A Survey of Malware Detection Techniques

By Nwokedi C. Idika, Aditya P. Mathur. 2007

527 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/888cb0ac77e16ec98c8e2a5af79e567d8a43dcd1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Li Deng
3. Dong Yu
4. Nicholas Carlini
5. Kyunghyun Cho
6. Zhe Gan
7. Yang Liu
8. Percy Liang

Citations in the corpus

• TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks (2020, unknown): “As there are often only a handful of sensitive scenarios that the user may be concerned about, this detection can be carried out ef-ficiently by exhaustively testing against all suspected tasks — a search technique similar to signature scanning in malware detection (Idika & Mathur, 2007).”
• Large-scale malware classification using random projections and neural networks (2013, methodology): “Given the severity of the problem, malware classification is an active research area [1], but the escalating threat indicates the problem is clearly not solved.”
• Malware classification with recurrent networks (2015, methodology): “Malware Classification: The most recent summary of the field of malware classification is given in [1].”
• A Comprehensive Review on Malware Detection Approaches (2020, background): “Static analysis examines the malware without running the actual code [19].”
• Evaluation and Design of Robust Neural Network Defenses (2018, methodology): “We refer the reader to Idika and Mathur [50] for an excellent survey which performs a detailed study of 45 different classification techniques.”
• Collaborative Security (2015, background): “Peer-to-peer networks should be the first attempt to enhance the communication in CIDS used in Janakiraman et al. [2003] and Zhang et al. [2003]. Subsequently, overlay networks (e.g., DHT overlay network) are proposed to accelerate the communication in Yegneswaran et al. [2004], Marchetti et al. [20”
• Polonium: Tera-Scale Graph Mining and Inference for Malware Detection (2011, background): “To the best of our knowledge, formulating the malware detection problem as a file reputation inference problem over a machine-file bipartite graph is novel.”
• Opcode sequences as representation of executables for data-mining-based unknown malware detection (2013, background): “Data mining approaches usually rely on machine-learning algorithms that use both malicious executables and benign software to detect malware in the wild [28, 29, 81, 70].”
• A Survey on Malware Detection Using Data Mining Techniques (2017, background): “In Idika and Mathur [2007] and Damshenas et al. [2013], the authors reviewed the malware propagation, analysis and detection; while in Siddiqui et al. [2008], Mathur and Hiranwal [2013], Bazrafshan et al. [2013], and Gandotra et al. [2014], the researchers surveyed the feature representation and…”
• A survey of the applications of text mining in financial domain (2016, background): “Idika and Mathur (2007) carried out research on different malware detection techniques and reported their limitations.”
• Malware Classification with Deep Convolutional Neural Networks (2018, background): “Non-Machine Learning Methods: In the past, malware was detected using static or dynamic signature-based techniques [10].”