Identification of malware using CNN and bio-inspired technique

Poonguzhali, N.; Rajakamalam, T.; Uma, S.; Manju, R.

doi:10.1109/icscan.2019.8878696

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…rough the above experiments, it can be seen that the malware visualization method introduced in this study is better than the methods in [17,23], etc. And its generalization ability is good.…”

Section: Discussionmentioning

confidence: 74%

“…And they found out the one with the best performance among the three algorithms is RF. Poonguzhali et al [17] proposed to combine convolutional neural network and intelligent optimization algorithm for malware image detection and achieved high detection accuracy. Many research based on visualization technology has been implemented in computer malware detection, but few on Android malware detection.…”

Section: Introductionmentioning

confidence: 99%

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Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Zhang

et al. 2022

Security and Communication Networks

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With the rapid development of Android, a major mobile Internet platform, Android malware attacks have become the number one threat to mobile Internet security. Traditional malware detection methods have low precision and greater time complexity. At present, image detection methods based on deep learning are used in malware detection. However, most of these methods are based on the largescale convolutional neural network model (such as VGG16). The computation and weight files of these models are very large, so they are not suitable for mobile Internet platforms with limited computation. A novel detection method based on a lightweight convolutional neural network is presented in this study. It transforms Android malware classes.dex, Androidmanifest.xml, and resource.arsc into RGB images and uses the lightweight convolutional neural network to extract the features of RGB images automatically. The experimental results of this study indicate that the method performs well in terms of precision and speed of detection.

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Section: Discussionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Zhang

et al. 2022

Security and Communication Networks

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“…Methodologies such as Genetic Algorithms (GA) [122], Particle Swarm Optimization (PSO) [123] and Ant Colony Optimization (ACO) [124]) are now used for feature selection and optimization for solving problems ranging from the detection of malware in Android OS to the improvement of intrusion detection systems. Unfortunately, to be efficiently deployed to production-quality scenarios, the bio-inspired methods require facing several problems, such as solving the imbalance of a dataset [125], tuning the configurations of neural network models [126], as well as finding the optimal combination of parameters while avoiding the problem of falling into local optimal solution [127]. However, GA algorithms demonstrated their capability for obtaining a strong generalization ability and robustness by finding the best learner group for ensemble models [128].…”

Section: E Bio-inspired and Other Detection Methodsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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“…The DL approach can ma ke cybersecurity much simpler by detecting various threats. The authors of Poonguzhali, et al [16] studied the method of revealing malicious software using convolutional neural networks (CNN). The dangerous code was transformed into grey-scaled images and classified with CNN, reaching an accuracy score of about 94.01%.…”

Section: Related Workmentioning

confidence: 99%

The development of a model for the threat detection system with the use of machine learning and neural network methods

Ussatova,

Zhumabekova,

Karyukin

et al. 2024

ijirss

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This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

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Identification of malware using CNN and bio-inspired technique

Cited by 7 publications

References 4 publications

Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

The development of a model for the threat detection system with the use of machine learning and neural network methods

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