Computer communications

Indian script character recognition: a survey

Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda.

Copyright protection for the electronic distribution of text documents : Identification and protection of multimedia information

Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions.

A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns

A review On reinforcement learning: Introduction and applications in industrial process control

Telugu is the language spoken by more than 100 million people of South India. Telugu has a complex orthography with a large number of distinct character shapes (estimated to be of the order of 10,000) composed of simple and compound characters formed from 16 vowels (called achchus) and 36 consonants (called hallus). We present an efficient and practical approach to Telugu OCR which limits the number of templates to be recognized to just 370, avoiding issues of classifier design for thousands of shapes or very complex glyph segmentation. A compositional approach using connected components and fringe distance template matching was tested to give a raw OCR accuracy of about 92%. Several experiments across varying fonts and resolutions showed the approach to be satisfactory.

An OCR system for Telugu

Internet of Things (IoT) is fast becoming a disruptive technology business opportunity, with standards emerging primarily for wireless communication between sensors, actuators and gadgets in day-to-day human life, all in general being referred to as “Things”. This offers the capability to measure for understanding environment indicators. This paper addresses the internet of things (IoT) as the main enabling factor of promising paradigm for integration and comprehensive of several technologies for communication solution, Identification and integrating for tracking of technologies as wireless sensor and actuators. IoT as envisioned is billion sensors connected to the internet through the sensors that would be generate large amount of data which need to analyzed, interpreted and utilized. Context aware capturing enables modeling, interpreting and storing of sensor data which is linked to appropriate context variable dynamically. Building or home automation, social smart communication for enhancement of quality of life, that could be considered as one of the application of IoT where the sensors, actuators and controllers can be connected to internet and controlled. This paper introduces the concept of application for internet of things and with the discussion of social and governance issues that arise as the future vision of internet of things.

Principle application and vision in Internet of Things (IoT)

Similarity or distance between two objects plays a fundamental role in many data mining tasks like classification and clustering. Categorical data, unlike numeric data, conceptually is deficient of default ordering relations on the attribute values. This makes the task of devising similarity or distance metrics and data mining tasks such as classification and clustering of categorical data more challenging. In this paper we formulate a taxonomy of various distance or similarity measures used in conjunction with data whose attributes are categorical. We categorize the existing measures into two broad classes, namely, Context-free and Context-sensitive measures for categorical data. In addition, we suggest a taxonomy of the clustering approaches for categorical data. We also propose a hybrid approach for measuring similarity between objects. We make a relative comparison of the strengths and weaknesses of some of the similarity measures and point out future research directions.

A survey of distance/similarity measures for categorical data

In this work we use Machine Learning (ML) techniques to learn the CPU time-slice utilization behavior of known programs in a Linux system. Learning is done by an analysis of certain static and dynamic attributes of the processes while they are being run. Our objective was to discover the most important static and dynamic attributes of the processes that can help best in prediction of CPU burst times which minimize the process TaT (Turn-around-Time). In our experimentation we modify the Linux Kernel scheduler (version 2.4.20-8) to allow scheduling with customized time slices. The "Waikato Environment for Knowledge Analysis" (Weka), an open source machine-learning tool is used to find the most suitable ML method to characterize our programs. We experimentally find that the C'4.5 Decision Tree algorithm most effectively solved the problem. We find that predictive scheduling could reduce TaT in the range of 1.4% to 5.8%. This was due to a reduction in the number of context switches needed to complete the process execution. We find our result interesting in the context that generally operating systems presently never make use of a program's previous execution history in their scheduling behavior.

/pdf/applying-machine-learning-techniques-to-improve-linux-1i7h049709.pdf

Applying Machine Learning Techniques to Improve Linux Process Scheduling

Wireless sensor networks (WSNs) are usually deployed in insecure remote and open environments. Nodes could be attacked and compromised. The attacker can use compromised sensor nodes to disrupt communications or inject misleading sensor values. Misleading data can corrupt the entire decision making process, which depends upon WSN data. We propose a trust mechanism, which evaluates communication trust and data trust for WSNs. Communication trust is computed from direct and indirect observations of the neighbour’s forwarding behavior. Direct trust is derived from the consistency of forwarding behavior. Indirect trust is derived from the neighbour’s observations translated into recommendations. We use weighted Dempster–Shaffer (D–S) theory to compute indirect trust. Data trust is computed by using median of sensor data. We present arguments and simulation results to show the effectiveness of the proposed algorithm against packet forwarding/modification attacks, bad mouthing attacks, collusion attacks, and on–off attacks. Our proposed trust model helps data in WSNs to be more secure.

Atul Negi

Papers

An OCR system for Telugu

Principle application and vision in Internet of Things (IoT)

A survey of distance/similarity measures for categorical data

Applying Machine Learning Techniques to Improve Linux Process Scheduling

Communication and Data Trust for Wireless Sensor Networks Using D–S Theory