🚨 New article now available online!
As new attack strategies emerge, network intrusion detection systems are subject to increasing pressure. Accurate classification of potential threats requires both adequate analysis of complex data sets and effective utilisation of computational resources available.
Researchers Akanksha Pamena and Manohar Naik Sugali propose an approach combining deep learning for dimensionality reduction with attention modules: SAE-AM. Their solution proves highly effective, reaching 99% accuracy on benchmark datasets. Read the latest article on our website, “SAE-AM: Enhancing Network Intrusion Detection Using Sparse Autoencoders with Attention Modules” and learn more about their method.
https://www.acigjournal.com/SAE-AM-Enhancing-Network-Intrusion-Detection-Using-Sparse-Autoencoders-with-Attention,213872,0,2.html
🌐 Applied Cybersecurity & Internet Governance (#ACIG) is published by #NASK – National Research Institute
#cybersecurity #machineLearning #networkSecurity #intrusionDetection #machineLearning #deepLearning









