Game-theoretic approach to epidemic modeling of countermeasures against future malware evolution

[1]  D. Madeo,et al.  Identification and Control of Game-Based Epidemic Models , 2022, Games.

[2]  D. Madeo,et al.  Evolutionary Game Theoretic Insights on the SIRS Model of the COVID-19 Pandemic , 2021, IFAC-PapersOnLine.

[3]  Jun Zhang,et al.  Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases , 2021, IEEE Transactions on Dependable and Secure Computing.

[4]  Yonghang Tai,et al.  Deep neural-based vulnerability discovery demystified: data, model and performance , 2021, Neural Computing and Applications.

[5]  Hyoungshick Kim,et al.  FUMVar: a practical framework for generating Fully-working and Unseen Malware Variants , 2021, SAC.

[6]  Jianguo Ren,et al.  A theoretical method to evaluate honeynet potency , 2021, Future Gener. Comput. Syst..

[7]  M. A. Javarone,et al.  An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics , 2021, Chaos, Solitons & Fractals.

[8]  Kai Chen,et al.  Tainting-Assisted and Context-Migrated Symbolic Execution of Android Framework for Vulnerability Discovery and Exploit Generation , 2020, IEEE Transactions on Mobile Computing.

[9]  Kemal Davaslioglu,et al.  Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing , 2020, IEEE Transactions on Cognitive Communications and Networking.

[10]  W. Bastiaan Kleijn,et al.  Edge-consensus Learning: Deep Learning on P2P Networks with Nonhomogeneous Data , 2020, KDD.

[11]  Ye Wang,et al.  A feature-vector generative adversarial network for evading PDF malware classifiers , 2020, Inf. Sci..

[12]  Ivan Zelinka,et al.  Artificial Intelligence in the Cyber Domain: Offense and Defense , 2020, Symmetry.

[13]  Henry Leung,et al.  Adversarial-Example Attacks Toward Android Malware Detection System , 2020, IEEE Systems Journal.

[14]  Tim Verbelen,et al.  A Survey on Distributed Machine Learning , 2019, ACM Comput. Surv..

[15]  Ivan Zelinka,et al.  A Survey on Artificial Intelligence in Malware as Next-Generation Threats , 2019 .

[16]  J. Tanimoto,et al.  Dynamical behaviors for vaccination can suppress infectious disease – A game theoretical approach , 2019, Chaos, Solitons & Fractals.

[17]  Polly Wainwright,et al.  An Analysis of Botnet Models , 2019, ICCDA.

[18]  Jianguo Ren,et al.  A compartmental model to explore the interplay between virus epidemics and honeynet potency , 2018, Applied Mathematical Modelling.

[19]  Kouji Hirata,et al.  Stochastic modeling of self-evolving botnets with vulnerability discovery , 2018, Comput. Commun..

[20]  Vickie Curtis,et al.  Patterns of Participation and Motivation in Folding@home: The Contribution of Hardware Enthusiasts and Overclockers , 2018 .

[21]  Lu-Xing Yang,et al.  Heterogeneous virus propagation in networks: a theoretical study , 2017 .

[22]  Shu-Tao Xia,et al.  Design and analysis of SEIQR worm propagation model in mobile internet , 2017, Commun. Nonlinear Sci. Numer. Simul..

[23]  Alexios Mylonas,et al.  A Game Theoretical Method for Cost-Benefit Analysis of Malware Dissemination Prevention , 2015, Inf. Secur. J. A Glob. Perspect..

[24]  Ángel Martín del Rey,et al.  Mathematical modeling of the propagation of malware: a review , 2015, Secur. Commun. Networks.

[25]  Bo Qu,et al.  SIS Epidemic Spreading with Heterogeneous Infection Rates , 2015, IEEE Transactions on Network Science and Engineering.

[26]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[27]  Wouter Joosen,et al.  Predicting Vulnerable Software Components via Text Mining , 2014, IEEE Transactions on Software Engineering.

[28]  Julian Jang,et al.  A survey of emerging threats in cybersecurity , 2014, J. Comput. Syst. Sci..

[29]  Min Wu,et al.  Propagation model of smartphone worms based on semi-Markov process and social relationship graph , 2014, Comput. Secur..

[30]  Martin A. Nowak,et al.  Games on graphs , 2014 .

[31]  Giovanni Squillero,et al.  Towards automated malware creation: code generation and code integration , 2014, SAC.

[32]  Ronaldo M. Salles,et al.  Botnets: A survey , 2013, Comput. Networks.

[33]  Ronald L. Rivest,et al.  FlipIt: The Game of “Stealthy Takeover” , 2012, Journal of Cryptology.

[34]  Vasileios Karyotis,et al.  Markov Random Fields for Malware Propagation: The Case of Chain Networks , 2010, IEEE Communications Letters.

[35]  Muhammad Zubair Shafiq,et al.  Evolvable malware , 2009, GECCO.

[36]  Ludovic Mé,et al.  Code obfuscation techniques for metamorphic viruses , 2008, Journal in Computer Virology.

[37]  G. Szabó,et al.  Evolutionary games on graphs , 2006, cond-mat/0607344.

[38]  S Redner,et al.  Evolutionary dynamics on degree-heterogeneous graphs. , 2006, Physical review letters.

[39]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[40]  Waqas Aman,et al.  Enhanced Metamorphic Techniques-A Case Study Against Havex Malware , 2021, IEEE Access.

[41]  Luis Alvarez-Icaza,et al.  Bluetooth Worm Propagation in Smartphones: Modeling and Analyzing Spatio-Temporal Dynamics , 2021, IEEE Access.

[42]  Gloria Rumbidzai Regedzai,et al.  A Survey on Botnet Attacks , 2021 .

[43]  Sheng Feng,et al.  An Epidemiology-Based Model for Disclosing Dynamics of Malware Propagation in Heterogeneous and Mobile WSNs , 2020, IEEE Access.

[44]  A. Barabasi,et al.  Emergence of Scaling in Random Networks , 1999 .