Bassam Mohammad Elzaghmouri¹, Yosef Hasan Fayez Jbara², Said Elaiwat³, Nisreen Innab^4,*, Ahmed Abdelgader Fadol Osman⁵, Mohammed Awad Mohammed Ataelfadiel⁵, Farah H. Zawaideh⁶, Mouiad Fadeil Alawneh⁷, Asef Al-Khateeb⁸, Marwan Abu-Zanona⁸

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2299-2316, 2024, DOI:10.32604/cmc.2024.054836 - 18 November 2024

Abstract As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.… More >

Amardeep Singh¹, Hamad Ali Abosaq², Saad Arif³, Zohaib Mushtaq^4,*, Muhammad Irfan⁵, Ghulam Abbas⁶, Arshad Ali⁷, Alanoud Al Mazroa⁸

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 857-873, 2024, DOI:10.32604/cmc.2024.048036 - 25 April 2024

Abstract Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries, especially in light of the growing number of cybersecurity threats. A major and ever-present threat is Ransomware-as-a-Service (RaaS) assaults, which enable even individuals with minimal technical knowledge to conduct ransomware operations. This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models. For this purpose, the network intrusion detection dataset “UNSW-NB15” from the Intelligent Security Group of the University of New South Wales, Australia is analyzed. In the… More >

Adnan Hussein¹, Abdulwahab Ali Almazroi^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3853-3866, 2023, DOI:10.32604/cmc.2023.040856 - 26 December 2023

Abstract The advances in technology increase the number of internet systems usage. As a result, cybersecurity issues have become more common. Cyber threats are one of the main problems in the area of cybersecurity. However, detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its importance. This study aims to analyze Twitter data to detect cyber threats using a multiclass classification approach. The data is passed through different tasks to prepare it for the analysis. Term Frequency and Inverse Document Frequency (TFIDF) features are extracted… More >

Z. Faizal Khan¹, Saeed M. Alshahrani^2,*, Abdulrahman Alghamdi², Someah Alangari³, Nouf Ibrahim Altamami⁴, Khalid A. Alissa⁵, Sana Alazwari⁶, Mesfer Al Duhayyim⁷, Fahd N. Al-Wesabi⁸

Computer Systems Science and Engineering, Vol.47, No.1, pp. 855-871, 2023, DOI:10.32604/csse.2023.036735 - 26 May 2023

Abstract The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this… More >

So-Eun Jeon¹, Sun-Jin Lee¹, Eun-Young Lee¹, Yeon-Ji Lee², Jung-Hwa Ryu², Jung-Hyun Moon², Sun-Min Yi², Il-Gu Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4231-4253, 2023, DOI:10.32604/cmc.2023.034287 - 31 March 2023

Abstract Recently, with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic, the possibility of cyberattacks through endpoints has increased. Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats. In particular, because telecommuting, telemedicine, and tele-education are implemented in uncontrolled environments, attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information, and reports of endpoint attacks have been increasing considerably. Advanced persistent threats (APTs) using various novel variant malicious codes are a form of a sophisticated attack. However, conventional commercial antivirus and… More >

U. Sakthivelu, C. N. S. Vinoth Kumar^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3691-3707, 2023, DOI:10.32604/iasc.2023.036946 - 15 March 2023

Abstract The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood. Several cyber-attacks lead to the compromise of data security. The proposed system offers complete data protection from Advanced Persistent Threat (APT) attacks with attack detection and defence mechanisms. The modified lateral movement detection algorithm detects the APT attacks, while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm. Before termination, every cyber-attack undergoes multiple stages, with the most prominent stage being Lateral Movement (LM). The LM uses… More >

Fatma S. Alrayes¹, Najm Alotaibi², Jaber S. Alzahrani³, Sana Alazwari⁴, Areej Alhogail⁵, Ali M. Al-Sharafi⁶, Mahmoud Othman⁷, Manar Ahmed Hamza^8,*

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3037-3052, 2023, DOI:10.32604/csse.2023.033970 - 21 December 2022

Abstract Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla… More >

Fadwa Alrowais¹, Sami Althahabi², Saud S. Alotaibi³, Abdullah Mohamed⁴, Manar Ahmed Hamza^5,*, Radwa Marzouk⁶

Computer Systems Science and Engineering, Vol.45, No.1, pp. 687-700, 2023, DOI:10.32604/csse.2023.030188 - 16 August 2022

Abstract Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM)… More >

Fahad F. Alruwaili^*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5809-5824, 2022, DOI:10.32604/cmc.2022.031613 - 28 July 2022

Abstract Internet of Things (IoT) is one of the hottest research topics in recent years, thanks to its dynamic working mechanism that integrates physical and digital world into a single system. IoT technology, applied in industries, is termed as Industrial IoT (IIoT). IIoT has been found to be highly susceptible to attacks from adversaries, based on the difficulties observed in IIoT and its increased dependency upon internet and communication network. Intentional or accidental attacks on these approaches result in catastrophic effects like power outage, denial of vital health services, disruption to civil service, etc., Thus, there… More >

Yichiet Aun^*, Yen-Min Jasmina Khaw, Ming-Lee Gan, Vasaki Ponnusamy

Journal of Cyber Security, Vol.4, No.1, pp. 17-28, 2022, DOI:10.32604/jcs.2022.027524 - 05 May 2022

Abstract Resilient network infrastructure is pivotal for business entities that are growing reliance on the Internet. Distributed Denial-of-Service (DDOS) is a common network threat that collectively overwhelms and exhausts network resources using coordinated botnets to interrupt access to network services, devices, and resources. IDS is typically deployed to detect DDOS based on Snort rules. Although being fairly accurate, IDS operates on a compute-intensive packet inspection technique and lacks rapid DDOS detection. Meanwhile, SNMP is a comparably lightweight countermeasure for fast detection. However, this SNMP trigger is often circumvented if the DDOS burst rate is coordinated to… More >