网络安全与网络取证：机器学习方法

Ibrahim Goni¹, Jerome Mishion Gumpy², Timothy Umar Maigari³, Murtala Muhammad⁴, Abdulrahman Saidu⁴
1、阿达马瓦州立大学计算机科学系
2、联邦大学计算机科学系
3、联邦教育学院计算机科学系
4、巴厘联邦理工学院计算机科学系

摘要

云计算和物联网的发展导致了世界各国（发达国家和发展中国家）的互联互通，全球网络为互联互通提供
了平台。数字取证是一个计算机安全领域，它使用软件应用程序和标准指南，支持从任何计算机设备中提取证据，
这些设备完全足以让法庭根据所获得信息的全面性、真实性和客观性进行使用和判断。由于物联网中每天都在发生
的攻击、威胁、病毒、入侵等最新形式，网络安全是全世界互联网用户最关心的问题。这项工作的目的是对机器学
习算法在网络安全和网络取证中的应用进行系统回顾，根据这一发现，对机器学习方法在网络取证和网络安全中的
最新应用进行了系统调查，还指出，网络安全有十个步骤；网络安全、用户教育和意识、恶意软件预防、可移动媒
体控制、安全配置、管理用户权限、事件管理、监控以及家庭和移动工作，为深入学习、计算智能、软计算在网络
安全和网络取证中的应用的进一步研究方向铺平道路。

关键词

网络安全；网络取证；网络空间；网络威胁；机器学习和深度学习

参考

[1] Shahzad S. (2015) protecting the integrity of digital

evidence and basic human rights during the process of digital

forensics. Ph. D. thesis Stockholm University.

[2] Abdalzim A. M. A. & Amin B. A. M. (2015) a survey

on mobile forensics for

android smart phones IOSR Journal of computer

engineering 17 (2) 15-19

[3] Nickson M. K., Victor R. K. & Venter H. (2019)

Divergency deep learning cognitive computing techniques

into cyber forensics Elservier Forensics Science international

synergy 1 (2019) 61-67.

[4] Rukayat A. A., Charles O. U. & Florence A. O. (2017)

computer forensics guidelines: a requirement for testing cyber

crime in Nigeria now?

[5] Casey E. (2016) Editorial- A sea change in digital

forensics and incident response. Digital investigation evidence

Elservier Ltd 17, A1-A2.

[6] Ehsan S. & Giti J. (2019) Seminars in proactive

artificial intelligence for cyber security consulting and

research, Systematic cybernetics and informatics 17 (1) 297-

305

[7] Bandir A. (2019) Forensics analysis using text

clustering in the age of large volume data: a review.

International journal of advanced computer and application.

10 (6), 72-76.

[8] Al-Jadir I., Wong K. W., Fing C. C. & Xie H. (2018)

Enhensing digital forensics analysis using memetic algorithm

feature selection method for document clustering 2018 IEEE

international conference on systems, Man and cybernetics

3673-3678.

[9] Sunil B. & Preeti B. (2018) Application of artificial

intelligence in cyber security. International journal of

engineering research in computer science and engineering 5

(4), 214-219.

[10] David O. A., Goodness O. & Etecte M. A. (2019)

Unbated cyber terrorism and huma security in Nigeria. Asian

social science 15 (11), 105-115.

[11] April (2014) threat start-SMS spam volume by

month of each region SC magazine. available online at http//

www.scmagazine.com/april-2014-threat-stats/slideshowz.

[12] Apruzze G., Colajanni M. F., Ferreti L., & Marchett

M. (2018) on the effectiveness of machine learning for cyber

security in 2018 IEEE international conference on cyber

conflict 371-390.

[13] Buckza A. L. & Guven E. (2016) A survey of data

mining and machine learning

methods for cyber security intrusion detection IEEE

communication survey and totorials 18 (2), 1153-1176.

[14] Biswas S. K. (2018) intrusion detection using

machine learning: A comparison study. International Journal

of pure and applied mathematics 118 (19), 101-114.

[15] Y. Xin, Kong L., Liu Z., Chen Y., Zhu H., Gao M.,

Hou H., & Wang C. Machine learning and deep learning

methods for cyber security. IEEE Access 6: 35365-35381

(2018).

[16] N. Miloseivic, Denghantanh A., Choo K. K. R.

Machine learning aided android malware classification.

Computer and electrical engineering 61: 266-274 (2017).

[17] B. Geluvaraj, Stawik P. M., Kumar T. A. the

future of cyber security: the major role of Artificial

intelligence, Machine learning and deep learning in cyber

space. International conference on computer network and

communication technologies Springer Singapore. 739-747

(2019).

[18] H. Mohammed B., Vinaykumar R., Soman K. P. A

short review on applications of deep learning for cyber security

(2018).

[19] M. Rege, Mbah R. B. K. Machine learning for cyber

defense and attack. in the 7th International conference on data

analysis 73-78 (2018).

[20] D. Ding, Hang Q. L., Xing Y., Ge X., and Zhang X. M.

A survey on security control and attack detection for industrial

cyber physical system. Neuro-computing. 275. 1674-1683

(2018).

[21] D. Berman S., Buczak A. L., Chavis J. S., Corbelt

C. L. A survey of deep learning methods for cyber security

information 10 (4): (2018).

[22] Y. Wang, Ye Z., Wan P., Zhao J. A survey of

dynamicspectrum allocation based on reinforcement

learningalgorithms in cognitive radio network. Artificial

intelligence review. 51 (3): 413-506 (2019).

[23] A. Abubakar, Paranggono B. Machine learning based

intrusion detection system for software defined networks.

7thInternational conference on Emerging security techniques

IEEE. 138-143. (2017).

[24] S. Jose, Malathi D., Reddy B., Jayaseeli D. A survey

on anomaly based host intrusion detection system. Journal of

physics. Conference series 1000 (1): (2018).

[25] S. Dey, Ye Q., Sampalli S. A Machine learning

basedintrusion detection scheme for data fusion in mobile

cloud involving heterogeneous clients network. Information

fussion 49: 205-215 (2019).

[26] P. Deshpande, Sharma S. C., Peddoju S. K., Junaid

S. HIDS: a host based intrusion detection system for cloud

computing environment. International journal of system

assuarance engineering and management. 9 (3): 567-576

(2018).

[27] M. Nobakht, Sivaraman V., Boreli R. A hostBased Intrusion detection and mitigation framework for smart

IoT using open flow in 11th International conference on

availability reliability and security IEEE. 147-156 (2016).

[28] A. Meshram, Christian H. Anomaly detection in

industrial networks using machine learning: A road map.

Machine learning for cyber physical system Springer Berlin

Heldelberg. 65-72 (2017).

[29] R. Devakunchari, Souraba, Prakhar M. A study

of cyber security using machine learning techniques.

International journal of innovative technology and exploring

engineering. 8 (7): 183-186 (2019).

[30] E. Alison N. FLUF: fuzzy logic utility framework

to support computer network defense decision making IEEE

(2016).

[31] A. Taylor, Leblanc S., Japkowicz N. Anomaly

detection in auto-mobile control network data with long short

term memory network in data science and advance analytics.

IEEE international conference. 130-139 (2016).

[32] O. Amosov S., Ivan Y. S., Amosovo S. G. Recognition

of abnormal traffic using deep neural networks and fuzzy logic.

International Multi-conference on industrial engineering and

modern technologies IEEE (2019).

[33] M. Gyun L. Artificial Intelligence for development

series: Report on AI and IoT in Security Aspect. (2018).

[34] L. Matt. Rise of machine: machine learning & its

cybersecurity applications, NCC group white paper (2017).

[35] National cyber security center UK, www.ncsc.gov.

uk.

[36] A. Nuril, Supriyanto (2019) Forensic Authentication

of WhatsApp Messenger Using the Information Retrieval

Approach. International Journal of Cyber Security and Digital

Forensics (IJCSDF) 8 (3): 206-212 (2019).

[37] A Marfianto, I Riadi. WhatsApp Messenger Forensic

Analysis Based on Android Using Text Mining Method.

International Journal of Cyber Security and Digital Forensics

(IJCSDF) 7 (3): 319-327 (2018).

[38] N Anwar, I. Riadi. Forensic Investigative Analysis

of WhatsApp Messenger Smartphone Against WhatsApp

WebBased, Journal Information Technology Electromagnetic

Computing and Information, 3 (1): 1-10 (2017).

[39] S. Ikhsani and C. Hidayanto, Whatsapp and LINE

Messenger Forensic Analysis with Strong and Valid Evidence

in Indonesia. Tek. ITS, 5 (2): 728-736 (2016).

[40] M. Ashawa, S. Morris. Analysis of Android Malware

Detection Techniques: A Systematic Review. International

Journal of Cyber Security and Digital Forensics (IJCSDF) 8 (3):

177-187 (2019).

[41] W. Songyang, Wang, P., Zhang, Y. Effective

detection of android malware based

on the usage of data flow APIs and machine learning:

Information and Software Technology, 75: 17--25 (2016).

[42] Anastasia, S., Gamayunov, D.: Review of the mobile

malware detection approaches: Parallel, Distributed and

NetworkBased Processing (PDP). In: Proc. 2015. IEEE 23rd

Euro micro International Conference, pp. 600—603 (2015).

[43] D. Anusha, Troia, F. D., Visaggio, C. A., Austin, T.

H., Stamp, M.: A comparison of static, dynamic, and hybrid

analysis for malware detection. Journal of Computer Virology

and Hacking Techniques, 13 (1) 1-12 (2017).

[44] S. Morgan, (2017). Cyber security Business

Report. Retrieved from CSO: https://www.csoonline.com/

article/3237674/ransomware/ransomware-damage-costspredicted-to-hit-115b-b y-2019.html.

[45] R. Collier, (2017). NHS ransomware attack spreads

worldwide. CMAJ, 189 (22), 786-787. https://doi.org/10.1503/

cmaj.1095434.

[46] H. Trisnasenjaya, I. Riadi Forensic Analysis of

Android-based Whats App Messenger Against Fraud Crime

Using The National Institute of Standard and Technology

Framework. International Journal of Cyber Security and

Digital Forensics (IJCSDF) 8 (1): 89-97 (2019).

[47] H. Parag Rughani. Artificial Intelligence Based

Digital Forensics Framework. International Journal of

Advanced Research in Computer Science. 8 (8): 10-14 (2017).

[48] 2016: Current State of Cybercrime, RSA Whitepaper,

2016.

[49] World Internet Users and 2017 Population Stats,

accessed from http://http://www.internetworldstats.com/stats.

htm.

[50] R. Mark. Computer forensics: Basics. Lecture note

Purdue University (2004).

[51] Ibrahim Goni & Ahmed L. (2015) Propose NeuroFuzzyGenetic Intrusion Detection System International

Journal of Computer Applications Vol. 115 No. 8 available

online at http://www.ijcaonline.com/archives/volume115/

number8/20169-2320.