Titlebook: Applications of Data Mining in Computer Security; Daniel Barbará,Sushil Jajodia Book 2002 Springer Science+Business Media New York 2002 In

記憶

https://doi.org/10.1007/978-3-662-66015-7ion or identification undertaken on a corpus of multi-author and multi-topic e-mail documents. We use an extended set of e-mail document features such as structural characteristics and linguistic patterns together with a Support Vector Machine as the learning algorithm. Experiments on a number of e-

使乳化

https://doi.org/10.1007/978-1-4615-0953-0Information; Variable; architecture; data mining; genome; knowledge; security

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custody

Advances in Information Securityhttp://image.papertrans.cn/a/image/159359.jpg

Anthrp

Applications of Data Mining in Computer Security978-1-4615-0953-0Series ISSN 1568-2633 Series E-ISSN 2512-2193

eustachian-tube

Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt,llels two important aspects of intrusion detection: general detection strategy (misuse detection versus anomaly detection) and data source (individual hosts versus network traffic). Misuse detection attempts to match known patterns of intrusion, while anomaly detection searches for deviations from n

Incorporate

Data Mining for Intrusion Detection,nd network management. Over the past five years, a growing number of research projects have applied data mining to various problems in intrusion detection. This chapter surveys a representative cross section of these research efforts. Moreover, four characteristics of contemporary research are ident

Living-Will

An Architecture for Anomaly Detection,profile that contains a representation of the “normal” or expected traffic. The system flags anything that exceeds the normal activity (usually by means of thresholds) as an attack. Unfortunately, not everything that surpasses the expected activity is indeed an attack. Thus, anomaly detection system

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