Browsing by Author "Sastry, V V S S"

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    Command agents with human-like decision making strategies
    (2010-02-23T17:03:06Z) Raza, Masood; Sastry, V V S S
    Human behaviour representation in military simulations is not sufficiently realistic, specially the decision making by synthetic military commanders. The decision making process lacks realistic representation of variability, flexibility, and adaptability exhibited by a single entity across various episodes. It is hypothesized that a widely accepted naturalistic decision model, suitable for military or other domains with high stakes, time stress, dynamic and uncertain environments, based on an equally tested cognitive architecture can address some of these deficiencies. And therefore, we have developed a computer implementation of Recognition Primed Decision Making (RPD) model using Soar cognitive architecture and it is referred to as RPD-Soar agent in this report. Due to the ability of the RPD-Soar agent to mentally simulate applicable courses of action it is possible for the agent to handle new situations very effectively using its prior knowledge. The proposed implementation is evaluated using prototypical scenarios arising in command decision making in tactical situations. These experiments are aimed at testing the RPD-Soar agent in recognising a situation in a changing context, changing its decision making strategy with experience, behavioural variability within and across individuals, and learning. The results clearly demonstrate the ability of the model to improve realism in representing human decision making behaviour by exhibiting the ability to recognise a situation in a changing context, handle new situations effectively, flexibility in the decision making process, variability within and across individuals, and adaptability. The observed variability in the implemented model is due to the ability of the agent to select a course of action from reasonable but some times sub-optimal choices available. RPD-Soar agent adapts by using ‘chunking’ process which is a form of explanation based learning provided by Soar architecture. The agent adapts to enhance its experience and thus improve its efficiency to represent expertise.
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    The detection and prevention of Malware attacks on android mobile through the application of artificial intelligence techniques
    (2021-09) Ashawa, Moses Aprofin; Morris, S; Sastry, V V S S
    Our everyday lives are integrated with the use of mobile devices which store sensitive data. Sensitive data stored on smartphones attract different threats including malware. Among mobile platforms, Android is the most popular OS with malware targeting sensitive information and other mobile services. If malware infects a digital device, then it has control over the device's functionality and data. This can impact your finances, your privacy, and your access to your data. Malware is a threat not only to individuals but also to corporate organisations and financial institutions as well. This could lead to communication traffic of an infected network, hardware failure of the physical device, data theft, and loss of critical business data, among others. There are existing detection techniques for identifying Android malware. However, these techniques are limited in detecting evolving and sophisticated malware which use permission features as attack vectors in a smart fashion to infect Android mobile devices. To improve malware detection accuracy based on the related problem, we developed techniques for identifying Android-based malicious applications. To achieve this, the author presents a thorough review of the mobile malware evolution and infection strategies. The second part of the survey covers Android mobile malware detection, classification, and analysis techniques where the author identifies their efficacy in detecting evolving malware and their limitations. The author identifies through the review research gaps which open unto the development of different and novel solutions for Android malware classification and analysis. We leveraged the existing strengths of the previous methods to develop a robust novel automated framework to classify and analyse Android malware based on permission features. Classification accuracy of 97% was achieved with our framework with a False Positive Rate of 3%. Our techniques identified privileges that malware exploits as attack vectors to infect Android-based devices. The results demonstrate that our framework has high feature diversity capabilities for Android malware classification. We identified that there are permissions with similar attributes that are correlated and can trigger the installation of similar permissions with the same threat level especially. However, these prevention techniques are not tested on other mobile platforms' data and do not focus on mitigating pileup susceptibilities. Finally, we believe that as the results of this research are being made public and cited by organizations and individuals, the outcome of this will influence the security and social policies that mobile companies will implement based on some of the recommendations by our findings.
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    On recognition of gestures arIsing in flight deck officer (FDO) training
    (2011-01-11) Turan, D; Sastry, V V S S
    This thesis presents an on-line recognition machine RM for the continuous and isolated recognition of dynamic and static gestures that arise in Flight Deck Officer (FDO) training. This thesis considers 18 distinct and commonly used dynamic and static gestures of FDO. Tracker and computer vision based systems are used to acquire the gestures. The recognition machine is based on the generic pattern recognition framework. The gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of the gestures via dynamic programming and Markovian process. The algorithm predicts the correspond-ing index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. Having estimated Score, some heuristics are employed to control the declaration in the final stages. The recognition machine addresses general gesture recognition issues: to recognize real time and dynamic gesture, no starting/end point and inter-intra personal tem-poral and spatial variance. The first two issues and temporal variance are addressed by the proposed algorithm. The spatial invariance is addressed by introducing inde-pendent units to construct gesture models. An important aspect of the algorithm is that it provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. The algorithm has the additional advantage of providing timely feedback for training purposes. In this thesis, we consider isolated and continuous gestures. The performance of RM is evaluated using six datasets - artificial (W_TTest), hand motion (Yang, Perrotta), Gesture Panel and FDO (tracker, vision). The Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) are used to compare RM's results. Various data analyses techniques are deployed to reveal the complexity and inter similarity of the datasets before experiments are conducted. In the isolated recogni-tion experiments, the recognition machine obtains comparable results with HMM and outperforms DTW. In the continuous experiments, RM surpasses HMM in terms of sentence and word recognition. In addition to these experiments, a multilayer per-ceptron neural network (MLPNN) is introduced for the prediction process of RM to validate modularity of RM. The overall conclusion of the thesis is that, RM achieves comparable results which are in agreement with HMM and DTW. Furthermore, the recognition machine pro-vides more reliable and accurate recognition in the case of missing and noisy data. The recognition machine addresses some common limitations of these algorithms and general temporal pattern recognition in the context of FDO training. The recognition algorithm is thus suited for on-line recognition.
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    On the derivation and analysis of decision architectures for unmanned aircraft systems
    (2013-10-08) Patchett, C H; Sastry, V V S S
    Operation of Unmanned Air Vehicles (UAVs) has increased significantly over the past few years. However, routine operation in non-segregated airspace remains a challenge, primarily due to nature of the environment and restrictions and challenges that accompany this. Currently, tight human control is envisaged as a means to achieve the oft quoted requirements of transparency , equivalence and safety. However, the problems of high cost of human operation, potential communication losses and operator remoteness remain as obstacles. One means of overcoming these obstacles is to devolve authority, from the ground controller to an on-board system able to understand its situation and make appropriate decisions when authorised. Such an on-board system is known as an Autonomous System. The nature of the autonomous system, how it should be designed, when and how authority should be transferred and in what context can they be allowed to control the vehicle are the general motivation for this study. To do this, the system must overcome the negative aspects of differentiators that exist between UASs and manned aircraft and introduce methods to achieve required increases in the levels of versatility, cost, safety and performance. The general thesis of this work is that the role and responsibility of an airborne autonomous system are sufficiently different from those of other conventionally controlled manned and unmanned systems to require a different architectural approach. Such a different architecture will also have additional requirements placed upon it in order to demonstrate acceptable levels of Transparency, Equivalence and Safety. The architecture for the system is developed from an analysis of the basic requirements and adapted from a consideration of other, suitable candidates for effective control of the vehicle under devolved authority. The best practices for airborne systems in general are identified and amalgamated with established principles and approaches of robotics and intelligent agents. From this, a decision architecture, capable of interacting with external human agencies such as the UAS Commander and Air Traffic Controllers, is proposed in detail. This architecture has been implemented and a number of further lessons can be drawn from this. In order to understand in detail the system safety requirements, an analysis of manned and unmanned aircraft accidents is made. Particular interest is given to the type of control moding of current unmanned aircraft in order to make a comparison, and prediction, with accidents likely to be caused by autonomously controlled vehicles. The effect of pilot remoteness on the accident rate is studied and a new classification of this remoteness is identified as a major contributor to accidents A preliminary Bayesian model for unmanned aircraft accidents is developed and results and predictions are made as an output of this model. From the accident analysis and modelling, strategies to improve UAS safety are identified. Detailed implementations within these strategies are analysed and a proposal for more advanced Human-Machine Interaction made. In particular, detailed analysis is given on exemplar scenarios that a UAS may encounter. These are: Sense and Avoid , Mission Management Failure, Take Off/Landing, and Lost Link procedures and Communications Failure. These analyses identify the nature of autonomous, as opposed to automatic, operation and clearly show the benefits to safety of autonomous air vehicle operation, with an identifiable decision architecture, and its relationship with the human controller. From the strategies and detailed analysis of the exemplar scenarios, proposals are made for the improvement of unmanned vehicle safety The incorporation of these proposals into the suggested decision architecture are accompanied by analysis of the levels of benefit that may be expected. These suggest that a level approaching that of conventional manned aircraft is achievable using currently available technologies but with substantial architectural design methodologies than currently fielded.