• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.1
• /
• pp.47-55
• /
• 2009

Bot is a kind of worm/virus that is remotely controlled by a herder. Bot can be used to launch distributed denial-of-service(DDoS) attacks or send spam e-mails etc. Launching cyber attacks using malicious Bots is motivated by increased monetary gain which is not the objective of worm/virus. However, it is very difficult for infected user to detect this infection of Botnet which becomes more serious problems. This is why botnet is a dangerous, malicious program. The Bot DNS Sinkhole is a domestic bot mitigation scheme which will be proved in this paper as one of an efficient ways to prevent malicious activities caused by bots and command/control servers. In this paper, we analysis botnet activities over more than one-year period, including Bot's lifetime, Bot command/control server's characterizing. And we analysis more efficient ways to prevent botnet activities. We have showed that DNS sinkhole scheme is one of the most effective Bot mitigation schemes.

• The Journal of the Korea Contents Association
• /
• v.5 no.5
• /
• pp.172-181
• /
• 2005

The latest attack type against network traffic appeared by worm and bot that are advanced in DDoS. It is difficult to detect them because they are diversified, intelligent, concealed and automated. The exisiting traffic analysis method using SNMP has a vulnerable problem; it considers normal P2P and other application program to be harmful traffic. It also has limitation that does not analyze advanced programs such as worm and bot to harmful traffic. Therefore, we analyzed harmful traffic out Protocol and Port analysis. We also classified traffic by protocol, well-known port, P2P port, existing attack port, and specification port, apply singularity weight to detect, and analyze attack availability. As a result of simulation, it is proved that it can effectively detect P2P application, worm, bot, and DDoS attack.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.1482-1485
• /
• 2009

DDoS 공격은 네트워크 보안에 큰 피해를 미치는 공격기법의 하나로써, 국내외로 많은 피해를 유발하고 있으며, 최근에도 DDoS 공격에 의한 피해는 빈번하게 보고되고 있다. DDoS 공격은 실제 공격에 앞서 웜과 악성 BOT을 이용하여 공격을 직접 수행할 호스트를 감염시킨다. 웜과 악성 BOT이 타깃 호스트를 감염시키기 전에 반드시 수행하는 것이 취약점에 대한 스캐닝이다. 본 논문에서는 웜과 악성 BOT의 스캐닝 행위에 초점을 맞추어 DDoS 공격으로부터 안전한 네트워크를 구축하기 위한 역 IP spoofing을 이용한 DDoS 웜 스캐닝 트래픽의 처리기법을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.6A
• /
• pp.107-114
• /
• 2008

The Bot is a kind of worm/virus that can be used to launch the distributed denial-of-service(DDoS) attacks or send massive amount of spam e-mails, etc. A lot of organizations make an effort to counter the Botnet's attacks. In Korea, we use DNS sinkhole system to protect from the Botnet's attack, while in Japan "so called" CCC(Cyber Clean Center) has been developed to protect from the Botnet's attacks. But in case of DNS sinkhole system, there is a problem since it cannot cure the Bot infected PCs themselves and in case of CCC there is a problem since only 30% of users with the Botnet-infected PCs can cooperate to cure themself. In this paper we propose a new method that prevent the Botnet's attacks and cure the Bot-infected PCs at the same time.

• The KIPS Transactions:PartC
• /
• v.13C no.4 s.107
• /
• pp.405-414
• /
• 2006

We present a symptom based taxonomy for network security. This taxonomy classifies attacks in the network using early symptoms of the attacks. Since we use the symptom it is relatively easy to access the information to classify the attack. Furthermore we are able to classify the unknown attack because the symptoms of unknown attacks are correlated with the one of known attacks. The taxonomy classifies the attack in two stages. In the first stage, the taxonomy identifies the attack in a single connection and then, combines the single connections into the aggregated connections to check if the attacks among single connections may create the distribute attack over the aggregated connections. Hence, it is possible to attain the high accuracy in identifying such complex attacks as DDoS, Worm and Bot We demonstrate the classification of the three major attacks in Internet using the proposed taxonomy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.5
• /
• pp.1445-1462
• /
• 2012

While botnets are used for various malicious activities, it is well known that they are widely used for email spam. Though the spam filtering systems currently in use block IPs that send email spam, simply blocking the IPs of zombie PCs participating in a botnet is not enough to prevent the spamming activities of the botnet because these IPs can easily be changed or manipulated. This IP blocking is also insufficient to prevent crimes other than spamming, as the botnet can be simultaneously used for multiple purposes. For this reason, we propose a system that detects botnets and zombie PCs based on email spam analysis. This study introduces the concept of "group pollution level" - the degree to which a certain spam group is suspected of being a botnet - and "IP pollution level" - the degree to which a certain IP in the spam group is suspected of being a zombie PC. Such concepts are applied in our system that detects botnets and zombie PCs by grouping spam mails based on the URL links or attachments contained, and by assessing the pollution level of each group and each IP address. For empirical testing, we used email spam data collected in an "email spam trap system" - Korea's national spam collection system. Our proposed system detected 203 botnets and 18,283 zombie PCs in a day and these zombie PCs sent about 70% of all the spam messages in our analysis. This shows the effectiveness of detecting zombie PCs by email spam analysis, and the possibility of a dramatic reduction in email spam by taking countermeasure against these botnets and zombie PCs.