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A Panoramic View of Cyber Attack Detection and Prevention Using Machine Learning and Deep Learning Approaches
Published in Pethuru Raj Chelliah, Usha Sakthivel, Nagarajan Susila, Applied Learning Algorithms for Intelligent IoT, 2021
Esther Daniel, N. Susila, S. Durga
Linux-based Malware or malicious software is used to harm the host operating system or to steal the sensitive data of the user, organization, and companies. It helps to gather the information illegally. Malware detection is used to detect the presence of malware on a host operating system or used to detect any malicious activity in the system. Malware detection is mandatory for the system to not lose the sensitive information of any user. Malware detection is performed using different techniques but in this survey, we are using machine learning algorithms to detect the malware on Linux operating systems. Nowadays malware is a great problem for personal data. Many approaches are used to identify the malicious executable linkable files (ELF). Linux operating system is open-source software and released under General Public License(GNL).
Optimal Simultaneous Multisurface and Multiobject Image Segmentation
Published in Olivier Lézoray, Leo Grady, Image Processing and Analysis with Graphs, 2012
Xiaodong Wu, Mona K. Garvin, Milan Sonka
When computing ELF for a computer generated 3D triangulated surface, the surface is composed of a limited number of vertices that are usually not uniformly distributed. These two observations greatly reduce the effect of charges located in close proximity. To cope with this undesirable effect, a positive charge Qi is assigned to each vertex υi. The value of Qi is determined by the area sum of triangles tj where υi ∈ tj. When changing r2 to rm (m > 2), the nonintersection property still holds. The difference is that more distant vertices will be penalized in ELF computing. Therefore, a slightly larger m will increase the robustness of local ELF computation. Discarding the constant term, the electric field is defined as E^=∑i∑jAREA(tj)rimr^i,
Design of Low-Power Processor Cores Using a Retargetable Tool Flow
Published in Christian Piguet, Low-Power Processors and Systems on Chips, 2018
Goossens Gert, Dytrych Peter, Lanneer Dirk
Chess. A retargetable C compiler that translates C source code into machine code for the target processor. Different from conventional compilers such as GCC [12], the Chess compiler uses graph-based modeling and optimization techniques [15], to deliver highly optimized code for specialized architectures exhibiting peculiarities such as complex instruction pipelines, heterogeneous register structures, specialized functional units, and instruction-level parallelism. Chess produces machine code in the Elf object file format, with source-level debug information in the Dwarf 2.0 format.
A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system
Published in Enterprise Information Systems, 2023
Akshat Gaurav, Brij B. Gupta, Prabin Kumar Panigrahi
In the IoT market, different manufacturers support their CPU architecture, so many processor architectures like MIPS, PowerPC, SPARC, etc., are available in the market. However, this diversity makes malware detection difficult because the algorithm that works on one processor may not work on another processor. In this context, Vasan et al. (2020) proposed a cross-architecture malware detection technology that works on the majority of IoT architectures. The proposed approach uses IG and dictionary-based methods for feature selection and 1DCNN to train the dataset. Apart from the dictionary-based method, there is another method in which the features are extracted from the binary files and OpCodes (Tien et al. 2020). In this proposed approach, the main focus is on the information present in the executable linkage format (ELF) (Executable 2020). As the malware detection process depends on the features of the binary files, it is independent of the OS platform and can detect different types of malware attacks. Table 2 represents different static malware detection methods.
On the Effectiveness of Image Processing Based Malware Detection Techniques
Published in Cybernetics and Systems, 2022
Binary executable files have specific formats, such as Portable Executable (PE), Executable, Linkable Format (ELF), and Mac Object (Mac-O), associated with each operating system Windows, Linux, and Mac OS, respectively. As we all know, computers running the Windows operating system are the most vulnerable to malware attacks, so we now realize that it is crucial to pay special attention to PE executables.