Malware Analysis and Detection Engineering

Mohanta, Abhijit; Saldanha, Anoop

doi:10.1007/978-1-4842-6193-4

Cited by 22 publications

(14 citation statements)

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“…A classification of malware dynamic analysis evasion strategies was presented in [50]. Conventionally, a sandbox was introduced to suffer unplanned impacts from unknown software [51]. Thus, the term sandbox represents a segregated or highly managed environment for testing unverified programs.…”

Section: Various Malware Attacks In Iotmentioning

confidence: 99%

Comprehensive Analysis of IoT Malware Evasion Techniques

Al-Marghilani

2021

Eng. Technol. Appl. Sci. Res.

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Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches. Malware uses a few common evasion techniques such as user interaction, environmental awareness, stegosploit, domain and IP identification, code obfuscation, code encryption, timing, and code compression. A comparative analysis was conducted pointing various advantages and disadvantages. This study provides guidelines on IoT malware evasion techniques.

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Section: Various Malware Attacks In Iotmentioning

confidence: 99%

Comprehensive Analysis of IoT Malware Evasion Techniques

Al-Marghilani

2021

Eng. Technol. Appl. Sci. Res.

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“…. [2][3][4]. Malware is a generic term that refers to different types, also families or classes, of malicious programs.…”

Section: Introductionmentioning

confidence: 99%

“…Malware is a generic term that refers to different types, also families or classes, of malicious programs. Several classifications of malware exist in the literature such as the ones in [2][3][4]. Six main families are to be distinguished: Virus, Worm, Trojan Horse, Ransomware, Botnet and Backdoor.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Malware Detection based on Image Conversion

Laouadi,

Menacer,

Benatchba

2024

Preprint

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Due to the sophistication of recent malware, classical detection approaches are becoming obsolete. Machine Learning for malware detection has emerged as a new trend and is becoming increasingly effective. Indeed, malware generate a tremendous amount of data that should be analyzed and used to detect them. The aim of this paper is to propose a Machine Learning approach to detect both recent and old malware by converting them into images. This approach, which consists of two phases, is based on Transfer Learning through the use of Convolutional Neural Networks (CNN) that extract features from malware images. These features are used to determine the maliciousness of a particular file. We define six strategies, each one is a combination of two image types (Grayscale and Color) and three CNN architectures (VGG, ResNet and Inception). Experimental evaluation has been done to test these six strategies. The strategy that fulfills the most testing goals is Grayscale + ResNet with a testing accuracy of 90.08\%. Even if the first results are promising, the future work is to automate the fine-tuning of the parameters to go through all possible values and obtain the best ones.

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“…Malware plays a key role in attacking computer systems [1]. Typically, malware sabotages the purpose of the machine instructions executing on a digital computer.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, malware sabotages the purpose of the machine instructions executing on a digital computer. Also, there is no computational mechanism in the register machine model [2,3,4] 1 for healing (selfrepairing) damaged instructions.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Systems that Heal

Fiske

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Malware plays a key role in attacking critical infrastructure. With this problem in mind, we introduce systems that heal from a broader perspective than the standard digital computer model: Our goal in a more general theory is to be applicable to systems that contain subsystems that do not solely rely on the execution of register machine instructions. Our broader approach assumes a dynamical system that performs tasks.Our primary contribution defines a principle of selfmodifiability in dynamical systems and demonstrates how it can be used to heal a malfunctioning dynamical system. As far as we know, to date there has not been a mathematical notion of self-modifiability in dynamical systems; hitherto there has not been a formal system for describing how to heal damaged computer instructions or to heal differential equations that perform tasks.

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Malware Analysis and Detection Engineering

Cited by 22 publications

References 0 publications

Comprehensive Analysis of IoT Malware Evasion Techniques

Comprehensive Analysis of IoT Malware Evasion Techniques

A Machine Learning Approach for Malware Detection based on Image Conversion

Dynamical Systems that Heal

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