• Corpus ID: 203610539

Ransomware Analysis using Feature Engineering and Deep Neural Networks

@article{Ashraf2019RansomwareAU,
  title={Ransomware Analysis using Feature Engineering and Deep Neural Networks},
  author={Arslan Ashraf and Abdul Aziz and Umme Zahoora and Asifullah Khan},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.00286}
}
Detection and Analysis of a potential malware specifically, used for ransom is a challenging task. Recently, intruders are utilizing advance cryptographic techniques to get hold of digital assets and then demand ransom. It is believed that generally, the files comprise of some attributes, states, and patterns that can be recognized by a machine learning technique. This work thus focuses on detection of Ransomware by performing feature engineering, which helps in analyzing vital attributes and… 

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References

SHOWING 1-10 OF 30 REFERENCES

Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection

EldeRan, a machine learning approach for dynamically analysing and classifying ransomware, is presented, suggesting that dynamic analysis can support ransomware detection, since ransomware samples exhibit a set of characteristic features at run-time that are common across families, and that helps the early detection of new variants.

Intrusion detection using deep sparse auto-encoder and self-taught learning

Self-taught learning-based extracted features, when concatenated with the original features of NSL-KDD dataset, enhance the performance of the sparse auto-encoder and offers good generalization in comparison with the sparse Autoencoder trained on original features alone.

Deep Learning for Classification of Malware System Call Sequences

The increase in number and variety of malware samples amplifies the need for improvement in automatic detection and classification of the malware variants, and neural network methodology has been grown to the state that can surpass limitations of previous machine learning methods.

Deep neural network based malware detection using two dimensional binary program features

A deep neural network based malware detection system that Invincea has developed is introduced, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware.

Automated Behavioral Analysis of Malware: A Case Study of WannaCry Ransomware

  • Qian ChenR. Bridges
  • Computer Science
    2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
  • 2017
This work introduces a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior, which can help automate tedious manual analysis.

A SURVEY ON RANSOMEWARE: EVOLUTION, GROWTH, AND IMPACT

The origin, evolution and growth of ransomware is discussed, the various families of ransomware, their attacks and prevention from these attacks have been presented, and various parameters contributing the growth of these attacks in todays’ technologically advanced world are discussed.

Static and Dynamic Malware Analysis Using Machine Learning

The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network, so the static analysis is not effective due to tricky and intelligent behaviours of malwares.