Sep 2026· Journal of Computer Virology and Hacking Techniques· Vol 22· 0 citations· 29 references
Computer Science
TL;DR
An incremental learning based framework to detect anomalous traffic patterns which may indicate any key misuse in Software-Defined Networks in dynamic and real-time environments in modern SDN environments is proposed.
This proposed framework aims to fortify data protection and ensure user privacy in essential areas like healthcare, financial services, and e-governance, thereby fostering increased trust.
Sai Kiranmai Dornala, S. P.· International Journal of Int...· 0 citations
This study evaluates two explainable ML classifiers, XGBoost and Random Forest, for DDoS detection and examines whether their near-perfect offline accuracy translates into reliable physical-network operation, indicating that offline benchmarks alone are insufficient for validating IDS readiness.
Muhammad Azzam Anshori, R. Amri· Journal of Computer Science...· 0 citations
An explainable machine learning-based framework for accurate, transparent, and reliable DDoS attack detection in an SDN environment that combines reliable DDoS detection with transparent, analyst-oriented decision support for SDN security monitoring is developed.
J. Malik, N. Naz, Muhammad Saleem et al.· Italian National Conference...· 0 citations
As cyber-attacks evolve and the use of encrypted command and control (C2) channels grows, it presents a real challenge for modern network security. The classifiers that are currently available for detecting malicious traffic in encrypted traffic streams are not sufficiently effective, and require more sophisticated met...
Manikandan S, S. M., Vaishalini V· International Journal of App...· 0 citations
An intelligent DDoS detection and mitigation framework that combines classical Machine Learning (ML) classifiers with Deep Learning (DL) architectures to achieve high-fidelity, low-latency attack identification across heterogeneous network topologies is presented.
S. Singh, Alok Kumar· International Journal of Com...· 0 citations
The rapid growth of network-connected systems has made cyber threat detection a critical priority for modern infrastructures. Traditional signature-based intrusion detection systems (IDSs) struggle to detect novel and evolving attacks, creating the need for intelligent learning-based approaches. This paper presents Sec...
Buddha Dev Sarker, Md Fahim Ahammed, Md Rasheduzzaman Labu et al.· International Conference Com...· 0 citations
AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
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