Proposed neural intrusion detection system to detect denial of service attacks in MANETs

Hadi, Raghad Mohammed; Abdullah, Salma; Abedi, Wafaa M. Salih

doi:10.21533/pen.v10i3.2997

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Without feature optimization, it would the results of the proposed IDS framework is highly improved over the other models [48][49][50][51] . Consequently, the detection rate of the existing [28] and proposed AMOA-DSLC models are validated and compared using Dataset 1 as shown in Fig. 5 and Table 3.…”

Section: Resultsmentioning

confidence: 99%

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Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Sheela,

Soundari,

Mudigonda

et al. 2024

Intell. and Converged Netw.

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Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network's regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the minmax normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

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Section: Resultsmentioning

confidence: 99%

“…Prasad et al [28] implemented a clustering based unsupervised learning model for spotting intrusions in MANET. The suggested centroid initialization based clustering method outperforms basic clustering in terms of accuracy and takes fewer iterations to form final clusters.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Sheela,

Soundari,

Mudigonda

et al. 2024

Intell. and Converged Netw.

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“…First, since there isn't a single, reliable supervisor node in MANETs, attack signatures need to be managed in a decentralized fashion [11].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Attack Detection using DL based Feature Selection with Improved Convolutional Neural Network

Krishnan

Hemamalini²,

Cheraku

et al. 2023

IJEER

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Decentralized wireless networks that may connect without a central hub are named Mobile Ad-hoc Networks (MANET). Attacks and threats of the most common kind can easily penetrate MANETs. Malware, APTs, and Distributed Denial of Service (DDoS) assaults all work together to make Internet services less reliable and less secure. Existing methods have been created to counter these assaults, but they either need more hardware, result in significant delivery delays, or fall short in other key areas like as energy consumption. This research therefore provides an intelligent agent system that can automatically choose and classify features to identify DDoS assaults. In this study, we provide an automated attack detector for MANETs based on a multilayer, (1D) convolutional neural network (CNN). Grey relational analysis classifiers are employed to screen attack levels in the classification layer because of their simple mathematical operation. The sunflower optimization technique is also used to fine-tune the classifier's weight. The research suggested a supervised feature classifier and fed the compressed data from an unsupervised auto encoder to it. In our experiment, conducted on the custom-generated dataset CICDDoS2018, the system outperformed state-of-the-art deep learning-based DDoS attack finding methods by a factor of 98%. Our suggested technique utilizes the freshest CICDDoS2018 dataset in combination with automated feature selection and classification to achieve state-of-the-art detection accuracy at a fraction of the processing time.

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Security boundaries of mobile adhoc network: A systematic literature review and future aspects

Brar,

Singh,

Singh

2024

AIP Conference Proceedings

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Proposed neural intrusion detection system to detect denial of service attacks in MANETs

Cited by 5 publications

References 9 publications

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Attack Detection using DL based Feature Selection with Improved Convolutional Neural Network

Security boundaries of mobile adhoc network: A systematic literature review and future aspects

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