Deep Echo State Networks for Detecting Internet Worm and Ransomware Attacks

Sharma, Tarun; Patni, Khushi; Li, Zhida; Trajković, Ljiljana

doi:10.1109/iscas46773.2023.10182056

2023 IEEE International Symposium on Circuits and Systems (ISCAS) 2023

DOI: 10.1109/iscas46773.2023.10182056

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Deep Echo State Networks for Detecting Internet Worm and Ransomware Attacks

Tarun Sharma¹,

Khushi Patni

Zhida Li

et al.

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Cited by 3 publications

References 37 publications

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E-SDNN: encoder-stacked deep neural networks for DDOS attack detection

Benmohamed,

Thaljaoui,

Elkhediri

et al. 2024

Neural Comput & Applic

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E-SDNN: encoder-stacked deep neural networks for DDOS attack detection

Benmohamed,

Thaljaoui,

Elkhediri

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

The types of hackers and cyberattacks in the aviation industry

Florido-Benítez

2024

J Transp Secur

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The main goal of this study is to analyse the types of hackers and cyberattacks in the aviation industry, to enhance cybersecurity in the air sector. This manuscript has identified 12 different typologies of hackers in the aviation context. First, those hackers who exercise responsibility in proper, effective, ethical, and good practices to improve the safety of citizens and organizations, such as white unicorns, red, blue, green, and nation sponsored hackers. And second, those hackers that are developing and using cyberattacks with bad practices to provoke serious material damage to public and private organizations, consumers, or even terrorist acts to kill people, including black, nation-state, cyberterrorist, whistle-blower, hacktivist, script kiddie, and gray hackers. Furthermore, findings reveal 54 cyberattacks documented in the period analysed (2000 – January 2024). Of the total cyberattacks in the period analysed, 35 were perpetrated at airports (65%) and 19 by airlines (35%). This study also suggests some lines of action to ensure and guarantee the security of data and private information for business-to-consumer (B2C) and business-to-business (B2B) and their transactions in the aviation industry.

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Enhancing Ransomware Attack Detection Using Transfer Learning and Deep Learning Ensemble Models on Cloud-Encrypted Data

Singh,

Mushtaq,

Abosaq

et al. 2023

Electronics

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Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue of ransomware attack classification. RANSOMNET+ excels over other models because it combines the greatest features of both architectures, allowing it to capture hierarchical features and local patterns. Our findings demonstrate the exceptional capabilities of RANSOMNET+. The model had a fantastic precision of 99.5%, recall of 98.5%, and F1 score of 97.64%, and attained a training accuracy of 99.6% and a testing accuracy of 99.1%. The loss values for RANSOMNET+ were impressively low, ranging from 0.0003 to 0.0035 throughout training and testing. We tested our model against the industry standard, ResNet 50, as well as the state-of-the-art, VGG 16. RANSOMNET+ excelled over the other two models in terms of F1 score, accuracy, precision, and recall. The algorithm’s decision-making process was also illuminated by RANSOMNET+’s interpretability analysis and graphical representations. The model’s openness and usefulness were improved by the incorporation of feature distributions, outlier detection, and feature importance analysis. Finally, RANSOMNET+ is a huge improvement in cloud safety and ransomware research. As a result of its unrivaled accuracy and resilience, it provides a formidable line of defense against ransomware attacks on cloud-encrypted data, keeping sensitive information secure and ensuring the reliability of cloud-stored data. Cybersecurity professionals and cloud service providers now have a reliable tool to combat ransomware threats thanks to this research.

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Deep Echo State Networks for Detecting Internet Worm and Ransomware Attacks

Cited by 3 publications

References 37 publications

E-SDNN: encoder-stacked deep neural networks for DDOS attack detection

E-SDNN: encoder-stacked deep neural networks for DDOS attack detection

The types of hackers and cyberattacks in the aviation industry

Enhancing Ransomware Attack Detection Using Transfer Learning and Deep Learning Ensemble Models on Cloud-Encrypted Data

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