Optimized neural network model for attack detection in LTE network

Jyothi, K. Krishna; Chaudhari, Shilpa

doi:10.1016/j.compeleceng.2020.106879

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…The adopted DDoS attack detection and mitigation process in SDN with the DNN + SIMFO model was implemented in PYTHON and the outcomes were verified. The performance of the adopted DNN + SIMFO model with attack detection in IoT was computed over the conventional schemes such as DNN + RHSO, 52 DNN + SSOA, 53 DNN + HHO, 54 DNN + MFO, 45 DDAML, 36 CNN, 55 NN, 56 and SVM, 57 correspondingly. Furthermore, the performance of both adopted and traditional models was computed by varying the learning rate to 60, 70, and 80 based on the different performance measures, including “accuracy, sensitivity, specificity, precision, FDR, FNR, FPR, NPV, F‐Measure, Recall and MCC”, respectively.…”

Section: Resultsmentioning

confidence: 99%

Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

Perumal

Karmel

2023

Concurrency and Computation

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The software-defined network (SDN) is a new network design with an operating system that allows better network quality control. The controller's primary role in an SDN network is to divide the control and forwarding planes in order to provide essential network power. The backup controller in an SDN may confront a variety of obstacles during a DDoS attack. It disrupts the flow of the network by attacking the service nodes hence obstructing the legitimate users from getting service. To overcome these issues, this work introduces a DDoS attack detection model that will aid in removing the attacker from the network. Initially, the input data is sent into the detection phase, which identifies the existence and kind of attacks. The presence of an attack is determined based on the data flow, and statistical features. With the reference of the extracted features, the optimized deep neural network (DNN) will decide the existence of attacker in the network. To make the decision more precise, the training of DNN will be carried out by self-improved moth flame optimization (SIMFO) Algorithm via tuning the optimal weights. Once an attacker's presence has been identified, it is critical to remove the attacker's node from the network.

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Section: Resultsmentioning

confidence: 99%

Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

Perumal

Karmel

2023

Concurrency and Computation

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“…While encryption is essential for maintaining data confidentiality, traditional encryption algorithms may introduce significant overhead, particularly in resource-constrained environments. To address this challenge, advanced encryption methods that prioritize lightweight and efficient cryptographic operations are recommended [13].…”

Section: Advanced Encryption Methodsmentioning

confidence: 99%

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

Bhavsingh,

Samunnisa,

Mallareddy

2024

Emerg. Technol. Eng. J.

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This study presents a new strategy to improve security and efficiency in Machine-Type Communication (MTC) networks, addressing the drawbacks of the existing Adaptive Hierarchical Group-based Mutual Authentication and Key Agreement (AHGMAKA) protocol. The AHGMAKA protocol, crucial for securing communication within groups of devices with limited resources, has been found to cause significant operational delays and inefficiencies. Our proposed solution integrates advanced cryptographic methods, including an optimized Authenticated Message Authentication Code (AMAC) and lightweight encryption, sophisticated optimization algorithms for dynamic grouping, and an efficient, lightweight group management protocol. It also introduces adaptive network management strategies to customize performance according to the needs of MTC networks. The effectiveness of this approach has been validated through empirical analysis, showing considerable improvements in operational performance and energy efficiency. These improvements mark a significant step toward achieving an optimal balance between efficiency and security for MTC networks. However, the research acknowledges ongoing challenges, including the trade-off between security and efficiency and the issue of compatibility with older devices, suggesting these as areas for future study. The paper outlines potential research paths, including using machine learning for better group management, adopting post-quantum cryptographic methods, applying hardware acceleration, and pushing to standardize these technologies. This work significantly advances the field of secure and efficient communication in MTC, a critical component of the growing Internet of Things (IoT) landscape, setting the stage for future breakthroughs.

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“…In case of an attack, the proposed model imposes a penalty and restricts the affected nodes from participating in MTC communication. Specifically, the improved NN was created using a new optimisation algorithm called Whale with Three-Level Update (WTU) (Jyothi and Chaudhari, 2020).…”

Section: Related Workmentioning

confidence: 99%

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

ÇAKMAK

2023

jista

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In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

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Optimized neural network model for attack detection in LTE network

Cited by 10 publications

References 26 publications

Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

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