The Use of Machine Learning Techniques to Advance the Detection and Classification of Unknown Malware

Shhadat, Ihab; Bataineh, Bara S; Hayajneh, Amena; Al-Sharif, Ziad A.

doi:10.1016/j.procs.2020.03.110

Cited by 39 publications

(13 citation statements)

References 11 publications

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“…Next, the ANN is utilized for detecting and classifying other data samples. Shhadat et al [17] examined the ML algorithm utilized in unknown malware detection. The study proposes a feature set using RF to minimize the amount of features.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Chandran¹,

Rajini²,

Jeyakarthic³

2023

Intelligent Automation &Amp; Soft Computing

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Cybercrime has increased considerably in recent times by creating new methods of stealing, changing, and destroying data in daily lives. Portable Document Format (PDF) has been traditionally utilized as a popular way of spreading malware. The recent advances of machine learning (ML) and deep learning (DL) models are utilized to detect and classify malware. With this motivation, this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification (MFODBN-MDC) technique. The major intention of the MFODBN-MDC technique is for identifying and classifying the presence of malware exist in the PDFs. The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets. In addition, Adamax optimizer with the DBN model is used for PDF malware detection and classification. The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classification demonstrates the novelty of the work. For demonstrating the improved outcomes of the MFODBN-MDC model, a wide range of simulations are executed, and the results are assessed in various aspects. The comparison study highlighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision, recall, and F1 score of 97.42%, 97.33%, and 97.33%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Chandran¹,

Rajini²,

Jeyakarthic³

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…Shhadat et al [25] applied seven learning algorithms on a benchmark dataset in their experiments, namely KNN, SVM, Bernoulli NB, RF, DT, logistic regression (LR), and hard voting (HV) on particular classification algorithms: LR, SVM, Bernoulli NB, and DT. This paper shows that the highest accuracy was achieved by DT, with a score 98.2% for binary classification and 95.8% by RF for multi-class classification.…”

Section: Reference Workmentioning

confidence: 99%

MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

et al. 2022

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This paper focuses on training machine learning models using the XGBoost and extremely randomized trees algorithms on two datasets obtained using static and dynamic analysis of real malicious and benign samples. We then compare their success rates—both mutually and with other algorithms, such as the random forest, the decision tree, the support vector machine, and the naïve Bayes algorithms, which we compared in our previous work on the same datasets. The best performing classification models, using the XGBoost algorithm, achieved 91.9% detection accuracy and 98.2% sensitivity, 0.853 AUC, and 0.949 F1 score on the static analysis dataset, and 96.4% accuracy and 98.5% sensitivity, 0.940 AUC, and 0.977 F1 score on the dynamic analysis dataset. Then, we exported the best performing machine learning models and used them in our proposed MLMD program, automating the process of static and dynamic analysis and allowing the trained models to be used for classification on new samples.

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“…Te-Shun Chou discussed cloud computing and thought that developing customized computer software, business applications, and online storage are the services provided by cloud computing platforms [10]. Many hackers attempt to make use of security vulnerabilities of the cloud architecture by data breaches in cloud platforms [11]. The cloud service models that are provided by many providers are SaaS, PaaS, and IaaS.…”

Section: Literature Surveymentioning

confidence: 99%

Prevention of Runtime Malware Injection Attack in Cloud Using Unsupervised Learning

Prabhavathy¹,

Umamaheswari²

2022

Intelligent Automation &Amp; Soft Computing

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Cloud computing utilizes various Internet-based technologies to enhance the Internet user experience. Cloud systems are on the rise, as this technology has completely revolutionized the digital industry. Currently, many users rely on cloud-based solutions to acquire business information and knowledge. As a result, cloud computing services such as SaaS and PaaS store a warehouse of sensitive and valuable information, which has turned the cloud systems into the obvious target for many malware creators and hackers. These malicious attackers attempt to gain illegal access to a myriad of valuable information such as user personal information, password, credit/debit card numbers, etc., from systems as the unsecured e-learning ones. As an important part of cloud services, security is needed to protect business customers and users from unauthorized threats. This paper aims to identify malware that attacks cloud-based software solutions using an unsupervised learning model with fixed-weight Hamming and Mexiannet. Different types of attack methodologies and various ways of malicious instructions targeting unknown files in cloud services are investigated. The result and analysis in this study provide an evolution of the unsupervised learning detection algorithm with an accuracy of 94.05%.

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The Use of Machine Learning Techniques to Advance the Detection and Classification of Unknown Malware

Cited by 39 publications

References 11 publications

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

Prevention of Runtime Malware Injection Attack in Cloud Using Unsupervised Learning

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