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

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The rise of big data on cloud computing

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A Break in the Clouds: Towards a Cloud Definition

Power Aware Routing in Mobile Ad Hoc Networks

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

The Internet of Things is a truly state-of-the-art field that has the potential to significantly improve essential services like power saving [8], security, maintenance and monitoring. The implementation of analytics and corrective feedback processes in the event of anomalous data that needs addressing — such as a sharp temperature increase detected by the sensor in a room that is typically maintained at a low temperature, the air-conditioning running for a long time in a room that has no occupant detected, excessive noise detected by a sound sensor — among other things, are some of several ways in which the Internet of Things can be leveraged to help solve fundamental problems in various walks of life. We provide information on setting up and operating a comprehensive apparatus to accomplish these objectives — starting with setting up the desired test bed that has a resilient Data-Flow Path, retrieving the requisite values through the sensors[9], performing analytics on the data using RStudio to gain further insights on the relationship between the various parameters being measured and help set up a predictive model that could draw a contrast between the expected and actual values, thereby suggesting the extent of wastage of resources. Additionally, the GSM module of the Intel Galileo board is also usefully deployed to send out emergency alerts.

Energy efficient IoT framework for Smart Buildings

This work studies the problem of optimal positioning of Intrusion Detection Systems (IDSs) in a Peer-to-Peer (P2P) environment involving a number of peers and super-peers. This scenario applies to network architectures like that of Gnutella, Skype or Tor, which involve a huge number of leaf-peers and a selected number of super-peers who have higher responsibilities in the network. A malicious entity may become part of the P2P network by joining from any part of the network. It can attack a super-peer and thus disrupt the functioning of the P2P network. Peers may try to secure the network by running IDSs at certain strategically-chosen locations in the network. But a deterministic schedule of running and positioning the IDSs can be observed and thwarted by an adversary. In this paper, we explore the problem of strategically positioning IDSs in a P2P network with a randomized, game-theoretic approach. Our approach distributes the responsibility of running the IDSs between the peers in a randomized fashion and minimizes the probability of a successful attack.

Game-theoretic strategies for IDS deployment in peer-to-peer networks

The performance of spectrum sensing is limited by noise uncertainty, multipath fading, and shadowing, which are the fundamental characteristics of wireless channels If the Primary User (PU) signal experiences deep fading or blocked by obstacles, the power of the received PU signal at the Secondary User (SU) may be too weak to be detected If the SU transmitter cannot detect the presence of the PU transmitter while the PU receiver is within the transmission range of the SU, the transmission of the PU will be interfered To address this problem, CSS has been shown to be an effective method to improve the detection performance by exploiting spatial diversity In this paper, we investigate the optimality of cooperation scheme using coalitional game theory The cognitive behavior ie observe, learn and act can be modelled effectively in game theory to study, model and analyze the strategic interactions among Cognitive Radio (CR) users This paper describes the analytical insights of coalitional game theory for CSS in cognitive radio, highlighting techniques for identifying steady states, convergence and steady state optimality We critically assess the techniques available in the literature, and then describe our ongoing work applying our insights to algorithm design for cognitive radios using Game theory

Efficient cooperative spectrum sensing in Cognitive Radio using coalitional game model

Scalability and efficient global search in unstructured peer-to-peer overlays have been extensively studied in the literature. The global search comes at the expense of local interactions between peers. Most of the unstructured peer-to-peer overlays do not provide any performance guarantee. In this work we propose a novel Quality of Service enabled lookup for unstructured peer-to-peer overlays that will allow the user's query to traverse only those overlay links which satisfy the given constraints. Additionally, it also improves the scalability by judiciously using the overlay resources. Our approach selectively forwards the queries using QoS metrics like latency, bandwidth, and overlay link status so as to ensure improved performance in a scenario where the degree of peer joins and leaves are high. User is given only those results which can be downloaded with the given constraints. Also, the protocol aims at minimizing the message overhead over the overlay network.

Adaptive Lookup for Unstructured Peer-to-Peer Overlays

There are attacks on or using an SSH server – SSH port scanning, SSH brute-force attack, and attack using a compromised server. Attacks using a server could be DoS attack, Phishing attack, E-mail spamming and so on. Sometimes an attacker breaks into a public SSH server and uses it for the above activities. Mostly, it is hard to detect the compromised SSH servers that were used by the attackers. However, by analysing the system logs an organisation can know about the compromises. For an organisation holding several SSH servers, it would be tedious to analyse the log files manually.Also, high-speed networks demand better mechanisms to detect the compromises. In this paper, we detect a compromised SSH session that is carrying out malicious activities. We use flow-based approach and machine learning techniques to detect a compromised session. In a flow-based approach, individual packets are not scrutinised. Hence, it works better on a high-speed network. The data is extracted from a distributed honeypot. The paper also describes the machine learning techniques with appropriate parameters and feature selection technique. A real-time detection model that is tested on a public server is also Journal of Software Networking, 79–100. doi: 10.13052/jsn2445-9739.2017.005 c © 2017 River Publishers. All rights reserved. 80 G. K. Sadasivam et al. presented. Several analyses proved that J48 decision tree algorithm and the PART algorithm are best suited for detection of SSH compromises. It was inferred that inter-arrival time between packets and the size of a packet payload play a significant role in detecting compromises.

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Chittaranjan Hota

Papers

Energy efficient IoT framework for Smart Buildings

Game-theoretic strategies for IDS deployment in peer-to-peer networks

Efficient cooperative spectrum sensing in Cognitive Radio using coalitional game model

Adaptive Lookup for Unstructured Peer-to-Peer Overlays

Detection of Severe SSH Attacks Using Honeypot Servers and Machine Learning Techniques