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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining Practical Machine Learning Tools and Techniques

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

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The knowledge complexity of interactive proof-systems

Reliability in wireless sensor networks

Malicious web pages that launch client-side attacks on web browsers have become an increasing problem in recent years. High-interaction client honeypots are security devices that can detect these malicious web pages on a network. However, high-interaction client honeypots are both resource-intensive and known to miss attacks. This paper presents a novel classification method for detecting malicious web pages that involves inspecting the underlying static attributes of the initial HTTP response and HTML code. Because malicious web pages import exploits from remote resources and hide exploit code, static attributes characterizing these actions can be used to identify a majority of malicious web pages. Combining high-interaction client honeypots and this new classification method into a hybrid system leads to significant performance improvements.

Identification of Malicious Web Pages with Static Heuristics

Review: Security threats and solutions in MANETs: A case study using AODV and SAODV

The pervasive interconnection of systems throughout the world has given computer services a significant socioeconomic value that both accidental faults and malicious activity can affect. The classical approach to security has mostly consisted of trying to prevent bad things from happening-by developing systems without vulnerabilities, for example, or by detecting attacks and intrusions and deploying ad hoc countermeasures before any part of the system is damaged. Building an intrusion-tolerant system to arrive at some notion of intrusion-tolerant middleware for application support presents multiple challenges. Surprising as it might seem, intrusion tolerance isn't just another instantiation of accidental fault tolerance

Intrusion-tolerant middleware: the road to automatic security

Client honeypots crawl the Internet to find and identify servers that exploit client-side vulnerabilities. Traditionally, these servers are identified by the client honeypot monitoring state changes that result
from a server interaction. These, so called high-interaction, client honeypots are slow and expensive to operate because they require an entire operating system to be hosted. We have developed a componentbased low-interaction client honeypot that emulates only the essential features of our target clients and that applies signature matching to allow fast static analysis of server responses. Performance measurements of a prototype implementation targeting clients using the HTTP 1.1 protocol indicate that low-interaction client honeypots are faster and cheaper than high-interaction client honeypots. The difference in false negatives
suggests that these technologies may be complementary rather than competitive in nature.

Ian Welch

Papers

Reliability in wireless sensor networks

Identification of Malicious Web Pages with Static Heuristics

Review: Security threats and solutions in MANETs: A case study using AODV and SAODV

Intrusion-tolerant middleware: the road to automatic security

HoneyC - The low-interaction client honeypot