http://repository.iiitd.edu.in/xmlui/handle/123456789/515 2026-04-10T20:07:54Z http://repository.iiitd.edu.in/xmlui/handle/123456789/614 Citadels in cyberspace Rawat, Madhur; Chakravarty, Sambuddho (Advisor) Cyberwarfare remains a sparsely explored domain of cybersecurity research, most often involving targeted attacks by one nation against another, using botnets. These botnets use malware to launch various kinds of attacks against their targets {ranging from exploiting vulnerabilities, launching Distributed Denial of Service (DDoS) attacks, to various forms of traffic interception attacks. A powerful nation could use network cartography based techniques to identify key locations within its own nation, where it could install defenders that involve interception of illegitimate traffic. More specifically, the government may use network tomography to identify a relatively small number of Autonomous Systems (ASes) such that they can intercept the large fraction of network paths (and potentially a large fraction of network traffic). In our research, we use network tomography to construct such large-scale network maps which could be used to identify Cyber Defense Line (viz., collection of strategically important ASes that intercept all the network paths of the country) for installing defenders to prevent various kinds of targeted attacks (like DDoS). These defenders would intercept traffic of large fraction of users based on their location, intercepting large fraction of network traffic. We study how well these defenders can prevent the attacker from crippling the critical networked services, such as financial institutions, defence sites etc. based on their networked locations. For our analysis, we selected 9 different countries (including China and India) and found \Cyber defence line" for aforementioned network services, DNS infrastructure and for full country net-work map. We found that, countries are significantly similar in network structures viz., all have hierarchical structure. For all sample countries, we found that handful ASes, intercept more than _ 90% of all intra country AS paths. For example, in India only 4 ASes capture more than 95% of the network paths. Interestingly, this holds true, if we select ASes based on different AS properties (like customer degree, cone size, and peer degree etc.) Finding cuts in country's AS topology is only meaningful, when one aims for intercepting 100% paths by the cut. Our results reveal that, for majority of our sample countries, all boundary ASes (that have peering relationship with foreign ASes of the country) capture more than 99% paths, whereas for 100% paths interception we require considerably very large number of ASes (for example, in China 9 ASes intercepts over 90% of the paths, 90 ASes for 99% of paths and 213 ASes for 100% paths). 2017-06-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/604 Action recognition in egocentric videos Verma, Sagar; Arora, Chetan (Advisor) With an increase in usage and availability of wearable devices like GoPro, Microsoft Hololens, Google Glass, etc, egocentric video analysis has become essential. An interesting application is action recognition in egocentric videos. Research has been performed on action recognition in the first person (egocentric) videos. First person action recognition is a hard problem given that first person videos are shaky, have limited hand-object interaction, and have limited publicly available datasets. Most of the existing research uses hand-crafted features to learn actions which work best for a given domain. First person videos have two types of actions. First, where hand-object interactions are present and the other one, where no such interactions are present. Current methods can only be used to recognize any one type of action but not both using a single method. This research proposes a novel action recognition method to recognize two types of actions, one where hand-object interaction is present and other where no such interactions are present. Further, a new dataset named IIITD Plumbing dataset is introduced which provides a large number of videos, objects, and actions. The proposed system makes use of spatio-temporal information captured from raw frames. We also introduce a new method to perform activity recognition that learns grammar from learned actions. 2017-07-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/542 Multiple valued current mode logic circuits Tarun, Kunwar; Hashmi, Mohammad S. (Advisor) The circuits having more than two logic levels called as multiple valued circuits have the potential of reducing area by reducing the on chip interconnection. Despite considerable effort, designing a system for processing a multiple valued signal is still a complicated task. Multiple valued circuits can be realized in voltage or current mode. Due to limited power supply, higher radix valued system is not feasible to design using voltage mode configuration. On the other hand, current mode circuits have the capability of scaling, copying, inverting using basic current mirror structure. The non self restoring nature and higher static power dissipation is the major problem in multiple valued current mode circuits. Self restoration circuits need to be developed for correct detectable output. In this study, performance of various fundamental current mode multiple valued operator is analyzed across different process corner and over wide temperature range. Voltage mode binary to current mode multiple valued encoding and current mode multiple valued to voltage mode binary decoding are presented here. Several combinational circuits such as Multiplexer, Demultiplexer and Full adder are proposed and discussed. Further sequential circuits such as Latch, D Flip-Flop, counters and arbitrary selected state diagram are presented here. Finally, 2-bit binary parallel adder and 1-digit quaternary full adder is compared in terms of various VLSI design criteria. 2017-05-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/541 Visual tracking using analysis dictionary learning Sitani, Divya (Advisor); Subramanyam, A V (Advisor); Majumdar, Angshul (Advisor) Visual tracking or object tracking is the process of estimating the state of the target in successive frames of a video sequence. It is an integral part of a plethora of applications like security, surveillance, navigation systems, traffic monitoring systems, human computer interaction systems, and robotics where the target is tracked in both stationary as well as dynamic environments. Visual tracking has remained a challenging problem in computer vision because of numerous factors like occlusion, illumination variation, background clutter, pose change, scale variation, deformation, etc. To overcome these challenges we propose an online analysis dictionary learning framework for visual tracking. Dictionary learning is a popular representation learning tool today. It has been successfully applied to a wide range of computer vision tasks like image denoising, image super resolution, face recognition, human action recognition, classification, etc. in the recent years. Synthesis dictionary based learning approaches have been applied to visual tracking as well. However, to the best of our knowledge, the use of analysis dictionary based algorithms for visual tracking has not been done yet. The main advantage of an analysis dictionary over a synthesis dictionary is, for an analysis and a synthesis dictionary of same dimensions, an analysis dictionary is able to capture significantly more variability in the data compared to a synthesis dictionary. We have developed our algorithm in two stages. In this first stage we track the targets in video sequences using a single analysis dictionary. In the next stage we develop a multiple analysis dictionaries model to track the object of interest. After extensive experimentation on video sequences from OTB-50 dataset, we have demonstrated that our algorithm works better than the synthesis dictionary learning based trackers and also some of the other state of the art trackers that do not incorporate dictionary learning in their tracking approach. 2017-08-01T00:00:00Z