Quantum secured 6G technology-based applications in Internet of Everything

Prateek, Kumar; Ojha, Nitish Kumar; Altaf, Fahiem; Maity, Soumyadev

doi:10.1007/s11235-022-00979-y

Cited by 19 publications

(6 citation statements)

References 162 publications

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“…While quantum vulnerabilities are beyond the scope of this paper, it's important to note that the advent of quantum computing presents new challenges in cybersecurity. With their potential to break traditional encryption methods, Quantum computers could make current security measures obsolete, introducing a new layer of complexity in protecting against intrusions, including those targeted in this research [59].…”

Section: A: Common Security Vulnerabilitiesmentioning

confidence: 99%

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Racherla,

Sripathi,

Faruqui

et al. 2024

IEEE Access

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The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation of today's smart services. However, the inherent resource-constrained nature of IoT nodes poses significant challenges in embedding advanced algorithms for cybersecurity, leading to an escalation in cyberattacks against these nodes. Contemporary research in Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, an innovative and practically deployable Deep Learning (DL)-based IDS. It employs a Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units and is trained on the CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS an ideal candidate for edge-server deployment, acting as a guardian between IoT nodes and the Internet against Denial of Service (DoS), Distributed Denial of Service (DDoS), Brute Force (BRF), Man-in-the-Middle (MITM), and Replay (RP) Attacks. A distinctive aspect of this research is the trade-off analysis between the intrusion detection rate and the false alarm rate, facilitating the real-time performance of the Deep-IDS. The system demonstrates an exemplary detection rate of 96.8% and an overall classification accuracy of 97.67%. Furthermore, Deep-IDS achieves precision, recall, and F1-scores of 97.67%, 98.17%, and 97.91%, respectively. On average, Deep-IDS requires 1.49 seconds to identify and mitigate intrusion attempts, effectively blocking malicious traffic sources. The remarkable efficacy, swift response time, innovative design, and novel defense strategy of Deep-IDS not only secure IoT nodes but also their interconnected subnetworks, thereby positioning Deep-IDS as a leading intrusion detection system for IoT-enhanced computer networks.

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Section: A: Common Security Vulnerabilitiesmentioning

confidence: 99%

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Racherla,

Sripathi,

Faruqui

et al. 2024

IEEE Access

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“…These techniques could provide secure communication and data protection, even in the face of sophisticated cyberattacks. 88 The purpose of the article is to provide a secure and efficient way to initiate, manage, and terminate multimedia sessions in big data communications, where large amounts of sensitive information are being transmitted and stored. By using a three-factor session initiation protocol, the risk of security breaches is reduced, and the privacy and security of the multimedia data is ensured.…”

Section: Security Threats In Fog Computingmentioning

confidence: 99%

“…The integration of quantum communication techniques with 6G technology could enhance the security and privacy of IoE applications, by leveraging the unique properties of quantum communication, such as QKD and quantum encryption. These techniques could provide secure communication and data protection, even in the face of sophisticated cyberattacks 88 . The purpose of the article is to provide a secure and efficient way to initiate, manage, and terminate multimedia sessions in big data communications, where large amounts of sensitive information are being transmitted and stored.…”

Section: Security Threats In Fog Computingmentioning

confidence: 99%

Fog computing security: A review

Jumani

Shi

Laghari

et al. 2023

Security and Privacy

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Fog computing, also known as edge computing, is a decentralized computing architecture that brings computing and data storage closer to the users and devices that need it. It offers several advantages over traditional cloud computing, such as lower latency, improved reliability, and enhanced security. As the Internet of Things continues to grow, the demand for fog computing is also increasing, making it an important topic for research and development. However, the deployment of fog computing also brings new technical challenges and security risks. For example, fog nodes are often deployed in resource‐constrained environments and are exposed to potential security threats, such as malware and attacks on devices connected to the network. In addition, the decentralized nature of fog computing creates new challenges in terms of privacy, security, and data management. This survey aims to address these technical challenges and research gaps in the field of fog computing security. It provides an overview of the current state of fog computing and its security challenges, and identifies key areas for future research. The survey also highlights the importance of fog computing security and the need for continued investment in this area in order to fully realize the potential of this promising technology.

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“…Rahman et al 9 proposed a Deep learning method in the Internet of Things to counter SQL Injection, malware, DNS poisoning, brute force attacks and XSS attacks. Prateek et al 10 discussed about quantum 6G technology based on IoT and IoV smart applications. Network architecture was proposed for secure and efficient Internet‐of‐Everything.…”

Section: Introductionmentioning

confidence: 99%

Analysis of SQL injection attacks in the cloud and in WEB applications

Kumar,

Dutta,

Pranav

2024

Security and Privacy

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Cloud computing has revolutionized the way IT industries work. Most modern‐day companies rely on cloud services to accomplish their day‐to‐day tasks. From hosting websites to developing platforms and storing resources, cloud computing has tremendous use in the modern information technology industry. Although an emerging technique, it has many security challenges. In structured query language injection attacks, the attacker modifies some parts of the user query to still sensitive user information. This type of attack is challenging to detect and prevent. In this article, we have reviewed 65 research articles that address the issue of its prevention and detection in cloud and Traditional Networks, of which 11 research articles are related to general cloud attacks, and the rest of the 54 research articles are specifically on web security. Our result shows that Random Forest has an accuracy of 99.8% and a Precision rate of 99.9%, and the worst‐performing model is Multi‐Layer Perceptron (MLP) in the SQLIA Model. For recall value, Random Forest performs best while TensorFlow Linear Classifier performs worst. F1 score is best in Random Forest, while MLP is the most diminutive performer.

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Quantum secured 6G technology-based applications in Internet of Everything

Cited by 19 publications

References 162 publications

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Fog computing security: A review

Analysis of SQL injection attacks in the cloud and in WEB applications

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