Abdelkader Ouda

Abdelkader Ouda

Western University

H-index: 13

North America-Canada

Abdelkader Ouda Information

University

Western University

Position

Western University - Canada

Citations(all)

936

Citations(since 2020)

451

Cited By

599

hIndex(all)

13

hIndex(since 2020)

11

i10Index(all)

19

i10Index(since 2020)

14

Email

University Profile Page

Western University

Top articles of Abdelkader Ouda

HSM4SSL: Leveraging HSMs for Enhanced Intra-Domain Security

In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, lacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about the safety and integrity of intra-domain environments, where all communication occurs within a single domain. As a result, this paper introduces HSM4SSL, a comprehensive solution designed to address the evolving challenges of secure data transmission in intra-domain environments. By leveraging hardware security modules (HSMs), HSM4SSL aims to utilize the Secure Socket Layer (SSL) protocol within intra-domain environments to ensure data confidentiality, authentication, and integrity. In addition, solutions proposed by academic researchers and in the industry have not addressed the issue in a holistic and integrative manner, as they only apply to specific types of environments or servers and do not utilize all cryptographic operations for robust security. Thus, HSM4SSL bridges this gap by offering a unified and comprehensive solution that includes certificate management, key management practices, and various security services. HSM4SSL comprises three layers to provide a standardized interaction between software applications and HSMs. A performance evaluation was conducted …

Authors

Yazan Aref,Abdelkader Ouda

Journal

Future Internet

Published Date

2024/4/26

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

This research article delves into the development of a reinforcement learning (RL)-based continuous authentication system utilizing behavioral biometrics for user identification on computing devices. Keystroke dynamics are employed to capture unique behavioral biometric signatures, while a reward-driven RL model is deployed to authenticate users throughout their sessions. The proposed system augments conventional authentication mechanisms, fortifying them with an additional layer of security to create a robust continuous authentication framework compatible with static authentication systems. The methodology entails training an RL model to discern atypical user typing patterns and identify potentially suspicious activities. Each user’s historical data are utilized to train an agent, which undergoes preprocessing to generate episodes for learning purposes. The environment involves the retrieval of observations, which are intentionally perturbed to facilitate learning of nonlinear behaviors. The observation vector encompasses both ongoing and summarized features. A binary and minimalist reward function is employed, with principal component analysis (PCA) utilized for encoding ongoing features, and the double deep Q-network (DDQN) algorithm implemented through a fully connected neural network serving as the policy net. Evaluation results showcase training accuracy and equal error rate (EER) ranging from 94.7% to 100% and 0 to 0.0126, respectively, while test accuracy and EER fall within the range of approximately 81.06% to 93.5% and 0.0323 to 0.11, respectively, for all users as encoder features increase in number. These …

Authors

Priya Bansal,Abdelkader Ouda

Journal

Computers

Published Date

2024/4

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates …

Authors

Iman IM Abu Sulayman,Peter Voege,Abdelkader Ouda

Journal

Information

Published Date

2024/3/6

Ensemble Learning to Enhance Continuous User Authentication For Real World Environments

Modern digital applications/systems need robust cybersecurity solutions. Traditional authentication methods like passwords, fingerprints, authorization cards, etc. authenticate the user at the beginning of the session but there is no validation during the session, which makes the system vulnerable. Continuous authentication is the solution to this challenge. In continuous authentication, keystroke data is used to extract the behavior patterns of the user. The data are then applied to train the machine learning (ML) classification algorithms to identify the unique behavioral patterns of each user and classify them accordingly. Thus, the performance of the ML classification algorithm is key in continuous user authentication, and it requires diverse and comprehensive data to be effective in the production environment. In many cases, the ML algorithm is trained on the datasets collected in a controlled lab environment and the …

Authors

Sanket Vilas Salunke,Abdelkader Ouda

Published Date

2023/7/4

Algorithm-based Data Generation (ADG) Engine for Data Analytics

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

Authors

Iman IM Abu Sulayman,Peter Voege,Abdelkader Ouda

Published Date

2023/6/19

An optimized approach to translate technical patents from english to japanese using machine translation models

This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific domain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and linguistic analysis. These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies.

Authors

Maimoonah Ahmed,Abdelkader Ouda,Mohamed Abusharkh,Sandeep Kohli,Khushwant Rai

Journal

Applied Sciences

Published Date

2023/6/14

Autonomous vehicle cyber-attacks classification framework

Autonomous Vehicles (AVs) are becoming more popular over the years. They are heavily loaded with sensors and many other modules to make real-time decisions and are connected to the internet to allow communication with other vehicles and the infrastructure. As with every internet-connected device, increased connectivity leads to a heightened level of cyber threats. As a result, AVs are being threatened with serious attacks. Hence, strict security measures need to be developed to preserve vehicular systems from those attacks. However, up to our knowledge, there have not been extensive efforts in proposing taxonomies to develop security measurements to mitigate AV attacks. Hence, the purpose of this paper is to classify cyber-attacks that exclusively target vehicular systems to give security researchers a good start to designing secure systems that mitigate those attacks.

Authors

Yazan Aref,Abdelkader Ouda

Published Date

2023/1/3

Still Computers Networking is Less Secure Than It should be, Causes and Solution

For the objective of securing communication channels among physical systems, various security approaches have been deployed that secure networking protocols. However, these approaches focused on securing protocols individually, and none have addressed the intra-domain communication issue in a holistic manner. This results in questioning the security of intra-domain environments, where all communication is exchanged within a single domain. These contentions rise from the fact that intra-domain communication had been burdened with a vexing absence of interest, by both academic and industry realms. To indicate the data transmission issue within organizations, this paper conducts a review of the applied security mechanisms that focus on securing individual networking protocols and their drawbacks, plus the implemented software-based and hardware-based solutions that secure communication …

Authors

Yazan Aref,Abdelkader Ouda

Published Date

2023/10/23

An Analysis of the Effects of Hyperparameters on the Performance of Simulated Autonomous Vehicles

Reinforcement learning (RL) is emerging as an effective technique to study autonomous vehicles (AVs) that are capable of navigating their surroundings safely and accurately. This is due to the fact that with RL, an agent can evaluate its surroundings and make appropriate decisions to maximize rewards without the need for human intervention. RL offers an alternative solution to complement Supervised learning solutions in the AV field and it offers some additional flexibility that makes it ideal to study the subject and test-focused solutions. To apply RL to AVs, we train the algorithm on a simulator, called AWS’s DeepRacer, first. The scope of this paper focuses on the hyperparameters of the algorithm and studies the performance of the model and how the hyperparameters affect it. As the need for autonomous vehicles increases to reduce traffic congestion and car crashes, it becomes significantly important to study …

Authors

Maimoonah Ahmed,Abdelkader Ouda,Mohamed Abusharkh

Published Date

2022/7/26

An Innovative Multi-Factor Authentication Approach

In a modern digital environment, there is a continual need for new and better authentication methods, particularly in the context of Multi-Factor Authentication systems. A new authentication framework has been proposed that leverages Big Data and Machine Learning to create a powerful new authentication method for Multi-Factor Authentication systems. In a previous paper, a design was proposed for a main component of the framework, a chatbot software that automatically generates authentication challenges and evaluates user responses, along with the necessary functionalities for the design to satisfy the framework. In this paper, we provide a detailed implementation plan for this design such that it will be able to complete this new authentication framework. In addition, in order to demonstrate the viability of this design, we conducted three experiments which each test a critical functionality in a controlled …

Authors

Peter Voege,Abdelkader Ouda

Published Date

2022/7/19

Comparison of machine learning techniques for activities of daily living classification with electromyographic data

Advances in data science and wearable robotic devices present an opportunity to improve rehabilitation outcomes. Some of these devices incorporate electromyography (EMG) electrodes that sense physiological patient activity, making it possible to develop rehabilitation systems able to assess the patient's progress when performing activities of daily living (ADLs). However, additional research is needed to improve the ability to interpret EMG signals. To address this issue, an off-line classification approach for the 26 upper-limb ADLs included in the KIN-MUS UJI dataset is presented in this paper. The ADLs were performed by 22 subjects, while seven EMG signals were recorded from their forearms. From variable-length EMG time windows, 18 features were computed, and 13 features more were extracted from frequency domain windows. The classification performance of five different machine learning techniques …

Authors

Sergio A Salinas,Mohamed Ahmed TA Elgalhud,Luke Tambakis,Sanket V Salunke,Kshitija Patel,Hamada Ghenniwa,Abdelkader Ouda,Kenneth McIsaac,Katarina Grolinger,Ana Luisa Trejos

Published Date

2022/7/25

Demystifying Wireless Technologies for Best Uses in IoT Echo-Systems

In this paper, a comprehensive comparison was conducted among a range of the latest wireless technologies including short-range, medium-range, and long-range. It is aiming to promote low power consumption and long-range wireless connectivity. This study covers two novel areas: first, it examines the most recent wireless communication technologies in terms of architecture, stack protocol, standard features, and security; and second, it analyzes the implementation challenges of each technology and its aptness for IoT echo-systems.

Authors

Wafaa Anani,Abdelkader Ouda

Published Date

2022/5/9

Wireless Meter Bus: Secure Remote Metering within the IoT Smart Grid

Smart metering and wireless communication technologies offer immense possibilities for scalable solutions to automated meter reading and energy production and distribution networks (smart grid). Around the world, researchers are working on methods for securing smart grid operations in general and smart metering in particular. Yet, their main focus remains on implementing and evaluating wireless communication technologies as an adequate solution to the smart grid. This paper proposes a security framework within the smart grid communication network. A new security profile called ‘W’ profile is introduced to cover all aspects of security protocols for Wireless M-Bus. Specifically, it aims to secure automated meter reading (AMR) using the Wireless M-Bus protocol within the Open Metering System (OMS) security standards in terms of authentication, integrity, and confidentiality.

Authors

Wafaa Anani,Abdelkader Ouda

Published Date

2022/7/19

Real-Time Data Generation and Anomaly Detection for Security User Profiles

Mostly, security user profiles are being generated from real-time sources of users’ data. User profiles generation process involves complicated and comprehensive data analysis mechanisms. Machine learning is, and become the most popular, technique among the researchers and the practitioners for this real-time data analysis. The scope of this paper is twofold: 1) to implement the anomaly detection module for real-time source of users’ data, and 2) to build real-time data generation engine to train and test this model and to provide the researchers an alternative or simulated methods for real-time data source. The data generation engine is powered by the Synthetic Data Vault (SDV) python library and is relaying on both the historical (past) and real-time (fresh) data. The purpose of using historical data generation is to increase the accuracy of anomaly detection model by expanding the user’s activity which will give …

Authors

Iman IM Abu Sulayman,Abdelkader Ouda

Published Date

2022/7/19

Transfer Learning for Behavioral Biometrics-based Continuous User Authentication

The cybersecurity industry is developing innovative solutions to avoid cyber-attacks. One such upcoming technology is continuous user authentication. It uses keystrokes and mouse movement behavioral patterns to authenticate the user continuously in the background. This technique uses machine learning to classify users based on the behavioral pattern. It requires a lot of data to find the user’s behavioral pattern and plenty of time is required to gather the data which extends the start of continuously authenticating the new user. In this research, the transfer learning technique was used for a feed-forward neural network model to overcome this issue for new users. Experiments were done using only one behavioral pattern with a set of 5 users to find the difference in accuracy between the model trained with transfer learning and the model trained without any previous learning. The results showed that the model …

Authors

Sanket Salunke,Abdelkader Ouda,Jonathan Gagne

Published Date

2022/7/19

Smart chatbot for user authentication

Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed or brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple different authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new authentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up to date information on the user from which authentication questions can be dynamically generated. This paper proposes a system that implements JitHDA, which we call Autonomous Inquiry-based Authentication Chatbot (AIAC). AIAC uses anomalous events gathered from a user’s recent activity to create personalized questions for the user to answer, and is designed to improve its own capabilities over time using neural networks trained on data gathered during authentication sessions. Due to using the user’s recent activity, they will be easy for the authentic user to answer and hard for a fraudulent user to guess, and as the user’s recent history updates between authentication sessions new questions will be dynamically generated to replace old ones. We intend to show in this paper that AIAC is a viable implementation of JitHDA.

Authors

Peter Voege,Iman IM Abu Sulayman,Abdelkader Ouda

Journal

Electronics

Published Date

2022/12/3

Study on integration of fastapi and machine learning for continuous authentication of behavioral biometrics

The traditional practices of security are failing slowly; new systems are needed to protect the information in the cyber world. The user authentication should be such that the systems are continuously learning and improving, and development should be fast paced without consuming too much time. The currently used continuous authentication systems have significant weaknesses in the huge data handling mechanisms including the run-time overhead taken in analyzing the user profiles. The objective of this work is to overcome these weaknesses to be able to handle multiple requests simultaneously, improve the overall performance, and decrease the cost of the behavioral biometrics-based authentication systems. In other words, we aim to create a machine learning algorithm to create user-profiles that are capable to user’s behavioral data of 64 bytes per second. The algorithm would provide over millions of user …

Authors

Priya Bansal,Abdelkader Ouda

Published Date

2022/7/19

LiFi/WiFi Authentication and Handover Protocols: Survey, Evaluation, and Recommendation

Current WiFi technology is not sufficient to meet the exponentially increasing demands for data bandwidth brought by the fast expansion of mobile communication and IoT services. Light fidelity (LiFi) and other means of visible light communication emerge as a promising solution that offers much higher data traffic than WiFi and is more robust against electromagnetic interference. However, there are security vulnerabilities in existing LiFi standards that impede its development. The anticipated LiFi-WiFi environment in the future also puts handover between the two technologies into focus. In this paper, we survey a wide range of protocols of LiFi systems and handover protocols of LiFi and WiFi hybrid networks. The security aspects of these protocols are examined closely and solutions for their exposed security flaws are proposed.

Authors

Iman IM Abu Sulayman,Rongji He,Marlin Manka,Andrew Ning,Abdelkader Ouda

Published Date

2021

A Study on Natural Language Chatbot-based Authentication Systems

The field of Natural Language Understanding has many powerful applications across a variety of domains. We seek to create a novel authentication system that utilizes Natural Language Understanding technologies to create a chatbot that asks dynamically-generated questions and compares the user’s response to the expected answer. We detail the necessary requirements for this system and examine recent developments in the fields of Natural Language Understanding and chatbot technologies to determine whether it can be implemented by any extant works. We then describe how this system can be expected to operate and what purpose it serves as an authentication technology.

Authors

Peter Voege,Abdelkader Ouda

Published Date

2021

Human trait analysis via machine learning techniques for user authentication

Machine learning is an extremely important technique that has become heavily used in different types of applications such as detection systems for fraud, intrusion or fault and monitoring systems for health or computer. Human trait analysis and identification is a field of research that needs a strong implementation for machine learning. Human trait analysis provides a tool with which human identification factors can be verified. Currently, detail aspects of human behavior are digitally and continuously logged in Big Data based platforms such as Twitter and Facebook. This continuous flow of high-volume data requires sophisticated data analysis to examine huge amounts of behavioral evidence so that human traits can be modeled. This paper proposes an innovative technique for human trait analysis that fits the needs for user's identity verification. The pioneering work of this technique is in the distinction of the normal …

Authors

Iman IM Abu Sulayman,Abdelkader Ouda

Published Date

2020/10/20

Designing security user profiles via anomaly detection for user authentication

The ability to detect the anomalous user behavior automatically and create user profiles, storing fresh and accurate security aspect user information, is important for systems administration, security, and development. This paper describes the best utilization of machine learning-based anomaly detection analysis, which is capable of distinguishing data that has security/identification potentials. Thereby, a novel technique for generating dynamic security user profiles is proposed. The real-time analytical outcomes of the anomaly detection methods are encapsulated into structured user profile records. These records store the sudden changing of the user's data, along with the real-time uniquely identifiable users' information. Each record is a unique entity describing a rear users' behavior, which have a substantial influence on user's identity verification. The verification process is in the form of user challenging questions …

Authors

Iman IM Abu Sulayman,Abdelkader Ouda

Published Date

2020/10/20

Abdelkader Ouda FAQs

What is Abdelkader Ouda's h-index at Western University?

The h-index of Abdelkader Ouda has been 11 since 2020 and 13 in total.

What are Abdelkader Ouda's top articles?

The articles with the titles of

HSM4SSL: Leveraging HSMs for Enhanced Intra-Domain Security

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

Ensemble Learning to Enhance Continuous User Authentication For Real World Environments

Algorithm-based Data Generation (ADG) Engine for Data Analytics

An optimized approach to translate technical patents from english to japanese using machine translation models

Autonomous vehicle cyber-attacks classification framework

Still Computers Networking is Less Secure Than It should be, Causes and Solution

...

are the top articles of Abdelkader Ouda at Western University.

What is Abdelkader Ouda's total number of citations?

Abdelkader Ouda has 936 citations in total.

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