 471-485, 2023, DOI:10.32604/cmes.2023.024033 - 05 January 2023

Abstract Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train’s safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a two-dimensional image by data More >

Mohammed Maray¹, Haya Mesfer Alshahrani², Khalid A. Alissa³, Najm Alotaibi⁴, Abdulbaset Gaddah⁵, Ali Meree^1,6, Mahmoud Othman⁷, Manar Ahmed Hamza^8,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6587-6604, 2023, DOI:10.32604/cmc.2023.034176 - 28 December 2022

Abstract In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed… More >

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Yu-Chieh Lin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128 - 31 August 2022

Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time More >

Amal H. Alharbi¹, S. Karthick², K. Venkatachalam³, Mohamed Abouhawwash^4,5, Doaa Sami Khafaga^1,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2773-2787, 2023, DOI:10.32604/iasc.2023.030763 - 17 August 2022

Abstract Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame… More >

Helen Josephine V L¹, Ramchand Vedaiyan², V. M. Arul Xavier³, Joy Winston J⁴, A. Jegatheesan⁵, D. Lakshmi⁶, Joshua Samuel Raj^7,*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 701-714, 2023, DOI:10.32604/csse.2023.030611 - 16 August 2022

Abstract The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or… More >

Ghada Emam Atteia^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 361-376, 2023, DOI:10.32604/csse.2023.029597 - 16 August 2022

Abstract Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates… More >

Na Hyoun Kim, Namgyu Kim^*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2667-2682, 2023, DOI:10.32604/csse.2023.029732 - 01 August 2022

Abstract Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities. Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images. In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality. Thus, an appropriate process for removing noise must precede to improve object detection performance. Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously. In this paper, we More >