Faheem Masoodi

University of Kashmir, Srinagar, J&K, India, Post Graduate Department of Computer Sciences, Faculty Member

Aligarh Muslim University, Computer Science, Graduate Student

Followers

277

Following

Co-authors

Public Views

John F Dooley

Knox College

Sherman S.M. Chow

The Chinese University of Hong Kong

Alberto Trombetta

Università degli Studi dell'Insubria

Jordi Herrera-Joancomartí

Universitat Autònoma de Barcelona

Arash Habibi Lashkari

York University

Paul Tobin

Dublin Institute of Technology

Roshan Chitrakar

Nepal College of Information Technology

Jaydip Sen

Praxis Business School

Dr. Shadab Alam

Jazan University

Adnan Gutub

Umm Al-Qura University, Makkah, Saudi Arabia

Interests

Uploads

Papers by Faheem Masoodi

Managing and Securing Information Storage in the Internet of Things

Auerbach Publications eBooks, Jun 14, 2024

Developing an innovative Imperfect Debugging Software Reliability Growth Model with enhanced Testing Coverage strategies

International journal of reliability, quality, and safety engineering/International journal of reliability, quality and safety engineering, Apr 25, 2024

From data to insights

Elsevier eBooks, 2024

System Reliability and Security

Auerbach Publications eBooks, Nov 5, 2023

Software Security

Auerbach Publications eBooks, Nov 5, 2023

Early detection of type 2 diabetes mellitus using machine learning-based prediction models

Scientific Reports, 2020

Most screening tests for T2DM in use today were developed using multivariate regression methods t... more Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highe...

Download

Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach

Frontiers in agronomy, Feb 15, 2024

The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such a... more The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such as scarce water resources, the elevated salinity and alkalinity of irrigation water, inefficient irrigation practices, and inter-sectoral competition for water resources. These challenges have led to diminishing agricultural yields and abandonment of arable lands. Internet of Things (IoT)-based irrigation systems present a promising remedy for these issues. By curbing water wastage and ensuring precise water delivery to crops, IoT-based irrigation systems offer a viable solution to the challenges entrenched in traditional irrigation methodologies in KSA. However, the widespread implementation of an IoT-based Smart Irrigation System (I-SIMS) poses a multifaceted and intricate challenge in KSA. This study is focused on the identification of the factors and challenges through a systematic review and ranking of the challenges/factors that exert a significant influence on the adoption of I-SIMS. Ranking aids in determining the importance of various alternatives. It enables locating the best options that support the required objectives in complex decision situations. The study employs both Grey Relational Analysis (GRA) and Analytical Hierarchical Process (AHP) methodologies to prioritize these factors. The study's conclusive findings indicate that among the challenges, technical expertise and security measures emerge as the foremost concerns that demand attention.

Download

Prediction of Erythemato-Squamous Diseases Using Machine Learning

Auerbach Publications eBooks, Feb 8, 2023

HIBE: Hierarchical Identity-Based Encryption

Springer eBooks, 2021

Effective Intrusion Detection in IoT Environment: Deep Learning Approach

Soft Computing Research Society eBooks, 2021

IoT is among the most important technologies in recent times, which has evolved for the good of h... more IoT is among the most important technologies in recent times, which has evolved for the good of human beings. It reduces the human efforts in every domain possibly by connecting things, making people work and live smarter. The use of smart devices in a variety of applications has helped pave the way for pervasive computing, which offers huge human, economic, and other advantages. However, these advantages come with a number of drawbacks that must be addressed, one of which is security.The issue addressed in this research will be an effective Intrusion Detection System deployed in the Internet of Things environment. Though, various Intrusion detection systems are available, some are using learning methods. However, they face challenges such as a lack of relevant data, with some relying on the KDD dataset, which isn't designed specifically for IoT. This paper presents Deep Learning-based techniques, such as DNN and LSTM-RNN classifier, for detecting classes of assaults and anomalies in a simulated smart environment and classifying them as abnormal or normal using the BoT-IoT dataset. The proposal framework was created with the Google Colab platform. The experimental results for all the attack classes using DNN and LSTM-RNN classifiers have achieved 99.7% and 99.8% accuracies respectively.

Download

Machine Learning for Classification analysis of Intrusion Detection on NSL-KDD Dataset

Turkish Journal of Computer and Mathematics Education (TURCOMAT), Apr 28, 2021

In the existing digital era, security concerns turn out to be a prime obstacle as it hampers the ... more In the existing digital era, security concerns turn out to be a prime obstacle as it hampers the user's privacy. Moreover, with the emergence of new technologies, enormous amount of data is present on the network which is subjected to innumerable malicious attacks and security vulnerabilities. It is therefore, essential to detect these vulnerabilities on time so that the privacy of the user's data is not hampered and for the same intrusion detection system (IDS) is used which is deemed as the cornerstone of security. An IDS is indispensable for timely detection of cyber-attacks as it is capable of detecting intrusive activities adequately so that any potential harm to the system resources and the user base can be avoided in time. Owing to their understanding of IDS for reducing security threats, in the current work, machine learning classifiers (MLC) were used for classifying the data. The system performance was evaluated using four diverse attribute subsets obtained from NSL-KDD dataset. For optimization, prior to the training/testing phase, the dataset was pre-processed so that irrelevant features could be removed as their contribution is inconsequential in detecting attack classes. Finally, the overall model accuracy for different attack classes namely DoS, Probe, U2L, and R2L was compared to detect the most suitable algorithm for a particular attack class

Download

Internet of Things: Financial Perspective and Associated Security Concerns

International journal of computer theory and engineering, 2020

The internet of things (IoT) has grown at a significant pace and has revolutionized the whole tec... more The internet of things (IoT) has grown at a significant pace and has revolutionized the whole technological aspects of internet and things. In the paper around 800 IoT companies headquartered in various countries globally were analyzed. It was observed that the financial returns of select IoT companies for past five years have witnessed a decent growth however; the industry witnessed a slight dip in the year 2016. Appropriate privacy and security measures need to be in place to effectively secure the communications and storage of huge amounts of data generated.

Download

Symmetric Algorithms I

Chapman and Hall/CRC eBooks, May 20, 2019

Applications of Machine Learning and Deep Learning on Biological Data

Auerbach Publications eBooks, Feb 8, 2023

Blockchain in Healthcare: Challenges and Opportunities

Social Science Research Network, 2021

The significance of an agile and widespread healthcare system was evident from the recent pandemi... more The significance of an agile and widespread healthcare system was evident from the recent pandemic, Covid-19. Healthcare encompasses different stakeholders and many domains that include pharmaceutical supply chain management (SCM), electronic medical records, patient histories, clinical trial results, imaging and scans, insurance claim records, and doctors' information as well. Currently, there are many domains in healthcare with challenges and issues where improvements are due. Blockchain is the single most imperative technology that could be integrated with healthcare to enhance the capabilities in every aspect of the trivial system. In this paper, the benefits of integrating blockchain with healthcare shall be discussed. A discussion of the challenges and possible solutions in healthcare using blockchain shall also be given.

Security Concerns and Privacy Preservation in Blockchain based IoT Systems: Opportunities and Challenges

Social Science Research Network, Jan 18, 2021

Ensemble based Effective Intrusion Detection System for Cloud Environment over UNSW-NB15 Dataset

Soft Computing Research Society eBooks, 2021

Advanced computing innovations are rapidly evolving, resulting in the advent of new organizationa... more Advanced computing innovations are rapidly evolving, resulting in the advent of new organizational and operational strategies. Cloud computing has emerged as one of the pre-eminent innovation in the recent years. Cloud computing enables its clients to access flexible, distributed computing domain via internet. Cloud has manifested itself as a viable framework that facilitates the use of application domains, data and infrastructural facilities that mainly encompasses workstations, network and storage infrastructure. Regardless of robust and comprehensive server processing capabilities in contrast to client's processing capabilities and efficiency there are numerous security risks to the cloud from both outside and within the cloud that might exploit security flaws to cause damage. Traditional security measures have some flaws when it comes to completely shielding the networks and devices from increasingly advanced attacks. Consequently, it is all important to build an intrusion detection system to detect and prevent all kinds of intrusions in the cloud with high accuracy along with low false alarms. In this study we have suggested an anomaly-based intrusion detection system that employs ML algorithms for detection of unknown malicious attacks using an ensemble approach over the UNSW-NB15 dataset. The experimental output demonstrated the accuracy of 99.28% and 99.47% for random forest and bagging algorithms respectively.

Download

Security & Privacy Threats, Attacks and Countermeasures in Internet of Things

International journal of network security and applications, Mar 31, 2019

The idea to connect everything to anything and at any point of time is what vaguely defines the c... more The idea to connect everything to anything and at any point of time is what vaguely defines the concept of the Internet of Things (IoT). The IoT is not only about providing connectivity but also facilitating interaction among these connected things. Though the term IoT was introduced in 1999 but has drawn significant attention during the past few years, the pace at which new devices are being integrated into the system will profoundly impact the world in a good way but also poses some severe queries about security and privacy. IoT in its current form is susceptible to a multitudinous set of attacks. One of the most significant concerns of IoT is to provide security assurance for the data exchange because data is vulnerable to some attacks by the attackers at each layer of IoT. The IoT has a layered structure where each layer provides a service. The security needs vary from layer to layer as each layer serves a different purpose. This paper aims to analyze the various security and privacy threats related to IoT. Some attacks have been discussed along with some existing and proposed countermeasures.

Download

An Effective Intrusion Detection System using Supervised Machine Learning Techniques

With the increased use of Internet resources, cyber attackers are using novel ways to attack the ... more With the increased use of Internet resources, cyber attackers are using novel ways to attack the services of network. Thus network security is becoming inevitable part of the network system. In order to detect such attacks efficiently and effectively, robust IDS (Intrusion Detection System) is needed. An IDS is a tool that analyzes each and every packet deeply to detects malicious activity by monitoring a network or a system. The main purpose of IDS is to identify unwanted or abnormal action and to inform the network administrator about such actions. Thus, IDS is important tool for the network administrator to prevent the network from both known and unknown attacks that make the network resources more vulnerable. Machine learning methods can be used to employ efficient intrusion detection system (IDS). In this research work four machine learning methods were used namely RF (Random Forest), DT (Decision Tree), MLP (Multilayer perceptron) and SVM (Support Vector Machine) for classification of the data. NSL-KDD dataset was used for training and testing these various machine learning models. Feature selections were used to eliminate the irrelevant and unwanted features from the dataset. Therefore feature selection reduces the dimensionality of the dataset which in turn reduces the computational complexity. The proposed model’s output was evaluated using three feature subsets, randomly selected from the NSL-KDD dataset. The proposed method has a classification accuracy of more than 99 percent.

A robust ECC-based authentication framework for energy internet (EI)-based vehicle to grid communication system

Vehicular Communications, Jun 1, 2023

Managing and Securing Information Storage in the Internet of Things

Auerbach Publications eBooks, Jun 14, 2024

Developing an innovative Imperfect Debugging Software Reliability Growth Model with enhanced Testing Coverage strategies

International journal of reliability, quality, and safety engineering/International journal of reliability, quality and safety engineering, Apr 25, 2024

From data to insights

Elsevier eBooks, 2024

System Reliability and Security

Auerbach Publications eBooks, Nov 5, 2023

Software Security

Auerbach Publications eBooks, Nov 5, 2023

Early detection of type 2 diabetes mellitus using machine learning-based prediction models

Scientific Reports, 2020

Download

Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach

Frontiers in agronomy, Feb 15, 2024

Download