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1. Full Text A Study on Malware and Malware Detection Techniques
by Tahir, Rabia and Department of Computer Science, Virtual University of Pakistan
International Journal of Education and Management Engineering, ISSN 2305-3623, 03/2018, Volume 8, Issue 2, pp. 20 - 30
Malware
Journal Article
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2. Anti-Virtualization in Dynamic Analysis of Malicious Code
by Jian-Ping Mo and Ling-Yun Ying and Pu-Rui Su and Jia-Jie Wang
Jisuanji Xitong Yingyong = Computer Systems and Applications, ISSN 1003-3254, 01/2018, Issue 12
Malware
Journal Article
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3. Full Text A survey of similarities in banking malware behaviours
by Black, Paul and Gondal, Iqbal and Layton, Robert
Computers & security, ISSN 0167-4048, 08/2018, Volume 77, pp. 756 - 772
Malware components | Banking malware | Malware similarity | Malware analysis | Malware capabilities | Computer Science, Information Systems | Technology | Computer Science | Science & Technology | Surveys | Usage | Applications programming | Banks (Finance) | Spyware | Computer viruses | Identification methods | Banking | Banking industry | Malware | Source code | Low level
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4. Full Text Mobile malware attacks: Review, taxonomy & future directions
by Qamar, Attia and Karim, Ahmad and Chang, Victor
Future generation computer systems, ISSN 0167-739X, 08/2019, Volume 97, pp. 887 - 909
Android malware | Malware | Mobile malware detection | Mobile malware | Forensics | Machine learning | Computer Science, Theory & Methods | Technology | Computer Science | Science & Technology | Business schools | Security management | Analysis | Forensic sciences | Artificial intelligence | Spyware
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5. Full Text An opcode‐based technique for polymorphic Internet of Things malware detection
by Darabian, Hamid and Dehghantanha, Ali and Hashemi, Sattar and Homayoun, Sajad and Choo, Kim‐Kwang Raymond
Concurrency and computation, ISSN 1532-0626, 03/2020, Volume 32, Issue 6, p. n/a
polymorphic malware | IoT security | malware detection | IoT malware | Spyware | Methods | Support vector machines | Machine learning | Multilayer perceptrons | Malware | Internet of Things | Data mining | Decision trees | Pattern analysis
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6. Android malware and analysis
by Dunham, Ken, author
2015, ISBN 9781482252194, xx, 222 pages
Smartphones | Operating systems (Computers) | LAW | Android (Electronic resource) | Malware (Computer software) | COMPUTERS | Software Development & Engineering | General | Forensic Science | Security | Security measures | Mobile computing
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7. Full Text Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm
by Roseline, S. Abijah and Geetha, S and Kadry, Seifedine and Nam, Yunyoung
IEEE access, ISSN 2169-3536, 2020, Volume 8, pp. 206303 - 206324
Deep learning | Visualization | malware variants | Machine learning | Forestry | malware images | Feature extraction | malware detection | Malware | Data mining | malware classification | malware visualization | Computer Science, Information Systems | Engineering | Technology | Computer Science | Engineering, Electrical & Electronic | Telecommunications | Science & Technology | Classifiers | Robustness (mathematics) | Classification | Back propagation
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8. Full Text High accuracy android malware detection using ensemble learning
by Yerima, Suleiman Y and Sezer, Sakir and Muttik, Igor
IET information security, ISSN 1751-8709, 11/2015, Volume 9, Issue 6, pp. 313 - 320
Research Articles | static analysis | invasive software | ensemble machine learning | Android (operating system) | learning (artificial intelligence) | high accuracy Android malware detection | Computer Science, Information Systems | Computer Science, Theory & Methods | Technology | Computer Science | Science & Technology | Smartphones | Platforms | Accuracy | Applications programs | Machine learning | Malware | Computer information security | Gain
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9. Full Text Malware classification using compact image features and multiclass support vector machines
by Ghouti, Lahouari and Imam, Muhammad
IET information security, ISSN 1751-8709, 7/2020, Volume 14, Issue 4, pp. 419 - 429
Research Article | malware category | Internet tools | invasive software | support vector machines | malware binaries | image classification | multiclass support vector machines | BIG2015 malware datasets | malware developers | impair computer software | undetected vulnerabilities | information security specialists | security of data | feature extraction | image representation | optimised support vector machine model | Internet | highly discriminative features | learning (artificial intelligence) | compact image features | principal component analysis | malware categories | malware classification | Computer Science, Information Systems | Computer Science, Theory & Methods | Technology | Computer Science | Science & Technology
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10. Full Text Survey of machine learning techniques for malware analysis
by Ucci, Daniele and Aniello, Leonardo and Baldoni, Roberto
Computers & security, ISSN 0167-4048, 03/2019, Volume 81, pp. 123 - 147
Portable executable | Benchmark | Malware analysis economics | Malware analysis | Machine learning | Computer Science, Information Systems | Technology | Computer Science | Science & Technology | Economic analysis | Algorithms | Malware | Cost analysis | Decision trees | Artificial intelligence | Complexity | Computer Science - Cryptography and Security
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11. A framework for generating malware threat intelligence
by Gandotra, Ekta and Bansal, Divya and Sofat, Sanjeev
Scalable Computing. Practice and Experience, ISSN 1895-1767, 2017, Volume 18, Issue 3, pp. 195 - 205
Malware | Threat intelligence | Malware analysis | Malware detection | Malware threat level
Journal Article
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12. Full Text Analysis of Bayesian classification-based approaches for Android malware detection
by Yerima, Suleiman Y and Sezer, Sakir and McWilliams, Gavin
IET information security, ISSN 1751-8709, 1/2014, Volume 8, Issue 1, pp. 25 - 36
invasive software | operating system kernels | pattern classification | mobile malware | Android malware detection | signature based scanning | static analysis | smart phones | smartphones | learning (artificial intelligence) | machine learning | static analytic Bayesian classification | Computer Science, Information Systems | Computer Science, Theory & Methods | Technology | Computer Science | Science & Technology | Memory organisation. Data processing | Exact sciences and technology | Applied sciences | Software | Memory and file management (including protection and security) | Artificial intelligence | Computer science; control theory; systems | Bayesian analysis
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13. Full Text Improved information on astrophysical S-factor for the 10B(p, α0)7Be reaction using the Trojan Horse method
by Cvetinović, A and Spitaleri, C and Spartá, R and Rapisarda, G.G and Puglia, S.M.R and La Cognata, M and Cherubini, S and Guardo, G.L and Gulino, M and Lamia, L and Pizzone, R.G and Romano, S and Sergi, M.L and Tumino, A
EPJ Web of Conferences, ISSN 2101-6275, 06/2018, Volume 184, p. 2002
Malware
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