TITLE:
Cyberspace Security Using Adversarial Learning and Conformal Prediction
AUTHORS:
Harry Wechsler
KEYWORDS:
Active Learning, Adversarial Learning, Anomaly Detection, Change Detection, Conformal Prediction, Cyber Security, Data Mining, Denial and Deception, Human Factors, Insider Threats, Intrusion Detection, Meta-Reasoning, Moving Target Defense, Performance Evaluation, Randomness, Semi-Supervised Learning, Sequence Analysis, Statistical Learning, Transduction
JOURNAL NAME:
Intelligent Information Management,
Vol.7 No.4,
July
10,
2015
ABSTRACT: This paper advances new directions for
cyber security using adversarial learning and conformal prediction in order to
enhance network and computing services defenses against adaptive, malicious,
persistent, and tactical offensive threats. Conformal prediction is the
principled and unified adaptive and learning framework used to design, develop,
and deploy a multi-facetedself-managing defensive shield to detect,
disrupt, and deny intrusive attacks, hostile and malicious behavior, and
subterfuge. Conformal prediction leverages apparent relationships between immunity
and intrusion detection using non-conformity measures characteristic of
affinity, a typicality, and surprise, to recognize patterns and messages as
friend or foe and to respond to them accordingly. The solutions proffered
throughout are built around active learning, meta-reasoning, randomness,
distributed semantics and stratification, and most important and above all
around adaptive Oracles. The motivation for using conformal prediction and its
immediate off-spring, those of semi-supervised learning and transduction, comes
from them first and foremost supporting discriminative and non-parametric
methods characteristic of principled demarcation using cohorts and sensitivity
analysis to hedge on the prediction outcomes including negative selection, on one
side, and providing credibility and confidence indices that assist
meta-reasoning and information fusion.