Sur Singh Rawat^1,*, Youseef Alotaibi², Nitima Malsa¹, Vimal Gupta¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1797-1815, 2023, DOI:10.32604/cmc.2023.039381

Abstract Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores… More >

Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Journal of Cyber Security, Vol.4, No.4, pp. 287-298, 2022, DOI:10.32604/jcs.2023.042486

Abstract Unmanned Aerial Vehicles (UAVs) proliferate quickly and play a significant part in crucial tasks, so it is important to protect the security and integrity of UAV communication channels. Intrusion Detection Systems (IDSs) are required to protect the UAV communication infrastructure from unauthorized access and harmful actions. In this paper, we examine a new approach for enhancing intrusion detection in UAV communication channels by utilizing the Long Short-Term Memory network (LSTM) combined with the Synthetic Minority Oversampling Technique (SMOTE) algorithm, and this integration is the binary classification method (LSTM-SMOTE). We successfully achieved 99.83% detection accuracy by using the proposed approach and… More >

Muhammad Waqas Nadeem^1,*, Hock Guan Goh¹, Yichiet Aun¹, Vasaki Ponnusamy²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2201-2217, 2023, DOI:10.32604/csse.2023.039893

Abstract Over the past few years, rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems. As a result, greater intelligence is necessary to effectively manage, optimize, and maintain these systems. Due to their distributed nature, machine learning models are challenging to deploy in traditional networks. However, Software-Defined Networking (SDN) presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes. SDN provides a centralized network view and allows for dynamic updates of flow rules and software-based traffic analysis. While the programmable nature of SDN makes… More >

Leilei Li¹, Yansong Wang², Dongjie Zhu^2,*, Xiaofang Li³, Haiwen Du⁴, Yixuan Lu², Rongning Qu³, Russell Higgs⁵

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 771-791, 2023, DOI:10.32604/cmc.2023.038268

Abstract With the rapid development of Internet technology, the issues of network asset detection and vulnerability warning have become hot topics of concern in the industry. However, most existing detection tools operate in a single-node mode and cannot parallelly process large-scale tasks, which cannot meet the current needs of the industry. To address the above issues, this paper proposes a distributed network asset detection and vulnerability warning platform (Dis-NDVW) based on distributed systems and multiple detection tools. Specifically, this paper proposes a distributed message subscription and publication system based on Zookeeper and Kafka, which endows Dis-NDVW with the ability to parallelly… More >

Yea-Sul Kim¹, Ye-Eun Kim¹, Hwankuk Kim^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550

Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer from generalization errors due to… More >

Junfeng Sun¹, Chenghai Li¹, Yafei Song^1,*, Peng Ni², Jian Wang¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 993-1021, 2023, DOI:10.32604/csse.2023.039215

Abstract The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore, a prediction model of TCAN-BiGRU… More >

Khaled M. Alalayah¹, Fatma S. Alrayes², Jaber S. Alzahrani³, Khadija M. Alaidarous¹, Ibrahim M. Alwayle¹, Heba Mohsen⁴, Ibrahim Abdulrab Ahmed⁵, Mesfer Al Duhayyim^6,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3121-3139, 2023, DOI:10.32604/csse.2023.036352

Abstract With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL)… More >

Muhammad Zulkifl Hasan^*, Zurina Mohd Hanapi, Muhammad Zunnurain Hussain

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4065-4084, 2023, DOI:10.32604/cmc.2023.036444

Abstract A computer network can be defined as many computing devices connected via a communication medium like the internet. Computer network development has proposed how humans and devices communicate today. These networks have improved, facilitated, and made conventional forms of communication easier. However, it has also led to uptick in-network threats and assaults. In 2022, the global market for information technology is expected to reach $170.4 billion. However, in contrast, 95% of cyber security threats globally are caused by human action. These networks may be utilized in several control systems, such as home-automation, chemical and physical assault detection, intrusion detection, and… More >

Ming Cheng¹, Shiming Li^1,3,*, Yuhe Wang¹, Guohui Zhou¹, Peng Han¹, Yan Zhao²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2527-2555, 2023, DOI:10.32604/cmc.2023.036427

Abstract To address the problem of network security situation assessment in the Industrial Internet, this paper adopts the evidential reasoning (ER)algorithm and belief rule base (BRB) method to establish an assessment model. First, this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge. Second, the evaluation indicators are fused with expert knowledge and the ER algorithm. According to the fusion results, a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established, and the projection covariance matrix adaptive evolution… More >

Shixin Tu, Yuanyuan Jia, Jinglong Du^*, Baoru Han^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 293-321, 2023, DOI:10.32604/cmes.2023.022308

Abstract The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,… More >