A Review of Cloud-Based Malware Detection System: Opportunities, Advances and Challenges

Aslan, Ömer; Ozkan-Okay, Merve; Gupta, Deepti

doi:10.24018/ejers.2021.6.3.2372

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…These reasons change the malware detection landscape from computer to mobile-IoT devices, and cloud environment. Cloud computing brings many advantages for malware detection including easy access, more computational power and much bigger databases [13]. Thus, most of the recent papers on malware detection and classification methods are written for mobile and IoT devices using deep learning and implemented in cloud computing environments.…”

Section: Related Workmentioning

confidence: 99%

A New Malware Classification Framework Based on Deep Learning Algorithms

Aslan

Yılmaz²

2021

IEEE Access

129

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Recent technological developments in computer systems transfer human life from real to virtual environments. Covid-19 disease accelerates this process. Cyber criminals' interest shifts from real to virtual life as well. This is because it is easier to commit a crime in cyberspace rather than regular life. Malicious software (malware) is an unwanted software which is frequently used by cyber criminals to launch cyber attacks. Malware variants are continuing to evolve by using advanced obfuscation and packing techniques. These concealing techniques make malware detection and classification significantly challenging. Novel methods which are quite different from traditional methods must be used to effectively combat new malware variants. Traditional artificial intelligence (AI) specifically machine earning (ML) algorithms are no longer effective to detect all new and complex malware. Deep learning (DL) approach which is quite different from traditional ML algorithms can be a promising solution when detecting all variants of malware. In this study, a novel deep-learning-based architecture is proposed which can classify malware variants based on a hybrid model. The main contribution of the study is to propose a new hybrid architecture which integrates two wide-ranging pre-trained network models in an optimized manner. This architecture consists of four main stages: namely, data acquisition, the design of deep neural network architecture, training of the proposed deep neural network architecture, and evaluation of the trained deep neural network. The experimental results show that the suggested method can effectively classify malware with high accuracy which outperforms the state of the art methods in the literature.INDEX TERMS Malware, malware classification, malware detection, malware variants, deep neural networks, transfer learning, deep learning.

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Section: Related Workmentioning

confidence: 99%

A New Malware Classification Framework Based on Deep Learning Algorithms

Aslan

Yılmaz²

2021

IEEE Access

129

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“…For businesses' needs, one cloud service can have more advantages than others. There are also different kinds of cloud deployment models including public, private, community and hybrid clouds [6]. Public clouds are owned by third-party cloud providers and services, and available to the general public.…”

Section: Cloud Computingmentioning

confidence: 99%

“…Using the cloud computing environment for malware detection brings many advantages. Cloud environment presents more computational power and much bigger databases [6]. Different methods and algorithms can be implemented in the cloud such as machine learning (ML), data mining, and deep learning.…”

Section: Cloud Computingmentioning

confidence: 99%

Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

2021

Self Cite

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These days, cloud computing is one of the most promising technologies to store information and provide services online efficiently. Using this rapidly developing technology to protect computer-based systems from cyber-related attacks can bring many advantages over traditional protection schemes. The protected assets can be any computer-based systems such as cyber-physical systems (CPS), critical systems, desktop and laptop computers, mobile devices, and Internet of Things (IoT). Malicious software (malware) is any software which targets the computer-based system to launch cyber-attacks to threaten the integrity, confidentiality and availability of the data. To detect the massively growing malware attacks surface, we propose an intelligent behavior-based detection system in the cloud environment. The proposed system first creates a malware dataset on different virtual machines which identify distinctive features efficiently. Then, selected features are given to the learning-based and rule-based detection agents to separate malware from benign samples. Totally, 10,000 program samples have been analyzed to evaluate the performance of the proposed system. The proposed system can detect both known and unknown malware efficiently with high detection and accuracy rate. Besides, the proposed method results have outperformed the leading methods' results in the literature. Our evaluation results show that the proposed algorithms along with machine learning (ML) classifiers achieve 99.8% detection rate, 0.4% false positive rate, and 99.7% accuracy. Our proposed system and algorithms may assist those who would like to develop a novel malware detection system in the cloud environment.

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“…After the outbreak of COVID-19 pandemic and the circumstances that led to continuous lockdowns, technology acceptance has augmented in multiple folds and through diverse methods. For example, e-health applications have been designed after COVID-19 outbreak to support the already-exhausted healthcare professionals and medical systems [1]. However, a comprehensive connection to the cyber-world, on the other hand, increased the amount of cyberattacks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

Maray¹,

Alqahtani²,

Alotaibi³

et al. 2023

Computers, Materials &Amp; Continua

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Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity, secrecy, and accessibility of the information. The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification (OBDDBN-CMC) model. The presented OBDDBN-CMC model intends to recognize and classify the malware that exists in IoT-based cloud platform. To attain this, Zscore data normalization is utilized to scale the data into a uniform format. In addition, BDDBN model is also exploited for recognition and categorization of malware. To effectually fine-tune the hyperparameters related to BDDBN model, Grasshopper Optimization Algorithm (GOA) is applied. This scenario enhances the classification results and also shows the novelty of current study. The experimental analysis was conducted upon OBDDBN-CMC model for 3102 CMC, 2023, vol.74, no.2 validation and the results confirmed the enhanced performance of OBDDBN-CMC model over recent approaches.

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A Review of Cloud-Based Malware Detection System: Opportunities, Advances and Challenges

Cited by 10 publications

References 15 publications

A New Malware Classification Framework Based on Deep Learning Algorithms

A New Malware Classification Framework Based on Deep Learning Algorithms

Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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