Shafi Ullah¹, Sibghat Ullah Bazai^1,*, Mohammad Imran², Qazi Mudassar Ilyas^3,*, Abid Mehmood⁴, Muhammad Asim Saleem⁵, Muhmmad Aasim Rafique³, Arsalan Haider⁶, Ilyas Khan⁷, Sajid Iqbal³, Yonis Gulzar⁴, Kauser Hameed³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 93-115, 2024, DOI:10.32604/cmc.2024.048796

Abstract Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices (MTCDs) regularly share extensive data without human intervention while making all types of decisions. These decisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeat monitoring, and several different alert systems. Many of these devices simultaneously share data to form an automated system. The data shared between machine-type communication devices (MTCDs) is prone to risk due to limited computational power, internal memory, and energy capacity. Therefore, securing the data and devices becomes challenging… More >

Peng Liao¹, Chaoge Liu², Jie Yin^1,3,*, Zhi Wang², Xiang Cui²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3255-3285, 2024, DOI:10.32604/cmes.2024.043855

Abstract Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens (NFTs). To be specific, the total trading volume of digital assets reached an astounding $55.5 billion in 2022. Nevertheless, numerous security concerns have been raised by the rapid expansion of the NFT ecosystem. NFT holders are exposed to a plethora of scams and traps, putting their digital assets at risk of being lost. However, academic research on NFT security is scarce, and the security issues have aroused rare attention. In this study, the NFT ecological process is comprehensively explored. This process falls into five different… More >

Pavlo Mykytyn^1,2,*, Marcin Brzozowski¹, Zoya Dyka^1,2, Peter Langendoerfer^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1019-1050, 2024, DOI:10.32604/cmes.2023.029075

Abstract The application field for Unmanned Aerial Vehicle (UAV) technology and its adoption rate have been increasing steadily in the past years. Decreasing cost of commercial drones has enabled their use at a scale broader than ever before. However, increasing the complexity of UAVs and decreasing the cost, both contribute to a lack of implemented security measures and raise new security and safety concerns. For instance, the issue of implausible or tampered UAV sensor measurements is barely addressed in the current research literature and thus, requires more attention from the research community. The goal of this survey is to extensively review… More >

Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721

Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to classify Android applications into malware… More >

Shangting Miao¹, Yang Li^2,*, Quan Pan²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2745-2762, 2023, DOI:10.32604/cmc.2023.037257

Abstract A space called Unmanned Aerial Vehicle (UAV) cyber is a new environment where UAV, Ground Control Station (GCS) and business processes are integrated. Denial of service (DoS) attack is a standard network attack method, especially suitable for attacking the UAV cyber. It is a robust security risk for UAV cyber and has recently become an active research area. Game theory is typically used to simulate the existing offensive and defensive mechanisms for DoS attacks in a traditional network. In addition, the honeypot, an effective security vulnerability defense mechanism, has not been widely adopted or modeled for defense against DoS attack… More >

Seshu Bhavani Mallampati¹, Seetha Hari^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1721-1744, 2023, DOI:10.32604/cmc.2023.040567

Abstract Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are utilized. The Adaptive Synthetic oversampling… More >

Nouf Saeed Alotaibi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2061-2078, 2023, DOI:10.32604/cmc.2023.039446

Abstract Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the input characteristics for the IDS… More >

Mohammad S. Al-kahtani¹, Zahid Mehmood^2,3,*, Tariq Sadad⁴, Islam Zada⁵, Gauhar Ali⁶, Mohammed ElAffendi⁶

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673

Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) and genetic algorithm (GA)) for… 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 >

Emad Alsuwat^1,*, Suhare Solaiman¹, Hatim Alsuwat²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3743-3759, 2023, DOI:10.32604/cmc.2023.035126

Abstract Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network.… More >