A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …
between different communicating devices. The data should be communicated securely …
Comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree
Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …
A new intelligent multilayer framework for insider threat detection
In several earlier studies, machine learning (ML) has been widely used for building insider
threat detection systems. However, the selection of the most appropriate ML classification …
threat detection systems. However, the selection of the most appropriate ML classification …
Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images
Gastrointestinal tract (GIT) infections such as ulcers, bleeding, polyps, Crohn's disease and
cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient …
cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient …
A survey on security issues in services communication of Microservices‐enabled fog applications
Fog computing is used as a popular extension of cloud computing for a variety of emerging
applications. To incorporate various design choices and customized policies in fog …
applications. To incorporate various design choices and customized policies in fog …
DDoS attack detection with feature engineering and machine learning: the framework and performance evaluation
M Aamir, SMA Zaidi - International Journal of Information Security, 2019 - Springer
This paper applies an organized flow of feature engineering and machine learning to detect
distributed denial-of-service (DDoS) attacks. Feature engineering has a focus to obtain the …
distributed denial-of-service (DDoS) attacks. Feature engineering has a focus to obtain the …
RETRACTED ARTICLE: Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS
The problem of low rate attack detection has been well studied in different situations.
However the methods suffer to achieve higher performance in low rate attack detection. The …
However the methods suffer to achieve higher performance in low rate attack detection. The …
Root exploit detection and features optimization: mobile device and blockchain based medical data management
The increasing demand for Android mobile devices and blockchain has motivated malware
creators to develop mobile malware to compromise the blockchain. Although the blockchain …
creators to develop mobile malware to compromise the blockchain. Although the blockchain …
Low-rate DoS attack detection using PSD based entropy and machine learning
The Distributed Denial of Service attack is one of the most common attacks and it is hard to
mitigate, however, it has become more difficult while dealing with the Low-rate DoS (LDoS) …
mitigate, however, it has become more difficult while dealing with the Low-rate DoS (LDoS) …
A self-supervised anomaly detection algorithm with interpretability
Z Wu, X Yang, X Wei, P Yuan, Y Zhang, J Bai - Expert Systems with …, 2024 - Elsevier
Identifying the abnormal samples from a data set and determining their type are two key
tasks of anomaly detection. However, the existing anomaly detection algorithms are …
tasks of anomaly detection. However, the existing anomaly detection algorithms are …