Oriental Journal of Computer Science and Technology

Open Access Open Access  Restricted Access Subscription Access

A Review on Cyber Security and the Fifth Generation Cyberattacks


Affiliations
1 Department of MCA, Sree Saraswathi Thyagaraja College, Pollachi, India
2 483, Lawley Road, Coimbatore, Tamil Nadu, India
 

Cyberattacks has become quite common in this internet era. The cybercrimes are getting increased every year and the intensity of damage is also increasing. providing security against cyber-attacks becomes the most significant in this digital world. However, ensuring cyber security is an extremely intricate task as requires domain knowledge about the attacks and capability of analysing the possibility of threats. The main challenge of cyber security is the evolving nature of the attacks. This paper presents the significance of cyber security along with the various risks that are in the current digital era. The analysis made for cyber-attacks and their statistics shows the intensity of the attacks. Various cyber security threats are presented along with the machine learning algorithms that can be applied on cyberattacks detection. The need for the fifth generation cybersecurity architecture is discussed.

Keywords

Cyberattacks, Cybersecurity, Fifth Generation, Machine Learning Algorithm, Security Threats.
User
Notifications
Font Size

  • Trustwave Global Securi t y. Report retrieved from: https://www2.trustwave.com/rs/815-RFM693/ images/2015_TrustwaveGlobalSecurityReport.pdf

  • International Organization for Standardization. ISO/IEC 27032:2012. Information technology—Security techniques— Guidelines for cybersecurity. 2012

  • Chowdhury A. Recent cyber security attacks and their mitigation approaches–An Overview. In International conference on applications and techniques in information security, Springer, Singapore. 2016; pp 54-65.

  • Passer i P. Cyber At tacks Stat ist ics Paolo Passeri, May 2016. http://www.hackmageddon.com/category/security/cyber-attacks-statistics/. Accessed 07 October 2016

  • Fischer EA. Creating a national framework for cybersecurity: an analysis of issues and options. Technical report. Congressional Research Service. 2005.

  • The Open Web Application Security Project (OWASP). 2018. Available online: https:// www.swascan.com/owasp/

  • The Open Web Application Security Project OWASP Top 10—the ten most critical web application security risks. The OWASP Foundation. 2018.

  • Check Point Research Survey of IT Security Professionals, sample size: 443 participants. 2018.

  • Check Point Mobile Threat Research Publications. 2017. Available Online: https:// research.checkpoint.com/check-point-mobile-research-team-looks-back-2017/

  • Cyber Attack Trends Analysis Key Insights to Gear Up for in 2019. Available Online: http://www.snt.hr/boxcontent/CheckPointSecurityReport2019_vol01.pdf

  • Check Point C-Level Perspective Survey. 2017. sample size: 59 C-Level Executives. Available Online: https://www.checkpoint.com/downloads/product-related/report/2018-security-report.pdf

  • Drucker H. Wu D. Vapnik VN. Support vector machines for spam categorization. IEEE Trans Neural Netw Publ IEEE Neural Netw Counc 1999; 10(5):1048–54

  • Cranor LF. Lamacchia BA. Spam!. Commun ACM. 1998; 41(8):74–83

  • SANS Institute. Top 15 Malicious Spyware Actions. 2018. Available Online: https://www.sans.org/security-resources/

  • Wang Z.J., Liu Y., Wang Z.J. E-mail filtration and classification based on variable weights of the Bayesian algorithm. Appl Mech Mater. 2014; 513–517:2111–2114.

  • Hsu W.C., Yu T.Y. E-mail spam filtering based on support vector machines with Taguchi method for parameter selection. J Converg Inf Technol 2010. 5(8):78–88.

  • Caruana G., Li M., Qi M. A MapReduce based parallel SVM for large scale spam filtering. In: IEEE 2011 eighth international conference on fuzzy systems and knowledge discovery (FSKD), 2011; pp 2659–2662.

  • Wu C.H. Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Syst Appl. 2009: 36(3):4321–4330.

  • Hazza Z.M., Aziz N.A. A new efficient text detection method for image spam filtering. Int Rev Comput Softw. 2015; 10(1):1–8.

  • Dhaya R., Poongodi M. Detecting software vulnerabilities in android using static analysis. 2015; pp 915–918.

  • Markel Z., Bilzor M. Building a machine learning classifier for malware detection. In: Second workshop on anti-malware testing research (WATeR). IEEE. Canterbury. UK. 2015.

  • Shijo P.V., Salim A. Integrated static and dynamic analysis for malware detection. Procedia Comput Sci. 2015; 46:804–811.

  • Divya S., Padmavathi G. A novel method for detection of internet worm malcodes using principal component analysis and multiclass support vector machine. Int J Secur Appl. 2014; 8(5):391–402

  • Akinyelu A.A., Adewumi A.O. Classification of phishing email using random forest machine learning technique. J Appl Math 2014; pp 1–6.

  • Santhana Lakshmi V., Vijaya M.S. Efficient prediction of phishing websites using supervised learning algorithms. Procedia Eng. 2012; 30:798–805.

  • Check point 2018 security report. 2018. Available Online: https://www.checkpoint.com/downloads/product-related/report/2018-security-report.pdf.


Abstract Views: 323

PDF Views: 0




  • A Review on Cyber Security and the Fifth Generation Cyberattacks

Abstract Views: 323  |  PDF Views: 0

Authors

A. Saravanan
Department of MCA, Sree Saraswathi Thyagaraja College, Pollachi, India
S. Sathya Bama
483, Lawley Road, Coimbatore, Tamil Nadu, India

Abstract


Cyberattacks has become quite common in this internet era. The cybercrimes are getting increased every year and the intensity of damage is also increasing. providing security against cyber-attacks becomes the most significant in this digital world. However, ensuring cyber security is an extremely intricate task as requires domain knowledge about the attacks and capability of analysing the possibility of threats. The main challenge of cyber security is the evolving nature of the attacks. This paper presents the significance of cyber security along with the various risks that are in the current digital era. The analysis made for cyber-attacks and their statistics shows the intensity of the attacks. Various cyber security threats are presented along with the machine learning algorithms that can be applied on cyberattacks detection. The need for the fifth generation cybersecurity architecture is discussed.

Keywords


Cyberattacks, Cybersecurity, Fifth Generation, Machine Learning Algorithm, Security Threats.

References





DOI: https://doi.org/10.13005/ojcst12.02.04