An enhanced detection system against routing attacks in mobile ad-hoc network

Prasad, Mahendra; Tripathi, Sachin; Dahal, Keshav

doi:10.1007/s11276-022-02913-1

Cited by 17 publications

(5 citation statements)

References 34 publications

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“…MANET is a promising technology that can provide important facilities for up-to-date transportation systems [1]. Due to its inherent nature, MANET is strongly vulnerable to miscellaneous security attacks [13,17,20]. Security attacks against MANET are divided into two categories according to their nature: active attacks and passive attacks [13,17,20].…”

Section: Related Workmentioning

confidence: 99%

“…For example, various intrusion detection methods have been developed to detect common network attacks, focusing on other routing attacks such as black hole attacks, Sybil attacks, identity replication attacks, selective forwarding attacks, wormhole attacks, and hello flood attacks [12]. Prasad et al investigated the detection method that can classify benign and malicious information in the MANET networks based only on routing attacks [13]. Ezhilarasi et al Introduced in 2022, a new intrusion detection system that uses fuzzy and feed-forward neural networks to detect only routing attacks in wireless sensor networks [12].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

Aurelle Tchagna Kouanou,

Theophile Fozin Fonzin,

Franck Mani Zanga

et al. 2023

Cloud Computing and Data Science

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This paper addresses the security concerns associated with Mobile Ad-hoc Networks (MANET) and proposes a new method for detecting and preventing attacks using machine learning. The study involved the creation of a MANET with 26 nodes in NetSim (Network Simulator) software, followed by the implementation of wormhole and blackhole attacks. A dataset was generated from the network traffic obtained during the simulations, and a machine-learning model was designed to predict and detect these attacks. The model achieved high sensitivity, accuracy and f1 scores of 99%. The effectiveness of the model was tested by developing a real-time application. This method can be applied to any wireless network and is particularly relevant for companies that use Ad-hoc networks for communication.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

Aurelle Tchagna Kouanou,

Theophile Fozin Fonzin,

Franck Mani Zanga

et al. 2023

Cloud Computing and Data Science

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“…Intrusion detection methods are validated on the KDD dataset, and experimental results of proposed methods are compared to existing machine learning techniques. Many machine learning techniques are applied for intrusion detection, that are SA-IDPS [32], MLP [33], C4.5, SVM, C4.5 + ACO, SVM + ACO, C4.5 + PSO, SVM + PSO, and EDADT [34]. Fig.…”

Section: Performance Analysis On Kdd Datasetmentioning

confidence: 99%

AI/ML driven intrusion detection framework for IoT-enabled cold storage monitoring system

Prasad

Pal

Tripathi

et al. 2022

Preprint

Self Cite

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An IoT-based monitoring system remotely controls and manages intelligent environments. Deployed sensors communicate themselves and transmit data over wireless communication. A sensor node (insider or outsider) is in the communication range, which can send data to other nodes. Due to such nature, it is more vulnerable to intrusions or attacks. Traditional (threshold based) is not powerful enough to detect intrusions, it shows low detection accuracy. An intrusion detection system is an efficient mechanism to detect malicious traffics and prevents abnormal activities. This paper suggests an intrusion detection framework for the cold storage monitoring system. In this, the temperature affects the environment and harms stored products. A malicious node work as a false data injection attack that manipulates temperature and forwards manipulated data, whereas a flooding attack disturbs the existing network. To handle these attacks, a dataset is generated and collected for training the machine learning techniques. Two machine learning techniques have applied as supervised learning (Bayesian Rough Set) and unsupervised learning (micro-clustering). These intrusion detection methods perform better and show high performance. Moreover, this work also provides the comparative analysis of the generated dataset to other IDS datasets.

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“…Mobile Ad-hoc Network (MANET) is a brand-new form of wireless communication that functions in a highly unpredictable and challenging environment [1,2] .…”

Section: Introductionmentioning

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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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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An enhanced detection system against routing attacks in mobile ad-hoc network

Abstract: An enhanced detection system against routing attacks in mobile ad-hoc network. Wireless Networks. This is a post-peer-review, pre-copyedit version of an article published in Wireless Networks on

Cited by 17 publications

References 34 publications

Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

AI/ML driven intrusion detection framework for IoT-enabled cold storage monitoring system

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

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