A Network Intrusion Detection Approach Using Extreme Gradient Boosting with Max-Depth Optimization and Feature Selection

Network intrusion detection system (NIDS) has become a vital tool to protect information anddetect attacks in computer networks. The performance of NIDSs can be evaluated by the numberof detected attacks and false alarm rates. Machine learning (ML) methods are commonly usedfor developing intrusion detection systems and combating the rapid evolution in the pattern ofattacks. Although there are several methods proposed in the state-of-the-art, the development ofthe most effective method is still of research interest and needs to be developed. In this paper,we develop an optimized approach using an extreme gradient boosting (XGB) classifier withcorrelation-based feature selection for accurate intrusion detection systems. We adopt the XGBclassifier in the proposed approach because it can bring down both variance and bias and hasseveral advantages such as parallelization, regularization, sparsity awareness hardware optimization,and tree pruning. The XGB uses the max-depth parameter as a specified criterion toprune the trees and improve the performance significantly. The proposed approach selects thebest value of the max-depth parameter through an exhaustive search optimization algorithm.We evaluate the approach on the UNSW-NB15 dataset that imitates the modern-day attacks ofnetwork traffic. The experimental results show the ability of the proposed approach to classifyingthe type of attacks and normal traffic with high accuracy results compared with the currentstate-of-the-art work on the same dataset with the same partitioning ratio of the test set

Trusted and secure communication system using intelligent devices for individuals with disabilities

Disability has traditionally posed a significant barrier for millions of individuals who are unable to benefit from current technologies such as digital interfaces, the internet, and personal computers. Nowadays, developments in information communication and technology (ICT) are transforming the way we handle and interact with our environment, making it easier and more convenient. However, individuals with disabilities face numerous restrictions and challenges, hindering their full involvement and access to the devices. This paper proposes an efficient and trusted communication mechanism that utilizes knowledge factors and friendship similarity schemes to improve the accessibility and accuracy of information transmission in a networked environment for disabled people. The proposed mechanism ensures the involvement of trusted devices in the network for accuracy and immediate decisions for disabled individual. The proposed approach is validated and compared to existing methods using various security metrics, including social trust, objective trust, experienced trust, and recommended trust. Through this research, we aim to address the barriers faced by individuals with disabilities and provide them with equal opportunities to benefit from modern technological advancements

Accessibility and ensured quality of life for disabled people using trusted edge computing

Now a days, the revolution or advancements in Information Communication Technology (ICT) are changing the way of handling the situations in the present environment. In our society, several people are present who live with physical difficulties and can not complete their basic household tasks for their own. The disabled person is all alone at home and caring the house where intermediator may gain the benefit of the opportunity. Though the modern era is revolutionizing the whole communication and interaction process among environment and human. The disabled person who cannot walk, and is deaf completely unable to hear, run or handle the current situation, the automatic systems or alarm may be compromised by the intruder and can access the house with the mean of steal. The aim of this paper is to propose a secure and competent communication mechanism for AI and edge-computing based home automation in case of disabled persons handling the situation. The proposed mechanism integrates the distrust-based recommendation system along with Social Contact Similarity mechanism that improves the efficiency and quick action decision corresponding to the altered device that may cause severe harm at the edge-level or to the person. The proposed mechanism is simulated and substantiated against various existing scheme over several performance metrics such as delay, alteration rate, accuracy and response time