By Marcus A. Maloof (auth.), Marcus A. Maloof BS, MS, PhD (eds.)
The web all started as a personal community connecting executive, army, and educational researchers. As such, there has been no need for safe protocols, encrypted packets, and hardened servers. whilst the construction of the realm extensive net all at once ushered within the age of the industrial web, the network's measurement and next quick growth made it very unlikely retroactively to use safe mechanisms. The Internet's architects by no means coined phrases equivalent to spam, phishing, zombies, and spyware, yet they're phrases and phenomena we now come across constantly.
Programming detectors for such threats has confirmed tricky. positioned easily, there's an excessive amount of information---too many protocols, too many layers, too many functions, and too many makes use of of those applications---for somebody to make adequate feel of all of it. mockingly, given this wealth of data, there's additionally too little information regarding what's vital for detecting attacks.
Methods of desktop studying and knowledge mining will help construct higher detectors from monstrous quantities of advanced information. Such equipment may also support observe the knowledge required to construct safer structures. For a few difficulties in machine protection, you'll be able to at once follow computing device studying and information mining innovations. different difficulties, either present and destiny, require new methods, equipment, and algorithms.
This ebook offers examine performed in academia and on equipment and purposes of laptop studying and knowledge mining for difficulties in desktop safeguard and should be of curiosity to researchers and practitioners, in addition students.
‘Dr. Maloof not just did a masterful activity of focusing the ebook on a severe zone that was once in dire desire of study, yet he additionally strategically picked papers that complemented one another in a effective demeanour. … This publication is a needs to learn for a person attracted to how examine can increase machine security.’
Dr Eric Cole, computing device protection Expert
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Additional info for Machine Learning and Data Mining for Computer Security: Methods and Applications
Example text
However, a large amount of data does not always make for a difficult learning or mining problem, for the complexity of what the algorithm must learn or mine is also critical. These difficulties of transforming a raw data source into a set of examples have prompted some researchers to investigate automated methods of finding representations for examples. Methods of feature construction, feature engineering, or constructive induction automatically transform raw data into examples suitable for learning.
There are two books about data mining approaches to computer security complementary to this one. Mena [90] surveys several machine learning and data mining technologies and describes companies, software, and case studies of such technologies applied to a wide range of applications, including intrusion detection, fraud detection, and criminal profiling. Reference [91] is a collection of research articles, similar to the second part of this volume. Readers with a concrete or practical understanding of such topics may want to investigate sources that discuss these issues more generally and more formally.
A+b+c+d Percent correct is simply accuracy expressed as a percentage. Other performance metrics include: • Error rate, being the portion of the examples in the test set the model predicts incorrectly: (b + c)/(a + b + c + d). • True-positive rate (tp), hit rate, detect rate, or sensitivity, being the portion of the positive examples the model predicts correctly: a/(a + b). • True-negative rate (tn), correct-reject rate, or specificity, being the portion of the negative examples the model predicts correctly: d/(c + d).