Review on Intelligent Algorithms for Cyber Security

Cyber security comprises of technologies, architecture, infrastructure, and software applications that are designed to protect computational resources against cyber-attacks. Cyber security concentrates on four main areas such as application security, disaster security, information security, and network security. Numerous cyber security algorithms and computational methods are introduced by researchers to protect cyberspace from undesirable invaders and susceptibilities. But, the performance of traditional cyber security algorithms suffers due to different types of offensive actions that target computer infrastructures, architectures and computer networks. The implementation of intelligent algorithms in encountering the wide range of cyber security problems is surveyed, namely, nature-inspired computing (NIC) paradigms, machine learning algorithms, and deep learning algorithms, based on exploratory analyses to identify the advantages of employing in enhancing cyber security techniques. Review on Intelligent Algorithms for Cyber Security

[1]  Guolong Chen,et al.  PSO-BPNN-Based Prediction of Network Security Situation , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[2]  Said El Kafhali,et al.  DDoS attack detection using machine learning techniques in cloud computing environments , 2017, 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech).

[3]  Yuefei Zhu,et al.  A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.

[4]  Arvinder Kaur,et al.  Hybridization of K-Means and Firefly Algorithm for intrusion detection system , 2018, Int. J. Syst. Assur. Eng. Manag..

[5]  Erdogan Dogdu,et al.  Phishing e-mail detection by using deep learning algorithms , 2018, ACM Southeast Regional Conference.

[6]  Michele Colajanni,et al.  On the effectiveness of machine and deep learning for cyber security , 2018, 2018 10th International Conference on Cyber Conflict (CyCon).

[7]  Andrew H. Sung,et al.  Detection of Phishing Attacks: A Machine Learning Approach , 2008, Soft Computing Applications in Industry.

[8]  Victor C. M. Leung,et al.  Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment , 2018, Wirel. Commun. Mob. Comput..

[9]  Xuan Dau Hoang,et al.  Botnet Detection Based On Machine Learning Techniques Using DNS Query Data , 2018, Future Internet.

[10]  Ghulam Ali Mallah,et al.  Implication of Genetic Algorithm in Cryptography to Enhance Security , 2018 .

[11]  Pradeep Kumar Krishnappa,et al.  Investigating Open Issues in Swarm Intelligence for Mitigating Security Threats in MANET , 2015 .

[12]  Karthikeyan Eswaramoorthy,et al.  Ant Colony Optimization Based Handoff Scheme and Verifiable Secret Sharing Security with M-M Scheme for VoIP , 2017 .

[13]  Benny Pinkas,et al.  Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples , 2018, 1802.04528.

[14]  Khaled M. Elleithy,et al.  A Highly Accurate Deep Learning Based Approach for Developing Wireless Sensor Network Middleware , 2018, IEEE Access.

[15]  Nidaa F. Hassan,et al.  Designing a smartphone honeypot system using performance counters , 2017 .

[16]  Jerry Murphree,et al.  Machine learning anomaly detection in large systems , 2016, 2016 IEEE AUTOTESTCON.

[17]  Je-Won Kang,et al.  Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security , 2016, PloS one.

[18]  Pierre Parrend,et al.  Morwilog: an ACO-based system for outlining multi-step attacks , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[19]  Anuradha Pillai,et al.  Applications of Machine Learning in Cyber Security , 2020, Handbook of Research on Machine and Deep Learning Applications for Cyber Security.

[20]  Mohd Afizi Mohd Shukran,et al.  A NOVEL ANOMALY-NETWORK INTRUSION DETECTION SYSTEM USING ABC ALGORITHMS , 2012 .

[21]  Waleed Ali,et al.  Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection , 2017 .

[22]  Monther Aldwairi,et al.  Application of artificial bee colony for intrusion detection systems , 2015, Secur. Commun. Networks.

[23]  Robert Sowah,et al.  Detection and Prevention of Man-in-the-Middle Spoofing Attacks in MANETs Using Predictive Techniques in Artificial Neural Networks (ANN) , 2019, J. Comput. Networks Commun..

[24]  Jinoh Kim,et al.  A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.

[25]  Sargur N. Srihari,et al.  Machine Learning for Signature Verification , 2006, ICVGIP.

[26]  M. A. Faizal,et al.  Machine Learning for HTTP Botnet Detection Using Classifier Algorithms , 2018 .

[27]  S. Anbuchelian,et al.  Improving security in Wireless Sensor Network using trust and metaheuristic algorithms , 2016, 2016 3rd International Conference on Computer and Information Sciences (ICCOINS).

[28]  Vineet Richariya,et al.  Ant Colony Optimization with Classification Algorithms used for Intrusion Detection , 2012 .

[29]  A HodashinskyI.,et al.  Constructing a Fuzzy Network Intrusion Classifier Based on Differential Evolution and Harmonic Search , 2018 .

[30]  Adel Bouhoula,et al.  Automatic Analysis of Web Service Honeypot Data Using Machine Learning Techniques , 2012, CISIS/ICEUTE/SOCO Special Sessions.

[31]  Kevin Curran,et al.  Pervasive and Ubiquitous Technology Innovations for Ambient Intelligence Environments , 2012 .

[32]  Nathan S. Netanyahu,et al.  DeepSign: Deep learning for automatic malware signature generation and classification , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[33]  L. M. R. J. Lobo,et al.  Use of Genetic Algorithm in Network Security , 2012 .

[34]  Matthew C. Valenti,et al.  Multibiometric secure system based on deep learning , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[35]  Charles C. Willow.  A Neural Network-Based Agent Framework for Mail Server Management , 2005, Int. J. Intell. Inf. Technol..

[36]  Alexander Pretschner,et al.  Automatically assessing vulnerabilities discovered by compositional analysis , 2018, MASES@ASE.

[37]  Naveen K. Chilamkurti,et al.  Distributed attack detection scheme using deep learning approach for Internet of Things , 2017, Future Gener. Comput. Syst..

[38]  Michal Choras,et al.  Machine Learning Techniques for Cyber Attacks Detection , 2013, IP&C.

[39]  Wei Cai,et al.  A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View , 2018, IEEE Access.

[40]  Dragos Gavrilut,et al.  Malware detection using machine learning , 2009, 2009 International Multiconference on Computer Science and Information Technology.

[41]  Yi Yi Aung,et al.  An Analysis of K-means Algorithm Based Network Intrusion Detection System , 2018 .

[42]  Habiba Chaoui,et al.  A security approach based on honeypots: Protecting Online Social network from malicious profiles , 2018, ArXiv.

[43]  Marcus Gutierrez,et al.  Adapting with Honeypot Configurations to Detect Evolving Exploits , 2017, AAMAS.

[44]  S. Prabha,et al.  Differential Evolution for Mobile Ad-hoc Networks A Review , 2018 .

[45]  Karim Faez,et al.  A New Lightweight Watchdog-Based Algorithm for Detecting Sybil Nodes in Mobile WSNs , 2018, Future Internet.

[46]  Yuansong Qiao,et al.  Future Multimedia System: SIP or the Advanced Multimedia System , 2011, Int. J. Ambient Comput. Intell..

[47]  Wei Wang,et al.  Web Phishing Detection Using a Deep Learning Framework , 2018, Wirel. Commun. Mob. Comput..

[48]  Philippe Owezarski,et al.  Unsupervised classification and characterization of honeypot attacks , 2014, 10th International Conference on Network and Service Management (CNSM) and Workshop.

[49]  Shih-Yuan Wang,et al.  Applying the Linguistic Strategy-Oriented Aggregation Approach to Determine the Supplier Performance with Ordinal and Cardinal Data Forms , 2011, Int. J. Fuzzy Syst. Appl..

[50]  Thiagarajan Revathi,et al.  PSO-based optimal peer selection approach for highly secure and trusted P2P system , 2016, Secur. Commun. Networks.

[51]  Bassey Isong,et al.  Identification of Compromised Nodes in MANETs using Machine Learning Technique , 2019, International Journal of Computer Network and Information Security.

[52]  David C. Yen,et al.  A Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means , 2015, TMIS.

[53]  R Vadivel.  PARTICLE SWARM OPTIMIZATION ALGORITHM (PSO) USED FOR SECURITY ENHANCEMENT IN MANET , 2018 .

[54]  Farha Haneef.  A FEATURE SELECTION TECHNIQUE FOR INTRUSION DETECTION SYSTEM BASED ON IWD AND ACO , 2017 .

[55]  Félix J. García Clemente,et al.  A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks , 2018, IEEE Access.