When a RF beats a CNN and GRU, together—A comparison of deep learning and classical machine learning approaches for encrypted malware traffic classification

A Lichy, O Bader, R Dubin, A Dvir, C Hajaj - Computers & Security, 2023 - Elsevier
Internet traffic classification plays a crucial role in Quality of Experience (QoE), Quality of
Services (QoS), intrusion detection, and traffic-trend analyses. While there is no theoretical …

TGPrint: Attack fingerprint classification on encrypted network traffic based graph convolution attention networks

L Wang, X Ma, N Li, Q Lv, Y Wang, W Huang… - Computers & Security, 2023 - Elsevier
Nowadays, most network traffic is encrypted, which protects user privacy but hides attack
traces, further hindering identifying attacks to inspect traffic packages. Machine Learning …

Efficient Machine Learning-Based Security Monitoring and Cyberattack Classification of Encrypted Network Traffic in Industrial Control Systems

F Specht, J Otto - 2024 IEEE 29th International Conference on …, 2024 - ieeexplore.ieee.org
Security monitoring is a key aspect to detect cyberattacks against industrial control systems.
However, with the increasing use of encryption in industrial communication protocols …

[PDF][PDF] CESSO-HCRNN: A Hybrid CRNN With Chaotic Enriched SSO-based Improved Information Gain to Detect Zero-Day Attacks.

DK Roy, R Patgiri - International Journal of Advanced …, 2023 - pdfs.semanticscholar.org
Hackers use the vulnerability before programmers have a chance to fix it, which is known as
a zero-day attack. Zero-day attackers have a variety of abilities, including the ability to alter …

The art of time-bending: Data augmentation and early prediction for efficient traffic classification

C Hajaj, P Aharon, R Dubin, A Dvir - Expert Systems with Applications, 2024 - Elsevier
The accurate identification of internet traffic is crucial for network management. However, the
use of encryption techniques and constant changes in network protocols make it difficult to …

Hidden in time, revealed in frequency: Spectral features and multiresolution analysis for encrypted internet traffic classification

N Dillbary, R Yozevitch, A Dvir… - 2024 IEEE 21st …, 2024 - ieeexplore.ieee.org
In recent years, privacy and security concerns have led to the wide adoption of encrypted
protocols, making encrypted traffic a major portion of overall communications online. The …

Augmenting Cyber Defense Counter To Zero-Day Attacks Through Predictive Analysis-A Fusion Methodology Assimilating Game Theory and RESNet Inspired …

S Akshaya, P Ganapathi - International Journal of …, 2024 - search.proquest.com
Zero-day attacks pose a significant threat to software vendors, as they exploit previously
unknown vulnerabilities, making them insidious and challenging to defend against …

CBR--Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution

A Lukach, R Dubin, A Dvir, C Hajaj - arxiv preprint arxiv:2403.11206, 2024 - arxiv.org
Encrypted network traffic Classification tackles the problem from different approaches and
with different goals. One of the common approaches is using Machine learning or Deep …

Machine learning approaches for detecting zero day intrusion attacks

H Suresh, N Kumar, BM Hithendra - … 3rd-4th July, 2024 (Volume 1 …, 2025 - books.google.com
The volume of data managed via the worldwide web has substantially increased over time
as more individuals use technology. There is an issue with data encryption while …

Early Detection and Classification of Zero-Day Attacks in Network Traffic Using Convolutional Neural Network

MP Singh, VP Singh, M Gupta - International Conference on Deep …, 2023 - Springer
Abstract In a Zero-Day cyberattack, attackers exploit a software vulnerability for which the
software vendor is unaware or has not released a patch. This can make it difficult for …