Intrusion Detection in Smart IoT Devices for People with Disabilities

Naveed, Muhammad; Usman, Syed Muhammad; Satti, Muhammad Islam; Aleshaiker, Sama; Anwar, Aamir

doi:10.1109/isc255366.2022.9921991

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a new version has been launched, the system updates itself to match the major versions. CRUD testing comprises Create, Read, Update, and Delete tests, which are conducted because it is the user who performs all these functions in the account creation and data sharing process [35,36]. Life-cycle testing means actually testing the mobile device in action.…”

Section: Mobile-level Testingmentioning

confidence: 99%

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

Aljaedi,

Siddique,

Satti

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for ensuring software quality in terms of both functional and non-functional aspects, testing within this intricate software–hardware ecosystem has been somewhat overlooked. To address this, various testing techniques are applied for real-time minimization of failure rates in IoT applications. However, the execution of a comprehensive test suite for specific IoT software remains a complex undertaking. This paper proposes a holistic framework aimed at aiding quality assurance engineers in delineating essential testing methods across different testing levels within the IoT. This delineation is crucial for effective quality assurance, ultimately reducing failure rates in real-time scenarios. Furthermore, the paper offers a mapping of these identified tests to each layer within the layered framework of the IoT. This comprehensive approach seeks to enhance the reliability and performance of IoT-based applications.

show abstract

Section: Mobile-level Testingmentioning

confidence: 99%

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

Aljaedi,

Siddique,

Satti

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

AI Driven IoT Healthcare Devices Security Vulnerability Management

Bajpayi,

Sharma,

Gaur

2024

2024 2nd International Conference on Disruptive Technologies (ICDT)

View full text Add to dashboard Cite

Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment

et al. 2022

View full text Add to dashboard Cite

The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models.

show abstract

Intrusion Detection in Smart IoT Devices for People with Disabilities

Cited by 3 publications

References 29 publications

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

AI Driven IoT Healthcare Devices Security Vulnerability Management

Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment

Contact Info

Product

Resources

About