arXiv subjects

Pooja Shah

Publications and source records attributed to Pooja Shah.

4 recordsLinked to original sources

arXiv2023

Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack

cs.CR↗

arXiv2022

Optimization of Rocker-Bogie Mechanism using Heuristic Approaches

Optimal locomotion and efficient traversal of extraterrestrial rovers in dynamic terrains and environments is an important problem statement in the field of planetary science and geophysical systems. Designing a superlative and efficient architecture for the suspension mechanism of planetary rovers is a crucial step towards robust rovers. This paper focuses on the Rocker Bogie mechanism, a standard suspension methodology associated with foreign terrains. After scrutinizing the available previous literature and by leveraging various optimization and global minimization algorithms, this paper offers a novel study on mechanical design optimization of a rovers suspension mechanism. This paper presents extensive tests on Simulated Annealing, Genetic Algorithms, Swarm Intelligence techniques, Basin Hoping and Differential Evolution, while thoroughly assessing every related hyper parameter, to find utility driven solutions. We also assess Dual Annealing and subsidiary algorithms for the aforementioned task while maintaining an unbiased testing standpoint for ethical research. Computational efficiency and overall fitness are considered key valedictory parameters for assessing the related algorithms, emphasis is also given to variable input seeds to find the most suitable utility driven strategy. Simulated Annealing was obtained empirically to be the top performing heuristic strategy, with a fitness of 760, which was considerably superior to other algorithms and provided consistent performance across various input seeds and individual performance indicators.

cs.NE↗

arXiv2005

Intrinsic vulnerabilities to mechanical failure in nanoscale films

We use molecular simulations to explore how sample dimensions and interfacial properties impact some generic aspects of the mechanical and structural behavior of nanoconfined materials. Specifically, we calculate the strain-dependent properties of minimum-energy thin-film particle configurations (i.e., inherent structures) confined between attractive, parallel substrates. We examine how the relationship between the transverse strain and the stress tensor (the equation of state of the energy landscape) depends on the properties of the film and substrate. We find that both film thickness and film-substrate attractions influence not only the mechanical properties of thin films, but also the shape and location of the "weak spots" where voids preferentially form in a film as it is strained beyond its point of maximum tensile stress. The sensitivity of weak spots to film properties suggests that nanoscale materials may be intrinsically vulnerabile to specific mechanisms of mechanical failure.

cond-mat.stat-mech↗

arXiv2002

Quasi-saddles of Liquids: Computational Study of a bulk Lennard-Jones system

Quasi-saddles or inherent saddles of the potential energy surface, $U$, of a liquid are defined as configurations which correspond to absolute minima of the pseudo-potential surface, $W =\wf$, as identified by a multi-dimensional minimisation procedure. The sensitivity of statistical properties of inherent saddles to the convergence criteria of the minimisation procedure is investigated using, as a test system, a simple liquid bound by a quadratically shifted Lennard-Jones pair potential with continuous zeroth, first and second derivatives at the cut-off distance. The variation in statistical properties of saddles is studied over a range of error tolerances spanning five orders of magnitude. The largest value of the tolerance corresponds to that used for the unshifted LJ liquids in a previous work (J. Chem. Phys. {\bf 115}, 8784 (2001)). Based on our results, it is clear that there are no qualitative changes in statistical properties of saddles over this range of error tolerances and even the quantitative changes are small. The lowest magnitude eigenvalue, $| ω_0^2|$, of the Hessian is, however, found to be very sensitive to the tolerance; as the tolerance is decreased, $| ω_0^2|$ is found to show an overall decrease. This indicates that if convergence criteria are not strict, absolute or low-lying minima of $W(\br)$ will be diagnosed as having no inflexion directions. The results also show that it is not possible to set up an unambiguous numerical criterion to further classify the quasi-saddles into true saddles which contain no zero curvature, non-translational normal modes and inflexion points which have one or more zero-curvature normal mode directions.

cond-mat↗