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Malware Detection and Mitigation
Published in Nicholas Kolokotronis, Stavros Shiaeles, Cyber-Security Threats, Actors, and Dynamic Mitigation, 2021
Gueltoum Bendiab, Stavros Shiaeles, Nick Savage
Other works focus on the classification of malware by families or similarity based on the assumption that malware variants belonging to the same family must have similar binary patterns that can be used in detecting malware variants and classifying families. This helps to significantly reduce the number of samples that need time-consuming manual analysis. They first transform the suspicious binary files to images with the majority utilizing grayscale image [17]. Then, similar images are visually classified using algorithms from the areas of image processing (e.g. graph entropy [38], image matrices, and image texture analysis, etc.), computer vision, and ML. For instance, the Computer Science Laboratory [39] proposed a static approach for malware detection and classification using images. First, the malware binary is converted to an image, then a texture-based feature is computed on the image to characterize the malware. This approach is resilient to packing techniques and enables security analytics to visually characterize and classify the malware samples. Another method for malware detection [40] extracts unique opcodes from the binary file and converts them into digital image. Then, visual features are extracted from the output image using the texture extraction method Local Binary Pattern (LBP) [41].
Atom-Bond Connectivity Index
Published in Mihai V. Putz, New Frontiers in Nanochemistry, 2020
A topological index is any function calculated based on a molecular graph (i.e., a connected graph with maximum vertex degree at most 4 and whose graphical representation may resemble a structural formula of a molecule), irrespective of the labeling of its vertices. Hundreds of topological indices have been introduced and studied, starting with the seminal work by Wiener (1947a,b), in which he used the sum of all shortest-path distances of a (molecular) graph for modeling physical properties of alkanes. Topological indices can be divided into various categories such as graph entropy (Bonchev, 1983; Dehmer, 2008; Dehmer et al., 2009; Dehmer & Mowshowitz, 2011) representing information-theoretic indices, eigenvalue-based measures (Randić et al., 2001; Estrada, 2002; Dehmer et al., 2012b), distance-based measures (Balaban, 1982; Khalifeh et al., 2009; De Corato et al., 2012; Putz et al., 2013; Alizadeh et al., 2013; 2014, Xu et al., 2014; Azari & Iranmanesh, 2013a, 2015a; Azari et al., 2016), symmetry-based descriptors (Todeschini & Consonni, 2000), and degree-based invariants (Nikolić et al., 2003; Vukičević & Gašperov, 2010; Furtula et al., 2013; Gutman, 2013; Gutman & Tošović, 2013; Zhong & Xu, 2014; Azari & Iranmanesh, 2011, 2013b, 2015, 2015b; Azari, 2014; Falahati-Nezhad et al., 2015).
Multiscale Rotation Invariant Local Features Extraction For Hyperspectral Image Classification Using Cnn
Published in B. K. Mishra, Samarjeet Borah, Hemant Kasturiwale, Computing and Communications Engineering in Real-Time Application Development, 2023
Sujata Alegavi, R. R. Sedamkar
In the above graph, entropy value in angle dataset is increasing, whereas resolution values are varying (Table 8.1; Figure 8.2). The value of 64 × 64 resolutions is same in all datasets. Maximum entropy value is nearest to 6.7, which is the fusion of 270° × 512 resolution. The average value is nearest to 6.5 which is the same for 512 and 256 resolution in all datasets (Fig. 8.3).
Topological study on degree based molecular descriptors of fullerene cages
Published in Molecular Physics, 2023
Tony Augustine, S. Roy, J. Sahaya Vijay, Jain Maria Thomas, P. Shanmugam
Graph entropy was created to describe the complexity of graphs. Entropy was initially used for communication and information by Shannon [28]. It is possible to interpret the entropy of a probability distribution as both a measure of information and uncertainty. In actuality, the quantity of knowledge we gain from observing an experiment's conclusion can be equated mathematically to the degree of ambiguity around the experiment's outcome before its execution [29, 30]. It was created to demonstrate the complexity of information transmission and communication. Still, it now has a wide range of useful applications in a range of scientific areas, including physical dissipative structures, biological systems, engineering domains, and others [31, 32]. The two types of graph entropies are deterministic and probabilistic, respectively. The probabilistic category is the focus of this work since it is widely employed in various domains, such as communication and the description of chemical structures. In addition, statistical methods are separated into intrinsic and extrinsic categories. In intrinsic measures, a probability distribution across the subsets of a graph with similar structural similarities is found [33, 34]. Vertices or edges in the graph are given a probability function for extrinsic measurements. This probability distribution function can be transformed into a numerical number to produce probabilistic measures of graph complexity [35].
Two-dimensional coronene fractal structures: topological entropy measures, energetics, NMR and ESR spectroscopic patterns and existence of isentropic structures
Published in Molecular Physics, 2022
Micheal Arockiaraj, Joseph Jency, Jessie Abraham, S. Ruth Julie Kavitha, Krishnan Balasubramanian
The graph entropy is a measure that quantifies the structural information content of the graph-based network topology. As it enumerates the complexity of the framework through amicable evaluation procedures, these entropy-based methods play a vital role in examining problems in various fields including mathematical chemistry, information processing and computational physics [55,56]. Consequently, several approaches have been developed over decades, to define this parameter through local information functionals of the graph [57–61]. Shannon's entropy is one such widely applied graph entropy measure that is obtained by deriving a probability distribution from the suitable vertex partition of the graph [62]. Recently, the structural index parameters are employed to generate these entropies, as they serve as efficient functionals of the information regarding graph topology [41,63,64]. In this study, we have developed graph-entropies for several tessellations of two-dimensional coronoid fractals using their degree-based structural functionals and their structural characterisations were compared among the different-dimensional entropy measures, identifying two types of isentropic structures. Furthermore, we have developed machine learning approaches for the generation of the NMR and ESR spectroscopic signatures of these fractals through combinatorial and graph-theoretical algorithms. We have also developed machine learning methods for the rapid computation of the enthalpy of formations of these fractals. For the isentropic fractals, we have expanded our computations to other distance-based topological indices, graph spectra, spectral patterns and quantum spectral-based indices. Such quantum-based measures for the electronic and shape properties of molecules have been the topic of a few studies [65–68].