Using Deep-Learning-Based Memory Analysis for Malware Detection in Cloud

Li, Huhua; Zhan, Dongyang; Liu, Tianrui

doi:10.1109/massw.2019.00008

Cited by 14 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [20] , deep learning was employed to detect Malware in cloud computing runs in the virtual machine-monitor layer; executing malware extracts memory snapshots of virtual machines. It converts the picture to grayscale images.…”

Section: Related Workmentioning

confidence: 99%

Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function

Sabah Talabani,

Abdulhadi,

Ali

2024

PJBAS

View full text Add to dashboard Cite

Malware is a severe threat to the network and host system security. It is frequently the primary cause of many events, such as Distributed Denial-of-Service attacks (DDoS), spam emails, etc. The detection and elimination of Malware are the subjects of intensive study. As a result, many antivirus programs have been created to help identify and remove Malware. The issue with this antivirus software is that it uses an obsolete method of detecting Malware, the signature-matching approach, which the primary forms of code obfuscation may deceive. Since then, this has resulted in the creation of a new generation of metamorphic and polymorphic Malware. In this paper, we investigated using the Instance-Based Learner (IBK) algorithm for detecting obfuscated Malware in a given dataset. Utilizing the Lazy IBK technique in malware detection is beneficial because the algorithm can accurately detect and classify the obfuscated Malware in the dataset using the Manhattan Distance function, one of the most well-known distance metric functions for measuring the distance between points. We analyzed an obfuscated malware dataset of 58,596 records selected from 3 malware categories. The algorithm was illustrated on the dataset utilizing 10-fold cross-validation. The results demonstrate that the proposed algorithm can quickly and accurately detect obfuscated Malware with an accuracy of 99.99%, a precision of 100%, and a recall of 100%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function

Sabah Talabani,

Abdulhadi,

Ali

2024

PJBAS

View full text Add to dashboard Cite

show abstract

“…An evolutionary algorithm is used to build a graph for each malware family and benign code, which is used to compare with when performing classification. Li et al [17] proposed a CNN-based malware detection approach. In the approach, virtual machine memory snapshot image of running malware and benign is captured and memory images converted to grayscale images, which is used for training and testing on the CNN-based model.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

Darem¹

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware. In this paper, a novel approach based on deep learning for detecting malware proposed. Furthermore, the proposed approach deploys novel feature selection, feature co-relation, and feature representations to significantly reduce the feature space. The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families. It achieved 96.01% accuracy and outperformed the existing techniques of malware detection.

show abstract

“…The analysis encompasses the usage of features obtained statically or dynamically and then using state of art technologies, to name a few, machine learning algorithms, or deep learning [5]. These approaches help identify the presence of malware and the family it belongs to [6]. The different approaches studied included advantages over the other and also limitations faced.…”

Section: Introductionmentioning

confidence: 99%

A Review and Case Study on Android Malware: Threat Model, Attacks, Techniques and Tools

Negi

Mishra

Chaudhary

et al. 2021

JCSANDM

View full text Add to dashboard Cite

As android devices have increased in number in the past few years, the android operating system has started dominating the smartphone market. The vast spread of android across all the devices has made security an important issue as the android users continue to grow exponentially. The security of android platform has become the need of the hour in view of increase in the number of malicious apps and thus several studies have emerged to present the detection approaches. In this paper, we review the android components to propose a threat model that illustrates the possible threats that are present in the android. We also present the attack taxonomy to illustrate the possible attacks at various layers of the android architecture. Experiments demonstrating the feature extraction and classification using machine earning algorithms have also been performed.

show abstract

Using Deep-Learning-Based Memory Analysis for Malware Detection in Cloud

Cited by 14 publications

References 20 publications

Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function

Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function

A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

A Review and Case Study on Android Malware: Threat Model, Attacks, Techniques and Tools

Contact Info

Product

Resources

About