DNS Based Detection of Spam Bots and Host Search Activity (original) (raw)

An empirical study of spam traffic and the use of DNS black lists

Proceedings of the 4th ACM SIGCOMM conference on Internet measurement - IMC '04, 2004

This paper presents quantitative data about SMTP traffic to MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) based on packet traces taken in December 2000 and February 2004. These traces show that the volume of email has increased by 866% between 2000 and 2004. Local mail hosts utilizing black lists generated over 470,000 DNS lookups, which accounts for 14% of all DNS lookups that were observed on the border gateway of CSAIL on a given day in 2004. In comparison, DNS black list lookups accounted for merely 0.4% of lookups in December 2000.

Detecting Botnet Activities Based on Abnormal DNS traffic

Arxiv preprint arXiv: …, 2009

The botnet is considered as a critical issue of the Internet due to its fast growing mechanism and affect. Recently, Botnets have utilized the DNS and query DNS server just like any legitimate hosts. In this case, it is difficult to distinguish between the legitimate DNS traffic and illegitimate DNS traffic. It is important to build a suitable solution for botnet detection in the DNS traffic and consequently protect the network from the malicious Botnets activities. In this paper, a simple mechanism is proposed to monitors the DNS traffic and detects the abnormal DNS traffic issued by the botnet based on the fact that botnets appear as a group of hosts periodically. The proposed mechanism is also able to classify the DNS traffic requested by group of hosts (group behavior) and single hosts (individual behavior), consequently detect the abnormal domain name issued by the malicious Botnets. Finally, the experimental results proved that the proposed mechanism is robust and able to classify DNS traffic, and efficiently detects the botnet activity with average detection rate of 89%.

A survey of botnet detection based on DNS

Botnet is a thorny and a grave problem of today’s Internet, resulting in economic damage for organizations and individuals. Botnet is a group of compromised hosts running malicious software program for malicious purposes, known as bots. It is also worth mentioning that the current trend of botnets is to hide their identities (i.e., the command and control server) using the DNS services to hinder their identification process. Fortunately, different approaches have been proposed and developed to tackle the problem of botnets; however, the problem still rises and emerges causing serious threat to the cyberspace-based businesses and individuals. Therefore, this paper comes up to explore the various botnet detection techniques through providing a survey to observe the current state of the art in the field of botnet detection techniques based on DNS traffic analysis. To the best of our knowledge, this is the first survey to discuss DNS-based botnet detection techniques in which the problems, existing solutions and the future research direction in the field of botnet detection based on DNS traffic analysis for effective botnet detection mechanisms in the future are explored and clarified.

Investigating DNS traffic anomalies for malicious activities

The Domain Name System (DNS) is one of the critical components of modern Internet networking. Proper Internet functions (such as mail delivery, web browsing and so on) are typically not possible without the use of DNS. However with the growth and commercialization of global networking, this protocol is often abused for malicious purposes which negatively impacts the security of Internet users. In this paper we perform security data analysis of DNS traffic at large scale for a prolonged period of time. In order to do this, we developed DNSPacketlizer, a DNS traffic analysis tool and deployed it at a mid-scale Internet Service Provider (ISP) for a period of six months. The findings presented in this paper demonstrate persistent abuse of the protocol by Botnet herders and antivirus software vendors for covert communication. Other suspicious or potentially malicious activities in DNS traffic are also discussed.

Detecting and Preventing the Malicious System based on DNS Analysis

Attackers, usually busy to launch malicious threat to damage the compromised host. Botnet's are newly developed technology by attackers and its duty to increase the traffic in DNS service to launch attacks. Due to increased traffic in DNS, botmaster's create a new channel between server and client; it has capability to command and control the Operating System and automatic generating more queries over DNS to increase the Traffic. Many botnet operators used HTTP server to pass the information. In this paper, we proposed viable approach called Wide Packet Inspection to analyze the DNS traffic to control and avoid the Botnet. This paper provides a countermeasure against botnet operators to slow down the bot activity.

Detection of spam hosts and spam bots using network flow traffic modeling

Proceedings of the 3rd Usenix Conference on Large Scale Exploits and Emergent Threats Botnets Spyware Worms and More, 2010

In this paper, we present an approach for detecting e-mail spam originating hosts, spam bots and their respective controllers based on network flow data and DNS metadata. Our approach consists of first establishing SMTP traffic models of legitimate vs. spammer SMTP clients and then classifying unknown SMTP clients with respect to their current SMTP traffic distance from these models. An entropy-based traffic component extraction algorithm is then applied to traffic flows of hosts identified as e-mail spammers to determine whether their traffic profiles indicate that they are engaged in other exploits. Spam hosts that are determined to be compromised are processed further to determine their command-and-control using a two-stage approach that involves the calculation of several flow-based metrics, such as distance to common control traffic models, periodicity, and recurrent behavior. DNS passive replication metadata are analyzed to provide additional evidence of abnormal use of DNS to access suspected controllers. We illustrate our approach with examples of detected controllers in large HTTP(S) botnets such as Cutwail, Ozdok and Zeus, using flow data collected from our backbone network.

Detection of malicious payload distribution channels in DNS

2014 IEEE International Conference on Communications (ICC), 2014

Botmasters are known to use different protocols to hide their activities. Throughout the past few years, several protocols have been abused, and recently Domain Name System (DNS) also became a target of such malicious activities. In this paper, we study the use of DNS as a malicious payload distribution channel. We present a system to analyze the resource record activities of domain names and build DNS zone profiles to detect payload distribution channels. Our work is based on an extensive analysis of malware datasets for one year, and a near real-time feed of passive DNS traffic. The experimental results reveal a few previously unreported long-running hidden domains used by the Morto worm for distributing malicious payloads. Our experiments on passive DNS traffic indicate that our system can detect these channels regardless of the payload format.

Detecting Malicious Activity With DNS Backscatter Over Time

IEEE/ACM Transactions on Networking, 2017

Network-wide activity is when one computer (the originator) touches many others (the targets). Motives for activity may be benign (mailing lists, content-delivery networks, and research scanning), malicious (spammers and scanners for security vulnerabilities), or perhaps indeterminate (ad trackers). Knowledge of malicious activity may help anticipate attacks, and understanding benign activity may set a baseline or characterize growth. This paper identifies domain name system (DNS) backscatter as a new source of information about networkwide activity. Backscatter is the reverse DNS queries caused when targets or middleboxes automatically look up the domain name of the originator. Queries are visible to the authoritative DNS servers that handle reverse DNS. While the fraction of backscatter they see depends on the server's location in the DNS hierarchy, we show that activity that touches many targets appear even in sampled observations. We use information about the queriers to classify originator activity using machine-learning. Our algorithm has reasonable accuracy and precision (70-80%) as shown by data from three different organizations operating DNS servers at the root or country level. Using this technique, we examine nine months of activity from one authority to identify trends in scanning, identifying bursts corresponding to Heartbleed, and broad and continuous scanning of secure shell.

Winning with DNS Failures: Strategies for Faster Botnet Detection

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2012

Botnets such as Conficker and Torpig utilize high entropy domains for fluxing and evasion. Bots may query a large number of domains, some of which may fail. In this paper, we present techniques where the failed domain queries (NXDOMAIN) may be utilized for: (i) Speeding up the present detection strategies which rely only on successful DNS domains. (ii) Detecting Command and Control (C&C) server addresses through features such as temporal correlation and information entropy of both successful and failed domains. We apply our technique to a Tier-1 ISP dataset obtained from South Asia, and a campus DNS trace, and thus validate our methods by detecting Conficker botnet IPs and other anomalies with a false positive rate as low as 0.02%. Our technique can be applied at the edge of an autonomous system for real-time detection.

A framework for DNS based detection and mitigation of malware infections on a network

2011 Information Security for South Africa, 2011

Modern botnet trends have lead to the use of IP and domain fast-fluxing to avoid detection and increase resillience. These techniques bypass traditional detection systems such as blacklists and intrusion detection systems. DNS is one of the most prevalent protocols on modern networks and is essential for the correct operation of many network activities, including botnet activity. For this reason DNS forms the ideal candidate for monitoring, detecting and mitigating botnet activity. In this paper a system placed at the network edge is developed with the capability to detect fast-flux domains using DNS queries. Multiple domain features were examined to determine which would be most effective in the classification of domains. This is achieved using a C5.0 decision tree classifier and Bayesian statistics, with positive samples being labelled as potentially malicious and negative samples as legitimate domains. The system detects malicious domain names with a high degree of accuracy, minimising the need for blacklists. Statistical methods, namely Naive Bayesian, Bayesian, Total Variation distance and Probability distribution are applied to detect malicious domain names. The detection techniques are tested against sample traffic and it is shown that malicious traffic can be detected with low false positive rates.