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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-faceted?self-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.
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篇名 Cyberspace Security Using Adversarial Learning and Conformal Prediction
来源期刊 智能信息管理(英文) 学科 工学
关键词 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
年,卷(期) 2015,(4) 所属期刊栏目
研究方向 页码范围 195-222
页数 28页 分类号 TP39
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
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
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
智能信息管理(英文)
半月刊
2160-5912
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
114
总下载数(次)
0
总被引数(次)
0
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