Machine learning in cybersecurity: a comprehensive survey
Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics
In the near future, objects have to connect with each other which can result in gathering
private sensitive data and cause various security threats and cyber crimes. To prevent cyber …
private sensitive data and cause various security threats and cyber crimes. To prevent cyber …
SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)
A Wani, R Khaliq - CAAI Transactions on Intelligence …, 2021 - Wiley Online Library
The participation of ordinary devices in networking has created a world of connected
devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every …
devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every …
[HTML][HTML] An inception V3 approach for malware classification using machine learning and transfer learning
Malware instances have been extremely used for illegitimate purposes, and new variants of
malware are observed every day. Machine learning in network security is one of the prime …
malware are observed every day. Machine learning in network security is one of the prime …
A review of artificial intelligence based malware detection using deep learning
AAM Majid, AJ Alshaibi, E Kostyuchenko… - Materials Today …, 2023 - Elsevier
Malware propagation by adversaries has witnessed many issues across the globe. Often it is
found that malware is released in different countries for monetary gains. With the …
found that malware is released in different countries for monetary gains. With the …
Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features
As the number of internet users increases so does the number of malicious attacks using
malware. The detection of malicious code is becoming critical, and the existing approaches …
malware. The detection of malicious code is becoming critical, and the existing approaches …
Windows PE malware detection using ensemble learning
In this Internet age, there are increasingly many threats to the security and safety of users
daily. One of such threats is malicious software otherwise known as malware (ransomware …
daily. One of such threats is malicious software otherwise known as malware (ransomware …
A multikernel and metaheuristic feature selection approach for IoT malware threat hunting in the edge layer
H Haddadpajouh, A Mohtadi… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Internet-of-Things (IoT) devices are increasingly targeted, partly due to their presence in a
broad range of applications (including home and corporate environments). In this article, we …
broad range of applications (including home and corporate environments). In this article, we …
ConRec: malware classification using convolutional recurrence
Today, the extensive reliae on technology has exposed us to a constant threat of
sophisticated malware attacks. Various automated malware production techniques have …
sophisticated malware attacks. Various automated malware production techniques have …