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

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

https://publications.uni.lu/bitstream/10993/20596/1/survey_on_SDN.pdf

Software-Defined Networking: A Comprehensive Survey

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

We present a survey of Voice over IP security research. Our goal is to provide a roadmap for researchers seeking to understand existing capabilities and, and to identify gaps in addressing the numerous threats and vulnerabilities present in VoIP systems. We also briefly discuss the implications of our findings with respect to actual vulnerabilities reported in a variety VoIP products.

/pdf/a-survey-of-voice-over-ip-security-research-4ydm132t28.pdf

A Survey of Voice over IP Security Research

Systems and methods for computing data transmission characteristics of a network path are disclosed. In some embodiments, the network path has a sending host, at least one intermediate host, and a receiving host, and the data transmission characteristics are computed based on single-ended measurements performed at the sending host.

Systems and methods for computing data transmission characteristics of a network path based on single-ended measurements

This memo describes the use of the HMAC algorithm [RFC 2104] in conjunction with the RIPEMD-160 algorithm [RIPEMD-160] as an authentication mechanism within the revised IPSEC Encapsulating Security Payload [ESP] and the revised IPSEC Authentication Header [AH]. HMAC with RIPEMD-160 provides data origin authentication and integrity protection.

The Use of HMAC-RIPEMD-160-96 within ESP and AH

Artificial diversity is one method for mitigating the security risks of software monoculture. Introducing diversity increases resilience by obfuscating the system parameters an attacker must control for a successful exploit. We take a different approach to resilience and introduce the concept of Application Communities (AC): collections of independent instances of the same application that cooperatively monitor their execution for flaws and attacks and notify the community when such events are detected.

We propose a set of parameters that define an AC and explore the tradeoffs between the minimal size of an AC, the marginal overhead imposed on each member, and the speed with which new faults are detected. We provide a sketch of both analytical and experimental results that show ACs are feasible for current applications: an AC of 15,000 members can monitor Apache for new faults with a 6% performance degradation for each member

https://academiccommons.columbia.edu/doi/10.7916/D8BR92HP/download

Application communities: using monoculture for dependability

Anonymous communication networks like Tor partially protect the confidentiality of their users' traffic by encrypting all intra-overlay communication. However, when the relayed traffic reaches the boundaries of the overlay network towards its actual destination, the original user traffic is inevitably exposed. At this point, unless end-to-end encryption is used, sensitive user data can be snooped by a malicious or compromised exit node, or by any other rogue network entity on the path towards the actual destination.

We explore the use of decoy traffic for the detection of traffic interception on anonymous proxying systems. Our approach is based on the injection of traffic that exposes bait credentials for decoy services that require user authentication. Our aim is to entice prospective eavesdroppers to access decoy accounts on servers under our control using the intercepted credentials. We have deployed our prototype implementation in the Tor network using decoy IMAP and SMTP servers. During the course of ten months, our system detected ten cases of traffic interception that involved ten different Tor exit nodes. We provide a detailed analysis of the detected incidents, discuss potential improvements to our system, and outline how our approach can be extended for the detection of HTTP session hijacking attacks.

/pdf/detecting-traffic-snooping-in-tor-using-decoys-3k0c552o3d.pdf

Angelos D. Keromytis

Papers

A Survey of Voice over IP Security Research

Systems and methods for computing data transmission characteristics of a network path based on single-ended measurements

The Use of HMAC-RIPEMD-160-96 within ESP and AH

Application communities: using monoculture for dependability

Detecting traffic snooping in tor using decoys