Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey
Machine learning has become widely adopted as a strategy for dealing with a variety of
cybersecurity issues, ranging from insider threat detection to intrusion and malware …
cybersecurity issues, ranging from insider threat detection to intrusion and malware …
Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system
The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
Synthetic attack data generation model applying generative adversarial network for intrusion detection
Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …
deep learning (DL) techniques depends on the number of representative samples available …
Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review
Abstract The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as
decentralized internet-based technologies and have led to an increase in information in …
decentralized internet-based technologies and have led to an increase in information in …
Numerical feature selection and hyperbolic tangent feature scaling in machine learning-based detection of anomalies in the computer network behavior
Anomaly-based intrusion detection systems identify the computer network behavior which
deviates from the statistical model of typical network behavior. Binary classifiers based on …
deviates from the statistical model of typical network behavior. Binary classifiers based on …
Effective network intrusion detection using stacking-based ensemble approach
The increasing demand for communication between networked devices connected either
through an intranet or the internet increases the need for a reliable and accurate network …
through an intranet or the internet increases the need for a reliable and accurate network …
Peerambush: Multi-layer perceptron to detect peer-to-peer botnet
Due to emerging internet technologies that mostly depend on the decentralization concept,
such as cryptocurrencies, cyber attackers also use the decentralization concept to develop …
such as cryptocurrencies, cyber attackers also use the decentralization concept to develop …
Feature drift aware for intrusion detection system using developed variable length particle swarm optimization in data stream
Intrusion Detection Systems (IDS) serve as critical components in safeguarding network
security by detecting malicious activities. Although IDS has recently been treated primarily …
security by detecting malicious activities. Although IDS has recently been treated primarily …
Cybersecurity in smart cities: Detection of opposing decisions on anomalies in the computer network behavior
The increased use of urban technologies in smart cities brings new challenges and issues.
Cyber security has become increasingly important as many critical components of …
Cyber security has become increasingly important as many critical components of …
Infrastructure-wide and intent-based networking dataset for 5G-and-beyond AI-driven autonomous networks
In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for
their development in cellular networks, especially in 5G-and-beyond networks. The …
their development in cellular networks, especially in 5G-and-beyond networks. The …