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.

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Community detection in graphs

Statistics for Spatial Data.

Managing network traffic to improve availability of network services by classifying network traffic flows using flow-level statistical information and machine learning estimation, based on a measurement of at least one of relevance and goodness of network features. Also, determining a network traffic profile representing applications associated with the classified network traffic flows, and managing network traffic using the network traffic profile. The flow-level statistical information includes packet-trace information and is available from at least one of Cisco NetFlow, NetStream or cflowd records. The classification of network flows includes tagging packet-trace flow record data based on defined packet content information. The classifying of network flows can result in the identification of a plurality of clusters based on the measurement of the relevance of the network features. Also, the classification of network traffic can use a correlation-based measure to determine the goodness of the network features.

Managing network traffic for improved availability of network services

Welcome to ACM SIGCOMM 2013!

This year's conference continues the SIGCOMM tradition of being the premier forum for the presentation of research on networking and communications. The technical program this year features a set of outstanding papers that cover a wide variety of areas including software defined networks, data center networks, wireless networks, content delivery, routing, congestion management, quality of service, security, privacy, measurement and analysis.

This year's call for papers attracted 246 submissions from all over the world. The 35 member Technical Program Committee along with a selected group of external experts carefully considered all of the submissions over two rounds or reviewing -- including an author feedback period - with a total of 898 detailed reviews completed. The TPC meeting to select the final program was held on the campus of the University of Wisconsin - Madison in late April, 2013. At the conclusion of the meeting, the committee had assembled a wonderful program composed of 38 papers, to be presented over three days at the conference. The quality of submissions was extremely high as reflected in the final technical program.

Following the TPC meeting, a subcommittee had the pleasure of selecting the best paper award winners. Five outstanding paper were considered as candidates for the award. After careful consideration, the paper entitled "Ambient Backscatter: Wireless Communication Out of Thin Air" by Vincent Liu, Aaron Parks, Vamsi Talla, Shyamnath Gollakota, David Wetherall and Joshua Smith received the Best Paper Award. Please join us in congratulating the authors!

Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM

Customer care calls serve as a direct channel for a service provider to learn feedbacks from their customers. They reveal details about the nature and impact of major events and problems observed by customers. By analyzing the customer care calls, a service provider can detect important events to speed up problem resolution. However, automating event detection based on customer care calls poses several significant challenges. First, the relationship between customers' calls and network events is blurred because customers respond to an event in different ways. Second, customer care calls can be labeled inconsistently across agents and across call centers, and a given event naturally give rise to calls spanning a number of categories. Third, many important events cannot be detected by looking at calls in one category. How to aggregate calls from different categories for event detection is important but challenging. Lastly, customer care call records have high dimensions (e.g., thousands of categories in our dataset). In this paper, we propose a systematic method for detecting events in a major cellular network using customer care call data. It consists of three main components: (i) using a regression approach that exploits temporal stability and low-rank properties to automatically learn the relationship between customer calls and major events, (ii) reducing the number of unknowns by clustering call categories and using L1 norm minimization to identify important categories, and (iii) employing multiple classifiers to enhance the robustness against noise and different response time. For the detected events, we leverage Twitter social media to summarize them and to locate the impacted regions. We show the effectiveness of our approach using data from a large cellular service provider in the US.

/pdf/event-detection-using-customer-care-calls-2819yru0v8.pdf

Event detection using customer care calls

Firewalls are critical security devices handling all traffic in and out of a network. Firewalls, like other software and hardware network devices, have vulnerabilities, which can be exploited by motivated attackers. However, because firewalls are usually placed in the network such that they are transparent to the end users, it is very hard to identify them and use their corresponding vulnerabilities to attack them. In this paper, we study firewall fingerprinting, in which one can use firewall decisions on TCP packets with unusual flags and machine learning techniques for inferring firewall implementation.

Firewall fingerprinting

A system and method are provided for identifying a change point in a set of data. The system performs the method by receiving a set of data. The data indicates a plurality of performance measurements from a measurement point in a network. Each of the plurality of measurements represents a single type of performance measurement made at the measurement point at each of a corresponding plurality of points in time. The method also includes dividing the set of data into a plurality of data points in a chronological order. Each data point has a value corresponding to the performance measurements. The method also includes ranking the data points in an ascending order, calculating a cumulative sum for each of the data points, calculating a change score for the set of data points. A change point is identified in the data set if the change score exceeds a predetermined confidence level.

Jia Wang

Papers

Managing network traffic for improved availability of network services

Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM

Event detection using customer care calls

Firewall fingerprinting

Method and system for detecting changes in network performance