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Transition Analysis of Cyber Attacks Based on Long-Term Observation— 2-3 nicterReport —TransitionAnalysisofCyberAttacksBasedon Long-termObservation— NAKAZATO Junji and OHTAKA Kazuhiro In this report, we provide a statistical data concerning cyber attacks and malwares based on a long-term network monitoring on the nicter. Especially, we show a continuous observation report of Conficker, which is a pandemic malware since November 2008. In addition, we report a transition analysis of the scale of botnet activities. Keywords Incident analysis, Darknet, Network monitoring, Malware analysis 1 Introduction leverages the traffic as detected by the four black hole sensors placed on different network We have been monitoring the IP address environments as shown by Fig. 1. space that is reachable and unused on the ● Sensor I : Structure where live nets and Internet (i.e. darknets) on a large-scale to darknets coexist in a class B understand the overall impact inflicted by network infectious activities including malware. This ● Sensor II : Structure where only darknets report analyzes the darknet traffic that has exist in a class B network been monitored and accumulated over six ● Sensor III : Structure where a /24 subnet years by an incident analysis center named in a class B network is a dark- ＊1 the nicter[1][2] to provide changing trends of net cyber attacks and fluctuation of attacker host ● Sensor IV : Structure where live nets and activities as obtained by long-term monitor- darknets coexist in a class B ing. In particular, we focus on Conficker, a network worm that has triggered large-scale infections The traffic obtained by these four sensors since November 2008, and report its impact on is analyzed by different analysis engines[3][4] the Internet and its current activities. We also provided by the nicter and are stored over a extract the scan of botnets detected through long period of time with their analysis results. our long-term monitoring and report on the The nicter monitors darknet traffic on fluctuation of botnet scales over a period. a large-scale, while it deploys and operates The nicter places black hole sensors on a honeypots collecting malware to identify darknet to collect and analyze darknet traf- the malware causing the traffic. Honeypots fic on a large scale. A black hole sensor is a used by this report are categorized into five sensor collecting all incoming packets with- groups: Honeypot I deployed on 250 succes- out responding to their source, capable of sive IP addresses; Honeypot II and Honeypot monitoring the scan tendency of malware and Backscatter (i.e. responses to DDoS attacks ＊1 There exist darknets other than the monitored /24 net- based on falsified IP addresses). This report work, with the other portions composed of live nets. NAKAZATO Junji and OHTAKA Kazuhiro 27 scenes. Large-scale infections could have increased the load and traffic of infected hosts and made users and network administrators more keen on noticing infections, reducing the scale of infectious activities step by step. Furthermore, the emergence of botnets enabled multiple infectious hosts to behave collabora- tively so the infectious activities for each host became smaller in their scale. These compo- nents made it look highly unlikely in the late 2000s for malware to trigger large-scale infec- tions through networks[5]. In fact, we saw some decrease in the number of detected hosts as we started monitoring through the nicter. Fig.1 Summary of monitoring network 2.1 Transitionofthenumberofunique hostsandpackets III deployed on one IP address; Honeypot IV Figures 2 and 3 show the transition of deployed on three IP addresses; and Honeypot the number of unique sender IP addresses V whose data is provided by external organi- (“unique hosts”) and the number of packets zations. Honeypots I, II, and III are composed included in the traffic detected through the of software emulating general vulnerabilities darknet monitoring by the nicter. These two (i.e. low-interaction honeypot), while Honeypot figures illustrate the transition of the moving IV is set up so that it can rotate different ver- average of the number of unique hosts and sions of Windows operating systems on real packets detected by each sensor per day (win- machines and deal with unknown vulnerabili- dow size: 7 days). The monitoring start date of ties (i.e. high-interaction honeypot). each sensor is September 5, 2006 for Sensor I, The overall trend of the cyber attacks as December 14, 2004 for Sensor II, October 22, obtained through our darknet monitoring over 2007 for Sensor III, and July 10, 2009 for Sen- six years is reported by Chapter 2. Chapter 3 sor IV. You can see an increase in the number shows the statistical data concerning the mal- of unique hosts and packets for Sensor II at the ware we have collected and analyzed through time when Sensor I was added (September 5, the honeypots deployed since 2007. Chapter 4 2006). This is because the scale of Sensor II analyzes the fluctuation of botnet scales based was enhanced from a /18 network to a /16 net- on the botnet scans we have monitored over a work. period. Our final conclusion is summarized by Figure 2 suggests a slight declining in the Chapter 5. number of unique hosts between the moni- toring start time and late 2008. However, we 2 Transitionofattacksdetectedon can see a dramatic increase in the number of darknets detected unique hosts starting in November 2008: a 15-times growth with Sensor I and In the early 2000s, malware that triggered Sensor II and about a 10-times increase with large-scale infections including MSBlaster Sensor III, which covers a smaller scale in were rampant on the Internet. On the other monitoring. This increase of unique hosts is hand, the malware detected in the late 2000s impacted by the large-scale infections caused have evaded detection through their more by the Conficker worm[6][7]. The influences sophisticated and subtle mechanisms and inflicted by Conficker worm are explained in enjoyed their covert existence behind the Section 2.2. 28 Journal of the National Institute of Information and Communications Technology Vol. 58 Nos. 3/4 2011 1e+06 160000 somewhat declining between the beginning Sensor I (left axis) Sensor II (left axis) 900000 Sensor III (right axis) of the late 2000s and November 2008. On the Sensor IV (right axis) 140000 800000 other hand, the number of transmitted packets 120000 700000 continued at almost at the same level, plac- 100000 600000 ing the average number of packets transmit- 500000 80000 ted by one host on a growth path. Starting in 400000 60000 Number of unique hosts Number of unique hosts November 2008, when the Conficker worm 300000 40000 started its activities, the number of infected 200000 20000 100000 hosts began to multiply in a dramatic manner 0 0 and the average number of packets transmitted 01/01/05 01/01/06 01/01/07 01/01/08 01/01/09 01/01/10 01/01/11 Date by each host dropped to about one half of the previous level. Fig.2 Number of unique hosts 2.2 ImpactsinflictedbytheConficker 9e+06 1.4e+06 wormonnetworks Sensor I (left axis) Sensor II (left axis) Sensor III (right axis) 8e+06 Sensor IV (right axis) Starting around November 2008, the 1.2e+06 7e+06 large-scale infections triggered by Win32/ 1e+06 6e+06 Conficker (also known as Downadup) have 5e+06 800000 become a social problem. This worm is known 4e+06 600000 to leverage vulnerability in Windows Server Number of packets Number of packets 3e+06 services (MS08-067). This vulnerability 400000 2e+06 can be attacked through networks, enabling 200000 1e+06 computers (hosts) infected with Conficker 0 0 01/01/05 01/01/06 01/01/07 01/01/08 01/01/09 01/01/10 01/01/11 to look for another attack target and lead- Date ing to large-scale scans implemented on net- Fig.3 Number of packets works. We have analyzed the impacts caused by Conficker.A (occurring on November 21, 2008), Conficker.B (occurring on December Figure 3 shows a dramatic increase in the 29, 2008), Conficker.C (occurring on Febru- number of detected packets and unique hosts ary 20, 2009), and Conficker.D (occurring on starting in late 2008, along with the transi- March 4, 2009) based on [6] and [7]. Microsoft tion of the number of unique hosts. Before has not reported any new varieties of Conficker November 2008, the number of detected pack- with the last-detected Conficker.E discovered ets was almost flat despite some wild ups and on April 8, 2009[8]. downs. The number of unique hosts was on Figure 4 shows the transition of the num- the decline with the number of packets almost ber of unique hosts for each protocol (TCP flat, suggesting that the average number of and UDP) and the number of 445/TCP unique packets transmitted by each host was on the hosts used by Conficker worm for infections. increase. On the other hand, Sensor I, which Figure 4 (a) and (c) suggest that a vast major- has detected the largest number of packets, ity of hosts detected by Sensor I and Sensor has monitored seven times more packets since III as TCP packets are transmitting to the port November 11 2008. This rate is about half number 445. On the other hand, we have not of the growth rate in the number of detected detected any impacts on 445/TCP from Sen- unique hosts (i.e. about 15 times). This means sor II and Sensor IV, with an increase in the that the average number of packets transmitted number of TCP/UDP unique hosts detected by each host has almost halved in comparison four months later (i.e.
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