The field of network security and intrusion detection is rapidly evolving, with a growing emphasis on AI-driven solutions. Recent developments have focused on improving the accuracy and efficiency of intrusion detection systems, as well as enhancing their ability to detect and respond to emerging threats. Notable advancements include the use of machine learning algorithms, such as decision trees and neural networks, to analyze network traffic and identify potential security threats. Additionally, researchers have explored the application of TinyML techniques to enable real-time intrusion detection on resource-constrained devices. Noteworthy papers in this area include the proposal of SPLIDT, a system that enables partitioned inference over sliding windows of packets, allowing for more accurate and scalable decision tree models. Another significant contribution is the development of a transformer-BiGRU-based framework for network intrusion detection, which combines machine learning and deep learning techniques to improve detection accuracy and efficiency. Overall, these advancements have the potential to significantly enhance network security and intrusion detection capabilities, enabling more effective protection against emerging threats.
Advancements in AI-Driven Network Security and Intrusion Detection
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SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Unsupervised Dataset Cleaning Framework for Encrypted Traffic Classification
Hybrid AI-Driven Intrusion Detection: Framework Leveraging Novel Feature Selection for Enhanced Network Security
Anomaly detection in network flows using unsupervised online machine learning
Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems
Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
DEViaN-LM: An R Package for Detecting Abnormal Values in the Gaussian Linear Model
Real-time ML-based Defense Against Malicious Payload in Reconfigurable Embedded Systems
HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
A Neural Network Approach to Multi-radionuclide TDCR Beta Spectroscopy
Evaluating Diverse Feature Extraction Techniques of Multifaceted IoT Malware Analysis: A Survey
A software security review on Uganda's Mobile Money Services: Dr. Jim Spire's tweets sentiment analysis
Where Have All the Firewalls Gone? Security Consequences of Residential IPv6 Transition
A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
A Framework for Detection and Classification of Attacks on Surveillance Cameras under IoT Networks
Lightweight Intrusion Detection System Using a Hybrid CNN and ConvNeXt-Tiny Model for Internet of Things Networks
Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs
Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection
SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks
Flow-Based Detection and Identification of Zero-Day IoT Cameras
A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management