Sum up Work on Intrusion Detection System in Vehicular Ad-hoc Networks

Badukale, Pooja; Thorat, Samrat; Rojatkar, Dinesh V.

doi:10.1109/icoei51242.2021.9452961

Cited by 2 publications

(2 citation statements)

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“…In DTS 1,1 particular, anomaly-based network intrusion detection emerged as an important research and development direction of intrusion detection (Bhuyan et al, 2013;Yang et al, 2022). We also observed the emergence of specialized IDS in domains such as IoT (Tiwari & Narain, 2021), smart phones (Ahmad, Shah, & Al-Khasawneh, 2021), vehicles (Badukale, Thorat, & Rojatkar, 2021) and smart grids (Liu, Hagenmeyer, & Keller, 2021) just to mention a few.…”

Section: Related Workmentioning

confidence: 88%

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Enabling intrusion detection systems with dueling double deep Q-learning

Badr

2022

DTS

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PurposeIn this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.Design/methodology/approachThe methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).FindingsMachine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.Research limitations/implicationsAll machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.Practical implicationsThe study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.Originality/valueThe research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

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Section: Related Workmentioning

confidence: 88%

“…, 2022). We also observed the emergence of specialized IDS in domains such as IoT (Tiwari & Narain, 2021), smart phones (Ahmad, Shah, & Al-Khasawneh, 2021), vehicles (Badukale, Thorat, & Rojatkar, 2021) and smart grids (Liu, Hagenmeyer, & Keller, 2021) just to mention a few.…”

Section: Related Workmentioning

confidence: 93%