Machine Learning and Data Mining for Computer Security: by Marcus A. Maloof

By Marcus A. Maloof

"Machine studying and information Mining for machine Security" offers an outline of the present country of analysis in desktop studying and information mining because it applies to difficulties in machine safeguard. This booklet has a robust specialize in details processing and combines and extends effects from machine security.

The first a part of the booklet surveys the information resources, the educational and mining equipment, assessment methodologies, and prior paintings proper for computing device defense. the second one a part of the publication includes articles written via the pinnacle researchers operating during this zone. those articles bargains with subject matters of host-based intrusion detection throughout the research of audit trails, of command sequences and of method calls in addition to community intrusion detection throughout the research of TCP packets and the detection of malicious executables.

This booklet fills the nice desire for a publication that collects and frames paintings on constructing and utilizing equipment from computer studying and information mining to difficulties in desktop security.


Intrusion detection and research has bought loads of feedback and exposure over the past numerous years. The Gartner document took a shot asserting Intrusion Detection platforms are lifeless, whereas others think Intrusion Detection is simply attaining its adulthood. the matter that few are looking to admit is that the present public tools of intrusion detection, whereas they could be mature, established exclusively at the truth they've been round for your time, should not super refined and don't paintings rather well. whereas there isn't any such factor as a hundred% defense, humans continually anticipate a expertise to complete greater than it at present does, and this is often essentially the case with intrusion detection. It has to be taken to the following point with extra complicated research being performed by way of the pc and not more via the human.

The present zone of Intrusion Detection is begging for laptop studying to be utilized to it. Convergence of those key components is important for it to be taken to the subsequent point. the matter is that i've got noticeable little learn targeting this, until eventually now.

After analyzing desktop studying and information Mining for machine safeguard, i think Dr Maloof has hit the objective useless centre. whereas a lot examine has been performed throughout desktop protection independently and computer studying independently, for a few cause not anyone desired to cross-breed the 2 topics.

Dr Maloof not just did a masterful activity of focusing the ebook on a serious quarter that was once in dire desire of study, yet he additionally strategically picked papers that complemented one another in a efficient demeanour. frequently analyzing an edited quantity like this, the chapters are very disjointed with out connection among them. whereas those chapters conceal various components of study, there's a hidden movement that enhances the former bankruptcy with the next.

While Dr Maloof issues out in his Preface the meant viewers, i believe that there are extra serious teams. to start with, i believe that any seller or answer supplier that's seeking to offer a aggressive virtue for his or her product may still learn this e-book and notice precisely what the potential for subsequent new release intrusion detection provides. Secondly, i think that any desktop technology pupil should still learn this publication to appreciate the facility of convergence throughout applied sciences. everyone seems to be trying to find new recommendations to intrusion detection inside machine technology and extra in particular inside desktop safety. yet until eventually they're keen to take a step again and supply perception and data from one other area, like Dr Maloof has performed, they won't locate compatible answers.

This publication is a needs to learn for somebody drawn to how examine can enhance computing device safety It additionally presents perception into extra parts of analysis that may be pursued. the factors to degree a e-book like this can be: ‘Does it get you considering and does it nurture the inventive juices from within?’ the fast solution is actually. After studying this e-book, i used to be writing down a number of extra components that i wished to investigate and pursue as follow-on to examining this booklet. that could be a key mark of a beneficial book!

-- Dr Eric Cole, desktop protection Expert

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Extra info for Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing)

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It is also less costly to extract attribute values from the packet header than from the data buffer, which could be encrypted. The examples themselves may have different associated costs. If we are interested in building a system to identify plants, collecting examples of plants that grow locally in abundance is less costly than collecting examples of endangered plants that grow only in remote forests. Similarly, we can easily generate traces of attacks if they have been scripted. However, it is more difficult – and more costly – to obtain traces of novel unscripted attacks.

As before, we apply an algorithm to the training set and evaluate the resulting model on the testing set, calculating percent correct. We repeat this process using each of the partitions as the testing set and using the remaining partitions to form a training set. The overall accuracy is the accuracy averaged over the number of runs, which is equivalent to the number of partitions. Stratified cross-validation involves creating partitions so that the number of examples of each class is proportional to the number in the original set of examples.

We should also note that there are applications, especially in computer security, in which attribute values and class labels for some examples are missing or difficult to determine. Regarding class labels in particular, there is a spectrum between a fully labeled set of examples and a fully unlabeled set. In the following discussion, we will use the term example to mean a set of attribute values with a label and use the term observation to mean a set of attribute values without a class label. To transform raw data into a set of examples, we can apply a myriad of operations.

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