With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify the network events as either normal events or attack events. Our research study claims that the Hidden Naive Bayes (HNB) model can be applied to intrusion detection problems that suffer from dimensionality, highly correlated features and high network data stream volumes. HNB is a data mining model that relaxes the Naive Bayes method's conditional independence assumption. Our experimental results show that the HNB model exhibits a superior overall performance in terms of accuracy, error rate and misclassification cost compared with the traditional Naive Bayes model, leading extended Naive Bayes models and the Knowledge Discovery and Data Mining (KDD) Cup 1999 winner. Our model performed better than other leading state-of-the art models, such as SVM, in predictive accuracy. The results also indicate that our model significantly improves the accuracy of detecting denial-of-services (DoS) attacks.

A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier

The overwhelming majority of a software systems lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Googles Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. Youll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficientlessons directly applicable to your organization. This book is divided into four sections: Introduction Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices Understand the theory and practice of an SREs day-to-day work: building and operating large distributed computing systems Management Explore Google's best practices for training, communication, and meetings that your organization can use

Site Reliability Engineering: How Google Runs Production Systems

For a static analysis project to succeed, developers must feel they benefit from and enjoy using it.

https://dl.acm.org/doi/pdf/10.1145/3188720

Lessons from building static analysis tools at Google

We report our experience with SAPFIX: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code1. We have used SAPFIX at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code, and which are collectively used by hundreds of millions of people worldwide.

/pdf/sapfix-automated-end-to-end-repair-at-scale-2idh1zkr7z.pdf

SapFix: automated end-to-end repair at scale

Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into feature selection. Surprisingly, in sharp contrast with binary logit, current software packages lack any feature-selection algorithm for MultiNomial Logit. Conversely, Random Forests, another algorithm learning multiclass problems, is just like MNL robust but unlike MNL it easily handles high-dimensional feature spaces. This paper investigates the potential of applying the Random Forests principles to the MNL framework. We propose the Random MultiNomial Logit (RMNL), i.e. a random forest of MNLs, and compare its predictive performance to that of (a) MNL with expert feature selection, (b) Random Forests of classification trees. We illustrate the Random MultiNomial Logit on a cross-sell CRM problem within the home-appliances industry. The results indicate a substantial increase in model accuracy of the RMNL model to that of the MNL model with expert feature selection. 

Random Forests for multiclass classification: Random MultiNomial Logit

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

/pdf/evolutionary-optimization-of-radial-basis-function-mbyw310yx0.pdf

Evolutionary optimization of radial basis function classifiers for data mining applications

A matching library of a code analysis system defines a number of predicates applicable to an abstract syntax tree (AST) corresponding to program code being analyzed. The predicates represent whether or not nodes of the program code's AST have corresponding properties, such as representing a class that inherits from another given class. The code analysis system obtains an AST to analyze, a predicate expression query that includes predicates of the matching library, and action code. The code analysis system identifies nodes of the AST that match the predicate expression query by executing code of the matching library and executes the action code against the identified nodes.

Predicate matching library for complex program analysis

A system, method, and computer-readable medium for static analysis of large-scale computer programs that uses a compiler to shard the computer programs by source files. Original compilation information is received for one or more groups of source files that reference modules in module containers. The compiler is run to determine a set of modules in the module containers and other source files that are referenced by a source file of the groups of source files. The compilation information is modified on the basis of the source file, the set of modules and the referenced source files. Static analysis is performed on the source file using the modified compilation information. Static analysis is efficiently performed over large-scale computer programs.

Sharding program compilation for large-scale static analysis

A build system and method, including receiving attribute rules and new rules, wherein the attribute rules correspond to one or more predefined default actions of the build system, wherein the new rules specify new actions that are to be added to the build system A graph is generated to include files specified as attributes in the attributes rules and the one or more predefined default actions that correspond to the attributes rules A request to enable at least one of the new rules is received Action listener rules are received, wherein the action listener rules indicate default actions and corresponding new rules of the one or more new rules The graph is checked for default actions that are indicated in the action listener rules Additional actions are added to the graph for new rules based on the default actions indicated in the action listener rules

Systems and methods for rule based meta-programming of actions for rule based build systems

Software reuse is a challenging and multifaceted topic. Significant research effort has been spent to address technical and organizational aspects. However, adoption of proposed practices and novel approaches often proceeds slowly. Additionally, little is known on how reuse is currently effected in practice and which solutions have proven useful. This paper aims to shed light on the matter by studying the current practice of reuse at Google. We conduct an exploratory study with a total of 49 participants of which 39 answered our online questionnaire and 10 participated in our 1h interviews. We assess reuse practices, success factors and challenges and collect ideas for improvement. We distill our findings to provide practitioners with examples of scalable reuse practices and detail on prerequisites required to implement/tailor a similar reuse approach. Furthermore, we point out open issues to support researchers and practitioners alike to align their efforts for developing solutions.

An exploratory study on reuse at google

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