A New Adaptive Learning Algorithm and Its Application to Online Malware Detection

Huynh, Ngoc Anh; Ng, Wee Keong; Ariyapala, Kanishka

doi:10.1007/978-3-319-67786-6_2

Cited by 9 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section compares the proposed method with the state of the art ones by a comprehensive database. The results in this paper are obtained using the database introduced in [22], which contains more than 100K malware. The malware was obtained by analyzing millions of executable files by 52 antiviruses over a period of 4 years and extracting 486 important features.…”

Section: Experimental Evaluation a Simulation Setupmentioning

confidence: 99%

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Ghahramani¹,

Taheri²,

Shojafar³

et al. 2022

Preprint

View full text Add to dashboard Cite

The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the field of cyber security have extensively explored the usage of sophisticated analytics and the efficiency of malware detection. With the introduction of new malware kinds and attack routes, security experts confront considerable challenges in developing efficient malware detection and analysis solutions. In this paper, a different view of malware analysis is considered and the risk level of each sample feature is computed, and based on that the risk level of that sample is calculated. In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment. In this paper, three malware detection methods based on visualization techniques called the clustering approach, the probabilistic approach, and the deep learning approach are proposed. Then, in addition to the usual machine learning criteria namely accuracy and FPR, a proposed criterion based on the risk of samples has also been used for comparison, with the results showing that the deep learning approach performed better in detecting malware.

show abstract

Section: Experimental Evaluation a Simulation Setupmentioning

confidence: 99%

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Ghahramani¹,

Taheri²,

Shojafar³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Huynh, Ng and Ariyapala [17] investigated an adaptive learning algorithm which was implemented to apply online learning to malware detection. 100k executable samples were obtained from VirusShare, with the cohort used described by the authors as being collected from March 2016 to April 2016.…”

Section: Incremental Learning In Malware Detectionmentioning

confidence: 99%

A cost analysis of machine learning using dynamic runtime opcodes for malware detection

Carlin

O’Kane

Sezer

2019

Computers & Security

View full text Add to dashboard Cite

show abstract

“…Malware: The fourth data set we evaluate on is the Dynamic Features of VirusShare Executables data set from Huynh et al [19] which contains the dynamic features of executables collected by VirusShare between November 2010 and July 2014. The target variable is a risk score between 0 and 1.…”

Section: B1 Dataset Detailsmentioning

confidence: 99%

Connecting Lyapunov Control Theory to Adversarial Attacks

Rahnama¹,

Nguyen²,

Raff³

2019

Preprint

View full text Add to dashboard Cite

Significant work is being done to develop the math and tools necessary to build provable defenses, or at least bounds, against adversarial attacks of neural networks. In this work, we argue that tools from control theory could be leveraged to aid in defending against such attacks. We do this by example, building a provable defense against a weaker adversary. This is done so we can focus on the mechanisms of control theory, and illuminate its intrinsic value.

show abstract

A New Adaptive Learning Algorithm and Its Application to Online Malware Detection

Cited by 9 publications

References 17 publications

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

A cost analysis of machine learning using dynamic runtime opcodes for malware detection

Connecting Lyapunov Control Theory to Adversarial Attacks

Contact Info

Product

Resources

About