Machine learning (ML), especially deep learning (DL) techniques have been increasingly
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …

In partially observable (PO) environments, deep reinforcement learning (RL) agents often
suffer from unsatisfactory performance, since two problems need to be tackled together: how to …

Unsupervised Deep Learning (DL) techniques have been widely used in various security-related
anomaly detection applications, owing to the great promise of being able to detect …

Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct
advantages, eg, solving memory-dependent tasks and meta-learning. However, little effort has …

Host-based threats such as Program Attack, Malware Implantation, and Advanced Persistent
Threats (APT), are commonly adopted by modern attackers. Recent studies propose …

Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …

Most image encryption algorithms encrypt the whole image, but only part of the data is
important in the image. In this paper, we propose a multidimensional chaotic image encryption …

How to make intelligent decisions is a central problem in machine learning and artificial
intelligence. Despite recent successes of deep reinforcement learning (RL) in various decision …

Securing inter-domain routing systems of the Internet from illegitimate prefix annoucements
has been a great concern for the researchers and network operators. After the failure of …

Differentiation of isomers is an important analytical task in many fields, eg, chemistry, biology,
pharmaceutical science, medicine, environmental science, food science. Ion mobility mass …