A survey on interest packet flooding attacks and its countermeasures in named data networking

Rabari, Jeet; Kumar, P. Arun Raj

doi:10.1007/s10207-022-00591-w

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…(1) Request flooding attack [24]: In this attack, an attacker floods the network with a large number of useless interest packets in a short amount of time, overwhelming the network and causing the cache table to be replaced constantly. This ultimately results in the route being unable to provide forwarding services to normal users.…”

Section: Threat Modelmentioning

confidence: 99%

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

Hu³

et al. 2023

Electronics

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The Named Data Network (NDN) enables efficient content dissemination through interest-based retrieval, name-based routing, and content caching. In the industrial Internet architecture based on NDN, device identity distribution, identification, resolution, and routing rely on identification resolution technology. However, this approach presents challenges such as cache poisoning, interest packet flood attacks, and black hole attacks. Existing security schemes primarily focused on routing forwarding and verification fail to address critical concerns, including routing environment credibility and data leakage, while exhibiting poor time and space efficiency. To address these challenges, this paper proposes a lightweight behavior auditing scheme using blockchain technology. The scheme utilizes an improved Bloom filter to compress behavioral information like interest and data packets during the identification transmission process. The compressed data are subsequently uploaded to a blockchain for auditing, achieving efficient space and time utilization while maintaining feasibility.

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Section: Threat Modelmentioning

confidence: 99%

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

Hu³

et al. 2023

Electronics

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“…Phishing perpetrators adeptly mimic legitimate websites, particularly those related to online banking and e-commerce. This creates a symmetrical illusion that induces users to unwittingly divulge sensitive information, leading to various fraudulent actions [5,6].…”

Section: Introductionmentioning

confidence: 99%

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Aslam,

Manzoor

et al. 2024

Symmetry

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The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

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Detection of Improved Collusive Interest Flooding Attacks Using BO-GBM Fusion Algorithm in NDN

Peng

Liu

2023

IEEE Trans. Netw. Sci. Eng.

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In Named Data Networking (NDN), Collusive Interest Flooding Attacks (CIFA) is a new type of Distributed Denial of Service (DDoS) attacks, which can effectively affect the performance of NDN by sending malicious Interests intermittently. Since the concealment of CIFA is strong, the existing detection methods for Interest Flooding Attacks (IFA) are difficult to find the malicious Interests in the NDN network. However, the subsequent attack strength of CIFA is weaker than that of IFA, resulting in the attack range of CIFA is much smaller than that of IFA in large network topologies. In order to launch the most serious attack with the least cost, the attack model of CIFA has been improved by our previous work, namely Improved Collusive Interest Flooding Attacks (I-CIFA). To better take the countermeasures against I-CIFA, this paper studies the adverse effects of I-CIFA in NDN and proposes a detection mechanism for I-CIFA. Foremost, we extract the corresponding network traffic and analyze the impact of I-CIFA on malicious routing nodes in different locations of the network. Furthermore, the detection mechanism based on BO-GBM fusion algorithm is proposed to detect I-CIFA through classifying the network traffic. Finally, several specific performance metrics are adopted to evaluate the practicability of BO-GBM fusion algorithm in detecting I-CIFA. The results show that BO-GBM fusion algorithm has better detection performance than other existing detection schemes, with the detection rate of 98.69%, false alarm rate of 1.36% and missing alarm rate of 1.43%.

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A survey on interest packet flooding attacks and its countermeasures in named data networking

Cited by 5 publications

References 56 publications

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

Lightweight Transmission Behavior Audit Scheme for NDN Industrial Internet Identity Resolution and Transmission Based on Blockchain

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Detection of Improved Collusive Interest Flooding Attacks Using BO-GBM Fusion Algorithm in NDN

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