The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Malicious code detection based on CNNs and multi-objective algorithm
Abstract
Abstract An increasing amount of malicious code causes harm on the internet by threatening user privacy as one of the primary sources of network security vulnerabilities. The detection of malicious code is becoming increasingly crucial, and current methods of detection require much improvement. This paper proposes a method to advance the detection of malicious code using convolutional neural networks (CNNs) and intelligence algorithm. The CNNs are used to identify and classify grayscale images converted from executable files of malicious code. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then employed to deal with the data imbalance of malware families. A series of experiments are designed for malware image data from Vision Research Lab. The experimental results demonstrate that the proposed method is effective, maintaining higher accuracy and less loss.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Cited by
IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture
Vasan, D., Alazab, M., Wassan, S., Naeem, H., Safaei, B., & Zheng, Q. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107138 · Cited by 513
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101748 · Cited by 401
ConvUNeXt: An efficient convolution neural network for medical image segmentation
Han, Z., Jian, M., & Wang, G.-G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109512 · Cited by 348
An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search
Cai, X., Niu, Y., Geng, S., Zhang, J., Cui, Z., Li, J., & Chen, J. (2020) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5478 · Cited by 267
Self-citationCyber Security Threats Detection in Internet of Things Using Deep Learning Approach
Ullah, F., Naeem, H., Jabbar, S., Khalid, S., Latif, M. A., Al-turjman, F., & Mostarda, L. (2019) · IEEE Access · doi:10.1109/access.2019.2937347 · Cited by 266
Metadata
- Authors
- Cui, Z., Du, L., Wang, P., Cai, X., & Zhang, W.
- Venue
- Journal of Parallel and Distributed Computing
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 129
- Pages
- 50-58
- ISSN
- 0743-7315 (ISSN-L)
- OpenAlex
- W2923563913
- Cited by
- 182
- References
- 2
- FWCI
- 18.15
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Malicious code detection based on CNNs and multi-objective algorithm. https://app.cochat.ai/papers/10.1016/j.jpdc.2019.03.010
- The paper
- Cui, Z., Du, L., Wang, P., Cai, X., & Zhang, W. (2019). Malicious code detection based on CNNs and multi-objective algorithm. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2019.03.010
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
