This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced open-source spatial indexing data structures implemented in C++ to speed up computation. An important advantage of this implementation is that it is up-to-date with several improvements that have been added since the original algorithms were publications (e.g., artifact corrections and dendrogram extraction methods for OPTICS). We provide a consistent presentation of the DBSCAN and OPTICS algorithms, and compare dbscan's implementation with other popular libraries such as the R package fpc, ELKI, WEKA, PyClustering, SciKit-Learn, and SPMF in terms of available features and using an experimental comparison.
Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of diagnosing what aspects of a model’s input drive its decisions. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN’s decisions has thus blossomed into an active and broad area of research. The field’s complexity is exacerbated by competing definitions of what it means “to explain” the actions of a DNN and to evaluate an approach’s “ability to explain”. This article offers a field guide to explore the space of explainable deep learning for those in the AI/ML field who are uninitiated. The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) discusses user-oriented explanation design and future directions. We hope the guide is seen as a starting point for those embarking on this research field.
Most modern Web robots that crawl the Internet to support value-added services and technologies possess sophisticated data collection and analysis capabilities. Some of these robots, however, may be ill-behaved or malicious, and hence, may impose a significant strain on a Web server. It is thus necessary to detect Web robots in order to block undesirable ones from accessing the server. Such detection is also essential to ensure that the robot traffic is considered appropriately in the performance and capacity planning of Web servers. Despite a variety of Web robot detection techniques, there is no consensus regarding a single technique, or even a specific "type" of technique, that performs well in practice. Therefore, to aid in the development of a practically applicable robot detection technique, this survey presents a critical analysis and comparison of the prevalent detection approaches. We propose a framework to classify the existing detection techniques into four categories based on their underlying detection philosophy. We compare the different classes to gain insights into those characteristics that make up an effective robot detection scheme. Finally, we discuss why the contemporary techniques fail to offer a general solution to the robot detection problem and propose a set of key ingredients necessary for strong Web robot detection.
Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
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