[Retracted] Analysis of Intrusion Detection Approaches for Network Traffic Anomalies with Comparative Analysis on Botnets (2008–2020)

Table 1

Various DDoS attacks on botnet.

S/no.	Authors	Target	Description
1	Hill [8]	Tunisian government websites	Site blackout that incorporated the president, head administrator, service of industry, service of remote issues, and stock trade.
2	Somaiya [9]	Egyptian government websites	Site went detached from the most punctual beginning stage of the revolt until the point when the moment that the president wandered down.
3	Bright [10]	HB gary federal	Hacked by dumping 68,000 messages from the structure.
4	Author [11]	Operation ouraborus	Perils from a secretive attacker.
5	Segall [12]	New York (CNN money)	The monster attack hit the association’s server ranches with an enormous number of packs each second.
6	Uygar [13]	Operation empire state rebellion	Bank of America affected badly with unknown threats.
7	Takahashi [14]	Operation “SONY”	Play station network affected shockingly.
8	Technology [15]	Police department of Spain	DDoS attack.
9	Albanesius [16]	Operation@Malaysia https://Malaysia.gov.my	91 Malaysian government portals got breakdown. The attack started approximately at 7:30 pm GMT.
10	Mack [17]	Operation@Orlando	Governmental portals of Orlando abruptly got breakdown and then went offline. The attack was through LOIC tool and continued daily.
11	Halliday [18]	VISA/MASTER/WIKILEAKS/https://www.paypal.com	Major payment links got breakdown for high-ranked payment gateways.
12	Tech4Biz [19]	A stock exchange at Hong Kong Aug 15, 2011	100 of companies’ portal got breakdown through a single attack.
13	Mills [20]	Portals of jurisdiction department <https://justice.gov>; https://hadopi.fr; https://MPAA.org; https://BMI.com; https://copyright.com; viacom, anti-piracy.be/nl; https://vivendi.fr; https://ChrisDodd.com,	This was the greatest breakdown occurred in the year 2012 by a bewildered and unethical attacker. All the portals got shut down for approximately ten minutes.
14	Yair Meidan [21]	Mirai and BASHLITE	Extraction of behaviour snapshots of the network and using deep autoencoders for detecting anomalous network traffic from affected IoT devices.
15	Hoang and Nguyen [22]	Signature-based anomaly detection methods	Abnormal botnet detection methods are more efficient than the signature-based methods as they do not require prebuilt botnet signatures.
16	Vengatesan et al. [23]	Intrusion detection systems	A novel deep learning intrusion detection system is found effective than the traditional signature-based systems.
17	McDermott et al. [24]	Consumer IoT devices and networks	Deep learning application based on BLSTM-RNN for detection of various attack vectors used by mirai botnet.
18	Albanese et al. [25]	Moving target defense (MTD)	MTD creates an asymmetric uncertainty which provides the defender with an advantage over the attacker.
19	Koroniotis et al. [26]	IoT-enabled botnets	IoT-enabled botnets are technologically advanced and use high-speed networks that are capable of investigating activities.
20	Zhong et al. [27]	C&C channel in the bitcoin network	A novel P2P botnet model based on bitcoin transactions for preparing for new cyber threats.
21	Alieyan et al. [28]	DNS traffic	This aims at detection of abnormalities in the DNS query and responsive behaviors.
22	Banerjee et al. [29]	HoneyNet	HoneyNet provides the activity logs for the intrusion attempts as well as network traffic dump.
23	Lysenko et al. [30]	Low-rate DDOS attacks	This detection technique involves the network traffic analysis generated by botnets.
24	Hashemi et al. [31]	Graph clustering	Graph clustering is a major trend in machine learning aiming to graph the vertices.