Open Access

ARTICLE

J. Pavithra^*, S. Selvakumarasamy
Journal of Cyber Security, Vol.4, No.3, pp. 135-151, 2022, DOI:10.32604/jcs.2022.031889 - 01 February 2023
Abstract Machine learning (ML) is often used to solve the problem of malware detection and classification, and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To address the problem, an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier “AFC-XG Boost” is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the XG Boost classifier in several stages to optimize performance.… More >

View

1424

Download

720

Open Access

ARTICLE

Y. Sarada Devi^*, M. Roopa
Journal of Cyber Security, Vol.4, No.3, pp. 153-167, 2022, DOI:10.32604/jcs.2022.033436 - 01 February 2023
Abstract Vehicular ad hoc networks (VANETs) are designed in accordance with the ad hoc mobile networks (MANETs), i.e., impulsive formation of a wireless network for V2V (vehicle-to-vehicle) communication. Each vehicle is preserved as a node which remains as share of network. All the vehicle in the network is made to be under communication in a VANET because of which all the vehicles in the range can be made connected to a to a unit & a wide network can be established with a huge range. Healthier traffic management, vehicle-to-vehicle communication and provision of road information can… More >

View

1942

Download

1076

Open Access

ARTICLE

Ahmad Moawad^*, Ahmed Ismail Ebada, Aya M. Al-Zoghby
Journal of Cyber Security, Vol.4, No.3, pp. 169-184, 2022, DOI:10.32604/jcs.2022.033537 - 01 February 2023
Abstract In computer security, the number of malware threats is increasing and causing damage to systems for individuals or organizations, necessitating a new detection technique capable of detecting a new variant of malware more efficiently than traditional anti-malware methods. Traditional anti-malware software cannot detect new malware variants, and conventional techniques such as static analysis, dynamic analysis, and hybrid analysis are time-consuming and rely on domain experts. Visualization-based malware detection has recently gained popularity due to its accuracy, independence from domain experts, and faster detection time. Visualization-based malware detection uses the image representation of the malware binary More >

View

1282

Download

700

Open Access

ARTICLE

A. S. Yusuf^1,*, A. Q. Ayinde²
Journal of Cyber Security, Vol.4, No.3, pp. 185-188, 2022, DOI:10.32604/jcs.2022.035446 - 01 February 2023
Abstract Hospitals provide daily health services for thousands of patients. People, processes, and technologies drive the objectives and goals of the hospitals to ensure optimal and satisfactory health care services are rendered to their customers. Due to the sensitivity of the organization data and patient data, it is essential to ensure that the confidentiality, integrity, availability, and security of these data are considered. The leadership of the organization (managers and executives) must integrate a robust security plan when choosing the technologies that will be used to drive the organization’s processes. This paper will evaluate the existing More >

View

1203

Download

677

Like

1

Open Access

ARTICLE

Yanyu Chen¹, Zhangjie Fu^1,2,*
Journal of Cyber Security, Vol.4, No.3, pp. 189-200, 2022, DOI:10.32604/jcs.2022.038401 - 01 February 2023
Abstract As a typical representative of web 2.0, Ethereum has significantly boosted the development of blockchain finance. However, due to the anonymity and financial attributes of Ethereum, the number of phishing scams is increasing rapidly and causing massive losses, which poses a serious threat to blockchain financial security. Phishing scam address identification enables to detect phishing scam addresses and alerts users to reduce losses. However, there are three primary challenges in phishing scam address recognition task: 1) the lack of publicly available large datasets of phishing scam address transactions; 2) the use of multi-order transaction information… More >

View

1726

Download

762