Thalavaipillai, Sudalaimuthu
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Journal : International Journal of Reconfigurable and Embedded Systems (IJRES)

Kernel rootkit prevention model using multiclass Srinivasan, Suresh Kumar; Thalavaipillai, Sudalaimuthu
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp395-402

Abstract

Malicious individuals can access a computer network or application thanks to a series of programmes known as rootkit malware. These kernel rootkits use covert methods to conceal the kernel components, various control frameworks, and system activities, making identifying or prohibiting their presence in the target machine challenging. The bulk of rootkit detection and prevention techniques used today are particular to a system and dependent on recognized sources, making them ineffective for growing, evolving, concealed, or unnamed rootkits. This study proposes using the kernel rootkit prevention model using multiclass (KRPMM) system to identify hash values and detect/prevent such rootkits. The file downloaded by the client, who is availing of the service, is not permitted into the node used by the client in the cloud. But, it is redirected to the node wherein the file that has been downloaded and has entered the node anew is examined by a program which is specially coded to test the presence of rootkit in the file by some mechanisms and then comes to a conclusion of either the file being malicious or the file being clean and is free of rootkits. KRPMM tested only 64 rootkits.
Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model Kaliappan, Balamurugan; Sudalaiyadumperumal, Bakkialakshmi Vaithialingam; Thalavaipillai, Sudalaimuthu
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp201-209

Abstract

Investigating human subjects is the goal of predicting human emotions in the stock market. A significant number of psychological effects require (feelings) to be produced, directly releasing human emotions. The development of effect theory leads one to believe that one must be aware of one's sentiments and emotions to forecast one's behavior. The proposed line of inquiry focuses on developing a reliable model incorporating neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the body's physiological systems. This approach is named after the notion of Gaussian mixture models (GMM). The statistical reaction following data processing, quantitative findings on emotion labels, and coincidental responses with training samples all directly impact the outcomes that are accomplished. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated compared to a technique considered to be state-of-the-art. The proposed system determines an individual's emotional state after a minimum of 6 iterative learning using the Gaussian expectation-maximization (GEM) statistical model, in which the iterations tend to continue to zero error. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.