Lightweight Multifactor Authentication Scheme for NextGen Cellular Networks

Khan, Adnan Shahid; Javed, Yasir; Saqib, Rashad Mahmood; Ahmad, Zeeshan; Abdullah, Johari; Zen, Karti­nah; Abbasi, Irshad Ahmed; Khan, Nayeem Ahmad

doi:10.1109/access.2022.3159686

Cited by 12 publications

(8 citation statements)

References 43 publications

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“…Hence, this research will focus on security issues on cell-free architecture for the ultra-dense environment, more specifically it will be dealing with mitigating communication attacks using multifactor authentication scheme assisted with blockchain technology. This research work is the extension of our previous works, where we proposed lightweight multifactor authentication scheme for cellular networks specifically 5G, and trust-based blockchain architecture for VANET, where we mitigate major communication attacks using blockchain technology [12], [13]. In this research work, we enhance our previous security schemes to address security issues in cell free 6G environment by integrating blockchain with lightweight multifactor authentication scheme [14].…”

Section: Introductionmentioning

confidence: 93%

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

et al. 2023

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Cell-Free mMIMO is a part of technology that will be integrated with future 6G ultradense cellular networks to ensure unlimited wireless connectivity and ubiquitous latency-sensitive services. Cell-Free gained researchers' interest as it offers ubiquitous communication with large bandwidth, high throughput, high data transmission, and greater signal gain. Cell-Free eliminates the idea of cell boundary in cellular communication that reduces frequent handover and inter-cell interference issues. However, the effectiveness of the current authentication protocol could become a serious issue due to the dynamic nature of Cell-Free in densely distributed, high number of users, high mobility, and frequent data exchange. Secondly, secure communication may be achieved in such a dynamic environment at the expense of high authentication overhead, high communication and computational costs. To address the above security challenges, we proposed a lightweight multifactor mutual authentication protocol for Cell-Free communication using ECC-based Deffie Hellman (ECDH). This scheme utilizes timestamping, one-way hash function, Blind-Fold Challenge scheme with public key infrastructure. The proposed cryptosystem integrates with blockchain technology using proof of staked (POS) as a consensus mechanism to ensure integrity, nonrepudiation and traceability. The proposed scheme can enforce the mitigation of several major security attacks on communication links such as spoofing attacks, eavesdropping, user location privacy issues, replay attacks, denial of service attacks, and man-in-the-middle (MITM) attacks, which is one of the significant features of the scheme. Furthermore, this scheme contributes to reducing authentication, communication, and computational overheads with an average of 32.8%, 52.4% and 53.2% better performance respectively as compared baseline authentication protocols.

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Section: Introductionmentioning

confidence: 93%

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

et al. 2023

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“…This makes it challenging for a mitigation method to differentiate between an attacker and a legitimate user. 18,[34][35][36] An SDN-based system may readily be subjected to a DoS attack by flooding the OpenFlow switch with packets having random destinations. In search of a new flow rule, every packet is now transmitted from the switch to the controller.…”

Section: Problem Statementmentioning

confidence: 99%

“…Network activity includes both of these examples. This makes it challenging for a mitigation method to differentiate between an attacker and a legitimate user 18,34–36 …”

Section: Introductionmentioning

confidence: 99%

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Nisa,

Khan,

Ahmad

et al. 2024

Int J Network Mgmt

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Software‐defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial‐of‐service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two‐Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K‐nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two‐Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.

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“…However, immense technological growth has resulted in a significant expansion of network size, interconnected devices, and the applications and the data handled, thereby has demanded an improvement in the current IDS systems, which have shown inefficiency in detecting new malicious security threats by monitoring the vast network traffic behavior. Hence resulting in a decline in the detection accuracy in detecting security attacks and a rise in the false alarm rate (FAR) and false negative rate (FNR) 12 …”

Section: Introductionmentioning

confidence: 99%

“…Hence resulting in a decline in the detection accuracy in detecting security attacks and a rise in the false alarm rate (FAR) and false negative rate (FNR). 12 Researchers have recently explored integrating IDS with artificial intelligence (AI) methods such as machine learning (ML) and deep learning (DL) to address these issues. ML and DL techniques are extremely powerful tools that have gained significant popularity over the last decade due to the invention of very powerful Graphics Processing Units (GPUs).…”

Section: Introductionmentioning

confidence: 99%

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

Ahmad

Khan

Zen

et al. 2023

Trans Emerging Tel Tech

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The innovative computing idea of Internet‐of‐Things (IoT) architecture has gained tremendous popularity over the last decade, resulting in an exponential increase in the connected devices and the data processed in the IoT networks. Since IoT devices collect a massive amount of sensitive information exchanged over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network‐based anomaly detection system can provide the much‐needed efficient security solution to the IoT network by detecting anomalies at the network entry points through constant traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false‐negative rate in classifying network traffic. To this end, this paper proposes an efficient Multistage Spectrogram image‐based network Anomaly Detection System (MS‐ADS) using a deep convolution neural network that utilizes a short‐time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re‐training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%.

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Lightweight Multifactor Authentication Scheme for NextGen Cellular Networks

Cited by 12 publications

References 43 publications

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

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