Search Results for author:
Found 17 papers, 2 papers with code
A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective
Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods.
Forensic Data Analytics for Anomaly Detection in Evolving Networks
In the prevailing convergence of traditional infrastructure-based deployment (i. e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective security controls to protect these deployments in-depth.
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities
This growth has led to the need for new communication technologies that will promote this transformation.
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection
Content delivery networks (CDNs) provide efficient content distribution over the Internet.
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks.
Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities
Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis.
Relationship between Student Engagement and Performance in e-Learning Environment Using Association Rules
The field of e-learning has emerged as a topic of interest in academia due to the increased ease of accessing the Internet using using smart-phones and wireless devices.
DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach
Moreover, the observed trends are validated by studying the same features in an unlabeled dataset using K-means clustering algorithm and through applying the developed ensemble learning classifier.
Optimized Random Forest Model for Botnet Detection Based on DNS Queries
To that end, this paper proposes a novel optimized ML-based framework to detect botnets based on their corresponding DNS queries.
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach
Experimental results show that the proposed optimized framework has a high detection accuracy, precision, recall, and F-score, highlighting its effectiveness and robustness for the detection of botnet attacks in IoT environments.
Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing
Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it.
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection
Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities.
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining
Furthermore, this work aims to predict the students' performance at two stages of course delivery (20% and 50% respectively).
Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection
This paper builds on our previous work in [1], which only proposed majority-voting based classifier, by proposing an ensemble-based feature selection and bagging classification model to detect DNS typo-squatting attack.
Softwarization, Virtualization, & Machine Learning For Intelligent & Effective V2X Communications
Concepts such as softwarization, virtualization, and machine learning will be key components as innovative and flexible enablers of such networks.
Systematic Ensemble Model Selection Approach for Educational Data Mining
A plethora of research has been done in the past focusing on predicting student's performance in order to support their development.
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks.
