Название: Malware: Detection and DefenseАвтор: Eduard Babulak
Издательство: ITexLi
Год: 2023
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
This book presents a collection of selected papers addressing malware detection, which is necessary to create reliable and resilient cyber and computer security mechanisms.
Cyber security providers are facing a continuous stream of new, sophisticated cyberattacks on cyber critical infrastructures worldwide. These cyberattacks are often triggered by malware and ransomware.
In today’s cyber security landscape, companies are facing increasing pressure to protect their data and systems from malicious attackers. As a result, there has been a significant rise in the number of security solutions that can identify malware. But how do you know if an image file is infected with malware? How can you prevent it from running? This blog post covers everything you need to know about malware in your images and how to prevent them from running. The malware will allow the attacker or un-legitimate user to enter the system without being recognized as a valid user. In this paper, we will look at how malware can hide within images and transfer between computers in the background of any system. In addition, we will describe how deep transfer learning can detect malware hidden beneath images in this paper. In addition, we will compare multiple kernel models for detecting malicious images. We also highly suggest which model should be used by the system for detecting malware. We used Keras Framework with TensorFlow in back-end, this about for Deep Learning libraries. For manipulate and process our images, we use Pandas and Scikit-leaning libraries. All experiments ran using Python 3.7 with notebook IDE.
Ransomware refers to a type of malware that encrypts files on an infected computer and holds the key to decrypt the files until the victim pays a ransom. Ransomware has seen explosive growth over the past few years and has rapidly evolved into a highly lucrative business model. Sophisticated advanced persistent threats (APTs) are employing ransomware to maximize their profits with multiple layers of monetization strategies. New versions appear frequently with ever-evolving tactics and techniques making detection harder. In this chapter, we present a brief history of ransomware, top threat actors employing ransomware, tactics used, and key strategies firms need to deploy to prevent, detect, and respond to ransomware in attacks.
Contents:
1. Cybercrime: Victims’ Shock Absorption Mechanisms
2. Classification Models for Preventing Juvenile Crimes Committed with Malware Apps
3. Detection and Minimization of Malware by Implementing AI in SMEs
4. Malware: Detection and Defense
5. Perspective Chapter: Ransomware
6. A Review of Mathematical Model Based in Clustered Computer Network
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