Network Packet Processing and Optimization
67 papers on this topic
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of...
HybridGuard: Enhancing Minority-Class Intrusion Detection in Dew-Enabled Edge-of-Things Networks
Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a framework that integrates machine learning and deep...
Explainable AI for Enhancing IDS Against Advanced Persistent Kill Chain
Advanced Persistent Threats (APTs) represent a sophisticated and persistent cy-bersecurity challenge, characterized by stealthy, multi-phase, and targeted attacks aimed at compromising information...
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks
Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their sophisticated and stealthy nature. Traditional Intrusion Detection Systems (IDS) often fall short...
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities...
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection
Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For Network Intrusion Detection (NID), however, scientific advances in ML are still seen with skepticism by...
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation
Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the security posture of...
Profile-Guided Temporal Prefetching
Temporal prefetching shows promise for handling irregular memory access patterns, which are common in data-dependent and pointer-based data structures. Recent studies introduced on-chip metadata...
PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices
Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory (PIM) by associating their memory banks with processing elements (PEs), allowing applications to overcome...
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection
The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and...
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
Among the various types of cyberattacks, identifying zero-day attacks is problematic because they are unknown to security systems as their pattern and characteristics do not match known...
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems
The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge...
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems
In this paper, we propose a robust and reinforcement-learning-enhanced network intrusion detection system (NIDS) designed for class-imbalanced and few-shot attack scenarios in Industrial Internet...
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML...
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection
As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe...
Unveiling the potential of Graph Neural Networks for robust Intrusion Detection
The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works...
Adaptive Quotient Filters
Adaptive filters, such as telescoping and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters...
Self-adaptive Traffic Anomaly Detection System for IoT Smart Home Environments
With the growth of internet of things (IoT) devices, cyberattacks, such as distributed denial of service, that exploit vulnerable devices infected with malware have increased. Therefore, vendors...