Data preprocessing is widely recognized as an important stage in anomaly detection. This
paper reviews the data preprocessing techniques used by anomaly-based network intrusion …

Anomaly detection (AD) use within the network intrusion detection field of research, or
network intrusion AD (NIAD), is dependent on the proper use of similarity and distance …

Learning-based classifiers are increasingly used for detection of various forms of malicious
data. However, if they are deployed online, an attacker may attempt to evade them by …

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem
from adversarial or unlikely events with unknown distributions. However, the predictive …

Security analysis of learning algorithms is gaining increasing importance, especially since
they have become target of deliberate obstruction in certain applications. Some security …

Written by leading researchers, this complete introduction brings together all the theory and
tools needed for building robust machine learning in adversarial environments. Discover …

Self-adaptive software systems are capable of adjusting their behavior at runtime to achieve
certain functional or quality-of-service goals. Often a representation that reflects the internal …

Intrusion Detection System (IDS) deals with huge amount of network traffic and uses large
feature set to discriminate normal pattern and intrusive pattern. However, most of existing …

We review and evaluate three techniques that allow a passive adversary to track users who
have dynamic IP addresses based on characteristic behavioral patterns, ie, without cookies …

ENCOPLOT: Pairwise Sequence Matching in Linear Time Applied to Plagiarism Detection Page
1 Network Security Plagiarism Encoplot ENCOPLOT: Pairwise Sequence Matching in Linear …