Academic literature on the topic 'Active Malware Analysis'
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Journal articles on the topic "Active Malware Analysis"
Joshi, Ankit, Komesh Borkar, Rohit Dhote, Saurabh Raut, Swapnil Thomare, Raghavendra Kulkarni, and Sharda Chhabria. "A Machine Learning Technique to Detect Malware." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 188–93. http://dx.doi.org/10.22214/ijraset.2022.47841.
Full textMiraglia, Armando, and Matteo Casenove. "Fight fire with fire: the ultimate active defence." Information & Computer Security 24, no. 3 (July 11, 2016): 288–96. http://dx.doi.org/10.1108/ics-01-2015-0004.
Full textZhang, Hong, Shumin Yang, Guowen Wu, Shigen Shen, and Qiying Cao. "Steady-State Availability Evaluation for Heterogeneous Edge Computing-Enabled WSNs with Malware Infections." Mobile Information Systems 2022 (April 11, 2022): 1–16. http://dx.doi.org/10.1155/2022/4743605.
Full textShatnawi, Ahmed S., Aya Jaradat, Tuqa Bani Yaseen, Eyad Taqieddin, Mahmoud Al-Ayyoub, and Dheya Mustafa. "An Android Malware Detection Leveraging Machine Learning." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/1830201.
Full textLondoño, Sebastián, Christian Urcuqui, Manuel Fuentes Amaya, Johan Gómez, and Andrés Navarro Cadavid. "SafeCandy: System for security, analysis and validation in Android." Sistemas y Telemática 13, no. 35 (December 3, 2015): 89–102. http://dx.doi.org/10.18046/syt.v13i35.2154.
Full textSartea, Riccardo, Alessandro Farinelli, and Matteo Murari. "SECUR-AMA: Active Malware Analysis Based on Monte Carlo Tree Search for Android Systems." Engineering Applications of Artificial Intelligence 87 (January 2020): 103303. http://dx.doi.org/10.1016/j.engappai.2019.103303.
Full textO'Callaghan, Derek, Martin Harrigan, Joe Carthy, and Pádraig Cunningham. "Network Analysis of Recurring YouTube Spam Campaigns." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (August 3, 2021): 531–34. http://dx.doi.org/10.1609/icwsm.v6i1.14288.
Full textDuraisamy Soundrapandian, Pradeepkumar, and Geetha Subbiah. "MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric." Symmetry 14, no. 10 (October 21, 2022): 2221. http://dx.doi.org/10.3390/sym14102221.
Full textNawaz, Umair, Muhammad Aleem, and Jerry Chun-Wei Lin. "On the evaluation of android malware detectors against code-obfuscation techniques." PeerJ Computer Science 8 (June 21, 2022): e1002. http://dx.doi.org/10.7717/peerj-cs.1002.
Full textWu, Xiaojun, Qiying Cao, Juan Jin, Yuanjie Li, and Hong Zhang. "Nodes Availability Analysis of NB-IoT Based Heterogeneous Wireless Sensor Networks under Malware Infection." Wireless Communications and Mobile Computing 2019 (January 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/4392839.
Full textDissertations / Theses on the topic "Active Malware Analysis"
Vermeulen, Japie. "An analysis of fusing advanced malware email protection logs, malware intelligence and active directory attributes as an instrument for threat intelligence." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/63922.
Full textChen, Yi-Ning, and 陳怡寧. "Combing Dynamic Passive Analysis and Active Fingerprinting for Effective Bot Malware Detection in Virtualized Environments." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/37410971770711187787.
Full text國立臺灣大學
資訊管理學研究所
100
Defeating botnet is the key to secure the Internet. Many cyber crimes are launched by botnets, such as DDoS, spamming and click frauds. Although numerous network-based detection mechanisms are proposed and implemented, they still have some limitations due to their passive nature. Host-based detection agent can perform more precisely in bot detection; however, it’s intrusive and can be aware by the bot. In order to complement current solutions, we propose a mechanism called active bot fingerprinting. By setting certain specific stimulus to a host, we observe whether certain expected behavior is triggered to examine if the host is a bot. Since the virtualized environment is widely used for enterprises to host their service (e.g., private cloud), we propose and implement a bot detection system combining both passive and active detection approach for virtualized environment. The detection result of both passive detection and active detection shows a good detection rate with low false positive rate and low false negative rate.
Book chapters on the topic "Active Malware Analysis"
Darki, Ahmad, Chun-Yu Chuang, Michalis Faloutsos, Zhiyun Qian, and Heng Yin. "RARE: A Systematic Augmented Router Emulation for Malware Analysis." In Passive and Active Measurement, 60–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76481-8_5.
Full textHsiao, Shun-Wen, Yi-Ning Chen, Yeali S. Sun, and Meng Chang Chen. "Combining Dynamic Passive Analysis and Active Fingerprinting for Effective Bot Malware Detection in Virtualized Environments." In Network and System Security, 699–706. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38631-2_59.
Full textD., Sangeetha, Umamaheswari S., and Rakshana Gopalakrishnan. "Deep Neural Network-Based Android Malware Detection (D-AMD)." In Deep Learning Applications and Intelligent Decision Making in Engineering, 161–75. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2108-3.ch006.
Full textGorment, Nor Zakiah, Ali Selamat, and Ondrej Krejcar. "Anti-Obfuscation Techniques: Recent Analysis of Malware Detection." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220249.
Full textVinod, P., P. R. Rakesh, and G. Alphy. "Similarity Measure for Obfuscated Malware Analysis." In Information Security in Diverse Computing Environments, 180–205. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6158-5.ch010.
Full textKumari, Reema, and Kavita Sharma. "Cross-Layer Based Intrusion Detection and Prevention for Network." In Handbook of Research on Network Forensics and Analysis Techniques, 38–56. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4100-4.ch003.
Full textConference papers on the topic "Active Malware Analysis"
Sartea, Riccardo, Mila Dalla Preda, Alessandro Farinelli, Roberto Giacobazzi, and Isabella Mastroeni. "Active Android malware analysis." In the 6th Workshop. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3015135.3015140.
Full textSartea, Riccardo, and Alessandro Farinelli. "A Monte Carlo Tree Search approach to Active Malware Analysis." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/535.
Full textHota, Abhilash, and Jurgen Schonwalder. "A Bayesian Model Combination based approach to Active Malware Analysis." In 2022 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2022. http://dx.doi.org/10.1109/csr54599.2022.9850338.
Full textAbdullah, Muhammed Amin, Yongbin Yu, Jingye Cai, Yakubu Imrana, Nartey Obed Tettey, Daniel Addo, Kwabena Sarpong, Bless Lord Y. Agbley, and Benjamin Appiah. "Disparity Analysis Between the Assembly and Byte Malware Samples with Deep Autoencoders." In 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2022. http://dx.doi.org/10.1109/iccwamtip56608.2022.10016485.
Full textYordanov, Petar, Krassimir Petkov, Sasho Yordanov, Nina Klenovska, and Ivan Terziiski. "RESEARCH ON THE RISKS IN CYBERSPACE DURING SPORTING EVENTS." In INTERNATIONAL SCIENTIFIC CONGRESS “APPLIED SPORTS SCIENCES”. Scientific Publishing House NSA Press, 2022. http://dx.doi.org/10.37393/icass2022/56.
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