A Game Theory based Attacker Defender Model for IDS in Cloud Security

Jain, Anuj; Tripathi, Khushboo; Jatain, Aman; Chaudhary, Manu

doi:10.23919/indiacom54597.2022.9763191

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DNN is also utilized in another threat detection model designed for secure cloud communications. In [13], a Game Theory Cloud Security Deep Neural Network (GT-CSDNN) is proposed and trained on the CIC-IDS2018 dataset. The proposed IDS has achieved higher accuracy in detecting various attacks.…”

Section: Related Studymentioning

confidence: 99%

See 1 more Smart Citation

An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment

Javeed

Saeed

Kumar

2024

IEEE Internet Things J.

View full text Add to dashboard Cite

The deployment of Internet of Things (IoT) systems in Smart Agriculture (SA) operates in extreme environments including wind, snowfall, flooding, landscape, and so on for collecting and processing real-time data. The increased connectivity and broad adoption of IoT devices with low-power communications on farmland support farmers in making data-driven decisions using various Artificial Intelligence (AI) techniques. Furthermore, in such an environment, edge computing is also utilized to provide computationally intensive, latency-sensitive, and bandwidth-demanding services at the edge of the network. However, protecting edge-to-Things in the extreme environment of SA is challenging, due to the volume of data, and also attackers exploit network gateways to perform Distributed Denial of Service (DDoS) attacks. Motivated by the aforementioned challenges, we develop a novel deep learning-based Intrusion Detection System (IDS) for edge-envisioned SA in extreme environments. Specifically, a hybrid approach is developed by combining bidirectional gated recurrent unit, long-short term memory with softmax classifier to detect attacks at the edge of the network. To allow faster learning, the proposed IDS employs the Truncated Backpropagation through Time (TBPTT) approach to handle lengthy sequences of network data. Furthermore, we suggest an attack scenario with deployment architecture for the proposed IDS in the extreme environment of SA. Extensive experiments using three publicly available datasets namely, CIC-IDS2018, ToN-IoT, and Edge-IIoTset prove the effectiveness of the proposed IDS over some traditional and contemporary stateof-the-art techniques.

show abstract

Section: Related Studymentioning

confidence: 99%

“…Finally, we made the performance comparison of the proposed IDS with recent threat detection approaches from the existing literature such that [19], [16], [13] and [20]. A complete comparison is depicted in Table VIII regarding ACC, PRE, and REC.…”

Section: E Performance Comparison With Recent Intrusion Detection App...mentioning

confidence: 99%

An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment

Javeed

Saeed

Kumar

2024

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…The Motion and Appearance DeepNet method proposed a mix of multiple one-class SVM methods with a DNN for anomaly identification in video data. A deep Gaussian mixture method was utilized to examine patterns of video events, and feature learning was accomplished utilizing PCANet for anomalous event identification using DL approaches [18,19]. Long Short-Term Memory and CNN methods were combined in a proposed hybrid neural network model to detect aberrant emotions in social media.…”

Section: Literature Reviewmentioning

confidence: 99%

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Annadurai

Nelson

Devi³

et al. 2022

Energies

View full text Add to dashboard Cite

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

show abstract

An Sdn-Based Hybrid-Dl-Driven Cognitive Intrusion Detection System for Iot Ecosystem

Shah,

Wahab,

Khan

et al. 2024

Preprint

View full text Add to dashboard Cite

A Game Theory based Attacker Defender Model for IDS in Cloud Security

Cited by 3 publications

References 20 publications

An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment

An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

An Sdn-Based Hybrid-Dl-Driven Cognitive Intrusion Detection System for Iot Ecosystem

Contact Info

Product

Resources

About