Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

Statistics for Spatial Data.

Disclosed is a method and system for optimizing a first set of rules enforced by a firewall on network traffic. Characteristics of the network traffic are examined and these characteristics are used to generate a second set of rules. The first set of rules may have a different order than the second set of rules.

Method and apparatus for optimizing a firewall

Significant geospatial and temporal correlations, in terms of traffic volume and application access, exist in cellular network usage as shown in recent studies on cellular network measurement. Such geospatial and temporal correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained joint characterization of the geospatial and temporal dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial and temporal dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings for better network performance and user experience.

/pdf/geospatial-and-temporal-dynamics-of-application-usage-in-4ekxcyohiu.pdf

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Social media sites such as Twitter continue to grow at a fast pace. People of all generations use social media to exchange messages and share experiences of their life in a timely fashion. Most of these sites make their data available. An intriguing question is can we exploit this real-time and massive data-flow to improve business in a measurable way. In this paper, we are particularly interested in tweets (Twitter messages) that are relevant to mobile network performance. We compare tweets with a more traditional source of user experience, i.e., customer care tickets, and correlate both of them with a list of major network incidents. From our study, we have the following observations. First, Twitter users and users who call customer service tend to report different types of performance issues. Second, we observe that tweets typically appear more rapidly in response to network problems than customer tickets. They also appear to respond to a wider range of network issues. Third, significant spikes in the number of tweets appear to indicate short term performance impairments which are not reported in our current list of major network incidents. These observations together indicate that Twitter is an attractive, complementary source for monitoring service performance and its impact on user experience.

/pdf/listen-to-me-if-you-can-tracking-user-experience-of-mobile-1inkto1zje.pdf

Listen to me if you can: tracking user experience of mobile network on social media

Network prefix-level traffic profiling: Characterizing, modeling, and evaluation

Prefix hijacking is one of the top known threats on today's Internet. A number of measurement based solutions have been proposed to detect prefix hijacking events. In this paper we take these solutions one step further by addressing the problem of locating the attacker in each of the detected hijacking event. Being able to locate the attacker is critical for conducting necessary mitigation mechanisms at the earliest possible time to limit the impact of the attack, successfully stopping the attack and restoring the service.

We propose a robust scheme named LOCK, for LOCating the prefix hijacKer ASes based on distributed Internet measurements. LOCK locates each attacker AS by actively monitoring paths (either in the control-plane or in the data-plane) to the victim prefix from a small number of carefully selected monitors distributed on the Internet. Moreover, LOCK is robust against various countermeasures that the hijackers may employ. This is achieved by taking advantage of two observations: that the hijacker cannot manipulate AS path before the path reaches the hijacker, and that the paths to victim prefix "converge" around the hijacker AS. We have deployed LOCK on a number of PlanetLab nodes and conducted several large scale measurements and experiments to evaluate the performance. Our results show that LOCK is able to pinpoint the prefix hijacker AS with an accuracy up to 94.3%.

/pdf/locating-prefix-hijackers-using-lock-4xf6iymmns.pdf

Jia Wang

Papers

Method and apparatus for optimizing a firewall

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Listen to me if you can: tracking user experience of mobile network on social media

Network prefix-level traffic profiling: Characterizing, modeling, and evaluation

Locating prefix hijackers using LOCK