Dong-Wook Kim¹, Gun-Yoon Shin¹, Myung-Mook Han^2,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 153-164, 2023, DOI:10.32604/csse.2023.034589

Abstract Unlike external attacks, insider threats arise from legitimate users who belong to the organization. These individuals may be a potential threat for hostile behavior depending on their motives. For insider detection, many intrusion detection systems learn and prevent known scenarios, but because malicious behavior has similar patterns to normal behavior, in reality, these systems can be evaded. Furthermore, because insider threats share a feature space similar to normal behavior, identifying them by detecting anomalies has limitations. This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied… More >

Mohanapriya Marimuthu¹, Santhosh Rajendran², Reshma Radhakrishnan², Kalpana Rengarajan³, Shahzada Khurram⁴, Shafiq Ahmad⁵, Abdelaty Edrees Sayed⁵, Muhammad Shafiq^6,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4729-4745, 2023, DOI:10.32604/cmc.2023.033020

Abstract Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated… More >

Machiraju Jayalakshmi^{1, *}, S. Nagaraja Rao²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710

Abstract In recent years, the development in the field of computer-aided diagnosis (CAD) has increased rapidly. Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images. The existing algorithms have drawbacks with respect to their accuracy, efficiency, and limited learning processes. To address these issues, we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast, 2D- discrete wavelet transformation (2D-DWT) to extract the features, probabilistic principal component analysis (PPCA) and linear discriminant analysis (LDA) to normalize and reduce the features, and a feed-forward neural network (FNN)… More >