Malware Analysis using Machine Learning and Deep Learning techniques

Patil, Rajvardhan; Deng, Wei

doi:10.1109/southeastcon44009.2020.9368268

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…The usage of the internet, computers, and smart gadgets is widespread nowadays, and many people use them on a daily basis. In the same way that there are people with good and ill intentions everywhere we travel, this is also true in the online world, where such services are being used by an increasing number of people [5]. As in the physical world, there are persons on the web with malicious motives who prey on trusting customers whenever money is involved [9].…”

Section: Introductionmentioning

confidence: 94%

“…When displayed on a screen, it scatters and blends into numerous lines, making it extremely difficult to spot. It may establish a direct connection to the operating system and begin cracking it in order to pierce and record specific or relevant information [5]. The search for malware lines and directories is referred to as malware discovery.…”

Section: Malware Detectionmentioning

confidence: 99%

“…A semi supervised model which uses Restricted Boltzmann Machines (RBM) in pre-training phase(unsupervised) followed by regular supervised backpropagation phase [7]. Paper proposes a new malware classification technique based on the maximal common subgraph detection problem [5]. Extracts various features like byte entropy, PE metadata, strings and import features [11].…”

Section: Related Workmentioning

confidence: 99%

“…The activation function used in the hidden layers is Parametric ReLU. The activation function used in output layer is sigmoid activation function since it is a binary classification problem [5]. N-gram based signature generation for malware detection [8].…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Malware Analysis Using Memory Forensics

Das¹

2022

IJRASET

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Malware is still the most dangerous issue facing internet users in today's online environment. The newly created malware is separate from the traditional kind, has a more dynamic design, and typically combines traits from two or more different malware types. comparing the various memory acquisition tools that are available, each of which has a varying performance dependent on the setups, installed hardware, and operating system version. If the ending character is not present. To address the growing malware issue, new methodologies like machine learning must be employed. Investigate how cybersecurity is used in this study for malware detection and machine learning. In this study will look at the PE (portable executable) headers of malware and non-malware samples in order to build a malware classifier that can identify if malware is there or not. The development of behavior-based malware detection and classification methods using various machine learning approaches is addressed in this study along with behavior-based detection methods itself

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Section: Introductionmentioning

confidence: 94%

Section: Malware Detectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Malware Analysis Using Memory Forensics

Das¹

2022

IJRASET

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“…Patil and Deng [26] showed how accuracy could be improved using DL networks rather than the traditional ML models by introducing a neural networks-based framework for malware analysis that achieved high accuracy. The findings from the experiment indicated that the DL-based malware classification method achieves high accuracy in classification.…”

Section: A Deep Learning-based Malware Analysismentioning

confidence: 99%

Machine Learning in Malware Analysis: Current Trends and Future Directions

Altaha,

Riad

2024

IJACSA

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Malware analysis is a critical component of cybersecurity due to the increasing sophistication and the widespread of malicious software. Machine learning is highly significant in malware analysis because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. This paper provides a comprehensive overview of existing work related to Machine Learning (ML) methods used to analyze malware along with a description of each trend. The results of the survey demonstrate the effectiveness and importance of three trends, which are: deep learning, transfer learning, and XML techniques in the context of malware analysis. These approaches improve accuracy, interpretability, and transparency in detecting and analyzing malware. Moreover, the related challenges and Issues are presented. After identifying these challenges, we highlight future directions and potential areas that require more attention and improvement, such as distributed computing and parallelization techniques which can reduce training time and memory requirements for large datasets. Also, further investigation is needed to develop image resizing techniques to be used during the visual representation of malware to minimize information loss while maintaining consistent image sizes. These areas can contribute to the enhancement of machine learning-based malware analysis.

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