Estimating the Hausdorff–Besicovitch dimension of boundary of basin of attraction in helicopter trim

Gouravaraju, Saipraneeth; Ganguli, Ranjan

doi:10.1016/j.amc.2012.03.104

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Hausdorff-Besicovitch dimension calculates an exponent of similarity when an object is magnified by a fixed scaling factor multiple times. This similarity exponent is equal to a topological dimension when it is a regular object having no complexity and exceeds the topological dimension when there are irregularities or fractalities [280].…”

Section: Fractal Dimensions To Measure Complexitymentioning

confidence: 99%

A Cognitive and Concurrent Cyber Kill Chain Model

Khan

Siddiqui

Ferens

2017

Computer and Network Security Essentials

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The challenges of cyber security have outpaced the advantages of cyber tools and technologies. In 2018, World Economic Forum has already placed cyber security in the top five risks faced by the world. Cyber threats are evolving and can cripple economies and nations. The major tools of cyber threats are anonymity, deception and uncertainty. Current state of the art research is also evolving into addressing these challenges by applying new and proactive threat hunting approaches instead of doing reactive cyber defense, which is proving futile. Malware is an indispensable tool of cyber threat actors to accomplish malicious activities i.e. exfiltration, espionage and disruption. Using advanced obfuscation and mutation methods, malware adversaries are able to remain ahead of cyber defenders. Most malware detection technologies are based on finding a-priori known signatures of malware payload or known patterns of malware behavior. This dissertation addresses the challenge of hunting unknown behaviorally mutated malware inside a host computer by proposing a proof of concept framework named Malvidence for characterizing malware behavior within a host operating system process tree using cognitive machine intelligence. Using Malvidence framework, tools and techniques can be derived for variety of cyber security methods for threat detection. Cognitive Computing is a promising domain of machine intelligence which explores and develops new tools to incorporate human cognitive characteristics so that the performance of existing domain of artificial intelligence and machine learning can be improved. Therefore, cognitive complexity based fractal analysis is demonstrated and a methodology of extracting inherent but hidden patterns of malware dynamics using a temporal graph theoretical approach is proposed. Further, a set of graph theoretical features is analyzed and proposed for an effective characterization of malware behavior which can be subsequently used for malware hunting and detection. In addition, the proposed features are tested for their mathematical validity. Finally, using proposed cognitive complexity analysis, characterization performance of an unsupervised clustering algorithm is provided to demonstrate the validity of Malvidence framework.

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Section: Fractal Dimensions To Measure Complexitymentioning

confidence: 99%

A Cognitive and Concurrent Cyber Kill Chain Model

Khan

Siddiqui

Ferens

2017

Computer and Network Security Essentials

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“…Hausdorff-Besicovitch dimension calculates an exponent of similarity when an object is magnified by a fixed scale factor multiple times. This similarity exponent is equal to a topological dimension when it is a regular object having no complexity and exceeds the topological dimension when there are irregularities or fractalities (Gouravaraju, S. & Ganguli, R., 2012).…”

Section: Cognitive Computing and Complexitymentioning

confidence: 99%

Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series

Khan

Ferens

Kinsner

2015

International Journal of Software Science and Computational Intelligence

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Growing global dependence over cyberspace has given rise to intelligent malicious threats due to increasing network complexities, inherent vulnerabilities embedded within the software and the limitations of existing cyber security systems to name a few. Malicious cyber actors exploit these vulnerabilities to carry out financial fraud, steal intellectual property and disrupt the delivery of essential online services. Unlike physical security, cyberspace is very difficult to secure due to the replacement of traditional computing platforms with sophisticated cloud computing and virtualization. These complex systems exhibit an increasing degree of complexity in tracking an attack or monitoring possible threats which is becoming intractable with the existing security firewalls and intrusion detection systems. In this paper, authors present a novel complexity detection technique using generalized multifractal singularity spectrum which is able to not only capture the growing complexity of the internet time series but also distinguishes the presence of an attack accurately.

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“…At the same time, the transport properties of the heater affect the extent of the thermal interaction among the cavities, causing the activation and deactivation of individual cavities. [49][50][51][52][53][54][55]58,59] Xiao and Yu [18] have obtained the fractal analytical ex-pressions of nucleate boiling heat transfer Q l,c from the base fluid in the CHF region. The CHF of pool boiling can be expressed as…”

Section: Formulation Of the Proposed Modelmentioning

confidence: 99%

Prediction of heat transfer of nanofluid on critical heat flux based on fractal geometry

Xiao¹

2013

Chinese Phys. B

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Analytical expressions for nucleate pool boiling heat transfer of nanofluid in the critical heat flux (CHF) region are derived taking into account the effect of nanoparticles moving in liquid based on the fractal geometry theory. The proposed fractal model for the CHF of nanofluid is explicitly related to the average diameter of the nanoparticles, the volumetric nanoparticle concentration, the thermal conductivity of nanoparticles, the fractal dimension of nanoparticles, the fractal dimension of active cavities on the heated surfaces, the temperature, and the properties of the fluid. It is found that the CHF of nanofluid decreases with the increase of the average diameter of nanoparticles. Each parameter of the proposed formulas on CHF has a clear physical meaning. The model predictions are compared with the existing experimental data, and a good agreement between the model predictions and experimental data is found. The validity of the present model is thus verified. The proposed fractal model can reveal the mechanism of heat transfer in nanofluid.

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Estimating the Hausdorff–Besicovitch dimension of boundary of basin of attraction in helicopter trim

Cited by 9 publications

References 30 publications

A Cognitive and Concurrent Cyber Kill Chain Model

A Cognitive and Concurrent Cyber Kill Chain Model

Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series

Prediction of heat transfer of nanofluid on critical heat flux based on fractal geometry

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