SKALD: A Scalable Architecture for Feature Extraction, Multi-user Analysis, and Real-Time Information Sharing

Webster, George D.; Hanif, Zachary D.; Ludwig, André; Lengyel, Tamas K.; Zarras, Apostolis; Eckert, Claudia

doi:10.1007/978-3-319-45871-7_15

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…They implement the solution using Tensorflow [148] and Theano [160]. Malware samples are collected using malware zoo [178] from three primary sources: Virus Share [133], Maltrieve [134] and proprietary collections. They obtain the labels for training the network from VirusTotal [135].…”

Section: ) Api Call Solutionsmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Rodríguez

Otero

Gutiérrez

et al. 2021

IEEE Commun. Surv. Tutorials

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The widespread use of mobile devices, as well as the increasing popularity of mobile services has raised serious cybersecurity challenges. In the last years, the number of cyberattacks has grown dramatically, as well as their complexity. Traditional cybersecurity systems have failed to detect complex attacks, unknown malware, and they do not guarantee the preservation of user privacy. Consequently, cybersecurity systems have embraced Deep Learning (DL) models as they provide efficient detection of novel attacks and better accuracy. This paper presents a comprehensive survey of recent cybersecurity works that use DL in mobile and wireless networks. It covers all cybersecurity aspects: infrastructure threads and attacks, software attacks and privacy preservation. First, we provide a detailed overview of DL techniques applied, or with potential applications, to cybersecurity. Then, we review cybersecurity works based on DL. For each cybersecurity threat or attack, we discuss the challenges for using DL methods. For each contribution, we review the implementation details and the performance of the solution. In a nutshell, this paper constitutes the first survey that provides a complete review of the DL methods for cybersecurity. Given the analysis performed, we identify the most effective DL methods for the different threats and attacks.

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Section: ) Api Call Solutionsmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Rodríguez

Otero

Gutiérrez

et al. 2021

IEEE Commun. Surv. Tutorials

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“…We collect malware samples and track the behavior of malware using Cuckoo malware analysis [24]. The malware collection consists of samples collected from two primary sources: Virus Share [25] and GitHub / TheZoo [26]. We chose this source because it provided a large and varied sample Portable Executable (PE) file for evaluation.…”

Section: Dataset Generationmentioning

confidence: 99%

Malware Classification Based on System Call Sequences Using Deep Learning

Maulana¹,

Kusuma²

2020

Adv. sci. technol. eng. syst. j.

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Malware has always been a big problem for companies, government agencies, and individuals because people still use it as a primary tool to influence networks, applications, and computer operating systems to gain unilateral benefits. Until now, malware detection with heuristic and signature-based methods are still struggling to keep up with the evolution of malware. Machine learning is known to be able to automate the work needed to detect families of existing and newly discovered malware. Unfortunately, the machine learning method using Support Vector Machine (SVM) for detecting malware can only reach a low level of accuracy. In this work, we propose a dynamic analysis method and uses a system call sequence to monitor malware behavior. It uses the word2vec technique as word embedding and implements deep learning models, namely Long Short-Term Memory (LSTM) and Nested LSTM, as classifiers. To compare with existing machine learning approach, we also apply the Support Vector Machine (SVM) as a benchmark method. The Nested LSTM gets an accuracy of 93.11%, while the LSTM gets the best accuracy of 98.61%. The LSTM also achieved the best performance in terms of average precision at 97.57%, the average recall at 97.29%, and the average score of f1 at 97.43%. We have found that our model is lightweight but powerful for detecting malware with significant accuracy.

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“…We use a published framework [15] to collect samples. Our dataset is consisted of files collected from 3 major sources: Virus Share [16], Maltrieve [17] and private collections.…”

Section: Datasetmentioning

confidence: 99%

Malware Detection with Neural Network Using Combined Features

Zhou¹

2019

Communications in Computer and Information Science

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The growth in amount and species of malicious programs are now turning into a severe problem that strengthens the demand for development in detecting and classifying the potential threats automatically. Deep learning is an acceptable method to process this increment. In this paper, we propose an innovative method for detecting malware which uses the combined features (static + dynamic) to classify whether a portable executable (PE) file is malicious or not. A thorough experimental research on a real PE file collection was executed to make comparisons with the results that was performed in diverse situations and the performances of different machine learning models. The experiments prove the effectiveness of our model and show that our method is able to detect unknown malicious samples well.

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SKALD: A Scalable Architecture for Feature Extraction, Multi-user Analysis, and Real-Time Information Sharing

Cited by 10 publications

References 9 publications

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Malware Classification Based on System Call Sequences Using Deep Learning

Malware Detection with Neural Network Using Combined Features

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