1 |
R. J. Hyndman and G. Athanasopoulos, Forecasting principles and practice, London: Bowker-Saur, Pharo, 1990.
|
2 |
E. S. Gardner, Exponential smoothing: the state of the art part ii, Int J Forecast 22 (2006), no. 4, 637-666.
DOI
|
3 |
P. Goodwin, The holt-winters approach to exponential smoothing: 50 years old and going strong, Foresight: The International Journal of Applied Forecasting (2010), no. 19, 30-33.
|
4 |
Y. Shu et al, Wireless traffic modeling and prediction using seasonal arima models, IEEE International Conference on Communications, 2003. ICC '03, vol 3, May 2003, pp. 1675-1679 vol. 3.
|
5 |
D. Joseph Dean, H. Nguyen, and X. Gu, UBL: unsupervised behavior learning for predicting performance anomalies in virtualized cloud systems, ICAC (2012).
|
6 |
H. Gunes Kayacik, A. Nur Zincir-Heywood, and M.I. Heywood, A hierarchical som-based intrusion detection system, Eng Appl Artif Intell 20 (2007), no. 4, 439-451.
DOI
|
7 |
T. V. Phan and M. Park, Efficient distributed denial-of-service attack defense in sdn-based cloud, IEEE Access (2019), 1-1.
|
8 |
Minisom, Available from: https://github.com/JustGlowing/minisom.
|
9 |
Bonesi, Available from: https://github.com/Markus-Go/bonesi/.
|
10 |
Gartner says 8.4 billion connected "things" will be in use in 2017, up 31 percent from 2016, Available from: https://www.gartner.com/en/newsroom/press-releases/2017-02-07-gartner-says-8-billionconnected-things-will-be-in-use-in-2017-up-31-percent-from-20.
|
11 |
C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, Ddos in the iot: Mirai and other botnets, Computer 50 (2017), no. 7, 80-84.
DOI
|
12 |
Quarterly security reports, glogal state of the internet security and ddos attack reports, 2018, Available from: https://www.akamai.com/uk/en/about/our-thinking/state-of-the-internet-report/globalstate-of-the-internet-security-ddos-attack-reports.jsp [last accessed April 2018].
|
13 |
R. Vadehra, N. Chowdhary, and J. Malhotra, Impact evaluation of distributed denial of service attacks using ns2, International Journal of Security and Its Applications 9 (2015), no. 8, 303-316.
DOI
|
14 |
N. Hoque, D. K. Bhattacharyya, and J. K. Kalita, Botnet in ddos attacks: trends and challenges, IEEE Communications Surveys Tutorials 17 (2015), no. 4, 2242-2270.
DOI
|
15 |
Mirai botnet, njccic, 2018, Available from: https://www.cyber.nj.gov/threat-profi les/botnet-variants/mirai-botnet [last accessed April 2018].
|
16 |
Imperva, DDoS Attacks, Available from: https://www.incapsula.com/ddos/ddos-attacks/ [last accessed Feb 2019].
|
17 |
Z. T. Fernando, I. S. Thaseen, and C. A. Kumar, Network attacks identification using consistency based feature selection and self organizing maps, 2014 First International Conference on Networks Soft Computing (ICNSC2014), Aug 2014, pp. 162-166.
|
18 |
S. Kumar. Survey of current network intrusion detection techniques, Available from: https://www.cse.wustl.edu/jain/cse571-07/ftp/ids.pdf [last accessed March 2018].
|
19 |
N. Sultana et al, Survey on sdn based network intrusion detection system using machine learning approaches, Peer-to-Peer Networking and Applications 12 (2019), 493-501.
DOI
|
20 |
N.-N. Dao et al, Securing heterogeneous iot with intelligent ddos attack behavior learning, CoRR abs/1711.06041 (2017).
|
21 |
A. A. Aburomman and M. Bin Ibne Reaz, Survey of learning methods in intrusion detection systems, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), Nov 2016, pp. 362-365.
|
22 |
L. Wang and R. Hones, Big data analytics for network intrusion detection: a survey, International Journal of Networks and Communications (2017).
|
23 |
E. Hodo et al, Shallow and deep networks intrusion detection system: a taxonomy and survey, CoRR abs/1701.02145 (2017).
|
24 |
S. Fitriani, S. Mandala, and M. A. Murti, Review of semi-supervised method for intrusion detection system, 2016 Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), Nov 2016, pp. 36-41.
|
25 |
K. Lu et al, Robust and efficient detection of ddos attacks for largescale internet, Comput Netw 51 (2007), 5036-5056.
DOI
|
26 |
M. Sachdeva, S. Gurvinder, and K. Saluja, Deployment of distributed defense against ddos attacks in ISP domain, International Journal of Computer Applications 15 (2011), 25-31.
|
27 |
Z. Liu et al, Umbrella: Enabling isps to offer readily deployable and privacy-preserving ddos prevention services, IEEE Trans Inf Forensics Secur 14 (2019), no. 4, 1098-1108.
DOI
|
28 |
B. Rodrigues et al, A blockchain-based architecture for collaborative ddos mitigation with smart contracts, Lecture Notes in Computer Science Security of Networks and Services in an All-Connected World (2017), 16-29.
|
29 |
I. Ko, D. Chambers, and E. Barrett, A lightweight ddos attack mitigation system within the ISP domain utilising self-organizing map: Volume 2, 01, 2019.
|
30 |
K. Choksi, P. B. Shah, and O. Kale, Intrusion detection system using self organizing map: a survey, 2014.
|
31 |
M. Fahad Umer, M. Sher, and Y. Bi, A twostage flow-based intrusion detection model for next-generation networks, PLoS ONE 13, (2018), no. e0180945, 1-20.
|
32 |
J. McHugh, Testing intrusion detection systems: A critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory, ACM Trans. Inf. Syst. Secur. 3 (2000), 262-294.
DOI
|
33 |
P. Lichodzijewski, A. Zincir-Heywood, M. I. Heywood, Dynamic intrusion detection using self-organizing maps, (2019).
|
34 |
V. K. Pachghare, P. Kulkarni, and D. M. Nikam, Intrusion detection system using self organizing maps, 2009 International Conference on Intelligent Agent Multi-Agent Systems, July 2009, pp. 1-5.
|
35 |
Z. Yong-xiong, W. Liang-ming, and Y. Lu-xia, A network attack discovery algorithm based on unbalanced sampling vehicle evolution strategy for intrusion detection, Int J Comput Appl (2017), 1-9. https://doi.org/10.1080/1206212X.2017.1397387
DOI
|
36 |
A. Midzic, Z. Avdagic, and S. Omanovic, Intrusion detection system modeling based on neural networks and fuzzy logic, 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES), June 2016, pp. 189-194.
|
37 |
S. Zhang et al, Psom: Periodic self-organizing maps for unsupervised anomaly detection in periodic time series, 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), June 2017, pp. 1-6.
|
38 |
Anomaly detection dataset, version 1.0, Available from: https://webscope.sandbox.yahoo.com/catalog.php?datatype=s.
|
39 |
T. Kohonen, The self-organizing map, Proc IEEE 78 (1990), no. 9, 1464-1480.
DOI
|
40 |
M. Jenkins, Time series analysis, forecasting and control, holden-day, Journal of the Royal Statistical Society 134 (1976), no 3.
|