Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications

Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices

The prevalent use of the Internet of Things (IoT) devices over the Sea, such as, on oil and gas platforms, cargo, and cruise ships, requires high-speed connectivity of these devices. Although satellite based backhaul links provide vast coverage, but they are inherently constrained by low data rates and expensive bandwidth. If a signal propagated over the sea is trapped between the sea surface and the Evaporation Duct (ED) layer, it can propagate beyond the horizon, achieving long-range backhaul connectivity with minimal attenuation. This paper presents experimental measurements and simulations conducted in the Industrial, Scientific, and Medical (ISM) Band Wi-Fi frequencies, such as 5.8 GHz to provide hassle-free offshore wireless backhaul connectivity for IoT devices over the South China Sea in the Malaysian region. Real-time experimental measurements are recorded for 10 km to 80 km path lengths to determine average path loss values. The fade margin calculation for ED must accommodate additional slow fading on top of average path loss with respect to time and climate-induced ED height variations to ensure reliable communication links for IoT devices. Experimental results confirm that 99% link availability of is achievable with minimum 50 Mbps data rate and up to 60 km distance over the Sea to connect offshore IoT devices

A Detailed Testing Procedure of Numerical Differential Protection Relay for EHV Auto Transformer

Abstract: In power systems, the programmable numerical differential relays are widely used for the protection of generators, bus bars, transformers, shunt reactors, and transmission lines. Retrofitting of relays is the need of the hour because lack of proper testing techniques and misunderstanding of vital procedures may result in under performance of the overall protection system. Lack of relay’s proper testing provokes an unpredictability in its behavior, that may prompt tripping of a healthy power system. Therefore, the main contribution of the paper is to prepare a step-by-step comprehensive procedural guideline for practical implementation of relay testing procedures and a detailed insight analysis of relay’s settings for the protection of an Extra High Voltage (EHV) auto transformer. The experimental results are scrutinized to document a detailed theoretical and technical analysis. Moreover, the paper also covers shortcomings of existing literature by documenting specialized literature that covers all aspects of protection relays, i.e., from basics of electromechanical domain to the technicalities of the numerical differential relay covering its detailed testing from different reputed manufacturers. A secondary injection relay test set is used for detailed testing of differential relay under test, and the S1 Agile software is used for protection relay settings, configuration modification, and detailed analysis

FADING CHARACTERISTICS OF EVAPORATION DUCT FOR AVAILABILITY & CAPACITY OF THE WIRELESS COMMUNICATION LINK OVER THE SEA

High-Capacity and reliable communication over the sea surface has always been a challenge due to vast distances. Evaporation ducts (ED) caused by the rapid decrease in the refractive index of the lower atmosphere, are known to trap radio waves between the evaporation duct layer and the sea surface and can be utilized to mitigate disadvantages in various technologies for long-range maritime wireless communication system

CORPORATE GOVERNANCE AND CORPORATE SOCIAL RESPONSIBILITY: A STUDY ON TELECOMMUNICATION SECTOR OF PAKISTAN: Shamaila Gull, Khurram Shabih Zaidi, Ibtisam Butt

The corporate governance mechanisms in Pakistan are not given much importance to enhance the corporate social responsibility activities in business organizations. Organizations are expanding their boundaries and adopting new and innovative ways to show that they care for humanity. This article studies the influence of corporate governance on different corporate social responsibilities activities in Pakistani organizations. Five-point likert scale questionnaire was used to measure the impact of corporate governance on corporate social responsibility activities in the telecommunication sector of Pakistan. Regression analysis was done to test the relationship between corporate governance and corporate social responsibility, along with other correlational and descriptive statistical analysis. The results suggest that the corporate governance dimensions of discipline, social awareness, transparency, fairness and responsibility have a significant effect on corporate social responsibility

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications