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/* * A program to transform words * Takes two arguments: The first is name of the word transformation file * The second is name of the input to transform This popular tutorial introduction to standard C++ has been completely updated, reorganized, and rewritten to help */ programmers learn the language faster and **argv) int main(int argc, char use it in a more modern, effective wayJust as C++ has evolved since the last edition, so has the authors' approach to teaching it They now introduce the C++ standard library from the beginning, giving { readers the // map to hold the word transformation having to master every language detail Highlighting today's best means to write useful programs without first pairs: practices, they show how to word programs that are safe, can be built quickly, and yet offer outstanding performance // key is the write to look for in the input; value is word to use in the output Examples that take advantage of the library,trans_map; features of C++, also show how to make the best use of the map<string, string> and explain the language As in its previous editions, the book's authoritative discussion of fundamental C++ concepts and techniques makes string key, value; it a valuableif (argc != more experienced programmersProgram Faster and More Effectively with This Rewritten resource even for 3) Classic Restructured for quicker learning, using the C++ standard library Updated to teach the most current programming throw runtime_error("wrong number of arguments"); styles and program design techniques Filled with new learning aids that emphasize important points, warn about common // open transformation file and check that open succeeded pitfalls, suggest good programming practices, and provide general usage tips Complete with exercises that reinforce skills ifstream map_file; learned Authoritative and comprehensive in its coverageThe source code for the book's extended examples is available on if (!open_file(map_file, argv[1])) the Web at the address below throw runtime_error("no transformation file"); // read the transformation map and build the map while (map_file >> key >> value) trans_mapinsert(make_pair(key, value)); // ok, now we're ready to do the transformations // open the input file and check that the pen succeeded ifstream input; if (!open_file(input, argv[2])) throw runtime_error("no input file"); string line; // hold each line from the input // read the text to transform it a line at a time while (getline(input, line)) { istringstream stream(line); // read the line a word at a time string word; bool firstword = true; // controls whether a space is printed while (stream >> word) { // ok: the actual mapwork, this part is the heart of the program map<string, string>::const_iterator map_it = trans_mapfind(word); // if this word is in the transformation map if (map_it != trans_mapend()) // replace it by the transformation value in the map word = map_it->second; if (firstword) firstword = false; else cout << " "; // print space between words cout << word; } cout << endl; // done with this line of input } return 0;.

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An anomaly is defined as something different, abnormal, peculiar or not easily classified While this concept can be applied to virtually anything, we are interested in how it applies to computer security In this context, an anomaly can be defined as some action or data that is not considered normal for a given system, user or network This definition still covers a fairly wide range and can include such things as traffic patterns, user activity and application behavior The belief is that at least a significant portion of threats or concerning conditions should manifest themselves as anomalies and thus be detectable The general approach used by anomaly detection is that something (ie, a network, a host, a set of users, etc) is observed and compared against expected behavior If variation from the expected is noted, that variation is flagged as an anomaly Often this includes some measure of the scope or nature of the variation and serves to drive further investigation, alerting, and analysis One of the key differences between anomaly detection and other forms of detection is that, rather than defining "what is not allowed or bad," it defines "what is allowed or good" Many traditional forms of detection rely on comparing observed behavior and noting when something known to be "bad" is seen These are often referred to as misuse detection systems, however, this nomenclature can be confusing since anomaly systems also detect misuse A more accurate name, perhaps, is explicit detection systems These systems operate well when the number of possible bad behaviors is small and does not change very rapidly owever, in larger systems with greater variation, these two conditions often do not hold It then becomes a very onerous task to maintain the list of what is bad Anomaly detection relies on having some definition of allowed behavior and then noting when observed behaviors differ Often additional information about the deviation is noted including its nature, size and frequency in order to further understand what the anomaly is This operates well when it is easier or more efficient to define what is allowed rather than what is not allowed In these cases, the definition of what is allowed tends to be much shorter It also tends not to require changes as new problems are created or discovered Naturally, however, some problems are more applicable than others to this type of approach In particular, certain considerations may alter the effectiveness of anomaly detection for a specific problem Since it is detecting things in a more general way, an anomaly detection system may not be able to identify a problem as precisely as an explicit system It is also important to consider the space that is being observed for anomalies If it is too large, variant and complex, it may be too difficult to describe problems in a manner general enough to make anomaly detection feasible (eg, general human behavior) Fortunately, many computer and network problems tend not to fall into this category which is what makes anomaly detection such an attractive technique Anomaly detection systems monitor networks for two primary criteria, characteristic deviation and statistical deviation While it can certainly be argued that there are more (or less) ways to classify such systems, these two divisions provide a convenient manner in which to discuss the various implementations Characteristic deviations tend to be more qualitative For example, "User joe123 does not normally use transfer files outside of the company" Statistical deviations, on the other hand, tend to be more quantitative For example, "This site's ICMP traffic never exceeds 15 percent of capacity".

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