A survey on data-driven network intrusion detection
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …
classes compared to normal traffic. Many datasets are collected in simulated environments …
Implementing data exfiltration defense in situ: a survey of countermeasures and human involvement
In this article we consider the problem of defending against increasing data exfiltration
threats in the domain of cybersecurity. We review existing work on exfiltration threats and …
threats in the domain of cybersecurity. We review existing work on exfiltration threats and …
Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in SDN: A social multimedia perspective
The continuous development and usage of multi-media-based applications and services
have contributed to the exponential growth of social multimedia traffic. In this context, secure …
have contributed to the exponential growth of social multimedia traffic. In this context, secure …
A deep learning method with filter based feature engineering for wireless intrusion detection system
SM Kasongo, Y Sun - IEEE access, 2019 - ieeexplore.ieee.org
In recent years, the increased use of wireless networks for the transmission of large volumes
of information has generated a myriad of security threats and privacy concerns; …
of information has generated a myriad of security threats and privacy concerns; …
Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms
The appealing features of Cloud Computing continue to fuel its adoption and its integration
in many sectors such industry, governments, education and entertainment. Nevertheless …
in many sectors such industry, governments, education and entertainment. Nevertheless …
Security threats, defense mechanisms, challenges, and future directions in cloud computing
S El Kafhali, I El Mir, M Hanini - Archives of Computational Methods in …, 2022 - Springer
Several new technologies such as the smart cities, the Internet of Things (IoT), and 5G
Internet need services offered by cloud computing for processing and storing more …
Internet need services offered by cloud computing for processing and storing more …
A deep learning approach for effective intrusion detection in wireless networks using CNN
B Riyaz, S Ganapathy - Soft Computing, 2020 - Springer
Security is playing a major role in this Internet world due to the rapid growth of Internet users.
The various intrusion detection systems were developed by many researchers in the past to …
The various intrusion detection systems were developed by many researchers in the past to …
Secure deep learning in defense in deep-learning-as-a-service computing systems in digital twins
While Digital Twins (DTs) bring convenience to city managers, they also generate new
challenges to city network security. Currently, cyberspace security becomes increasingly …
challenges to city network security. Currently, cyberspace security becomes increasingly …
[HTML][HTML] An IoT-focused intrusion detection system approach based on preprocessing characterization for cybersecurity datasets
Security in IoT networks is currently mandatory, due to the high amount of data that has to be
handled. These systems are vulnerable to several cybersecurity attacks, which are …
handled. These systems are vulnerable to several cybersecurity attacks, which are …
A novel model for anomaly detection in network traffic based on kernel support vector machine
Q Ma, C Sun, B Cui, X Jin - Computers & Security, 2021 - Elsevier
Abstract Machine learning models are widely used for anomaly detection in network traffic.
Effective transformation of the raw traffic data into mathematical expressions and hyper …
Effective transformation of the raw traffic data into mathematical expressions and hyper …