DNS Based Detection of Spam Bots and Host Search Activity

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.

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.

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.

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.

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.

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.

Journal of Computer Networks and Communications, 2021

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extrac...

2011

Computers connected to Internet are constantly threatened by different types of malware. One of the most important malware are botnets that convert infected computers into agents that follow actions instructed by a command-and-control server. A botmaster can control thousands of agents. This means a significant capacity to accomplish any kind of network attack (DoS), email spam or phishing. In this paper, communication peculiarities with the command-and-control server are used to provide an identification of computers infected by a botnet. This identification is based mainly in DNS records of registered domains where command-and-control servers are hosted. Therefore, processing overhead is reduced avoiding per packet or per flow network supervision.

Domain Name System (DNS) is one of the earliest vulnerable network protocols with various security gaps that have been exploited repeatedly over the last decades. DNS abuse is one of the most challenging threats for cybersecurity specialists. However, providing secure DNS is still a big challenging mission as attackers use complicated methodologies to inject malicious code in DNS inquiries. Many researchers have explored different machine learning (ML) techniques to encounter this challenge. However, there are still several challenges and barriers to utilizing ML. This paper introduces a systematic approach for identifying malicious and encrypted DNS queries by examining the network traffic and deriving statistical characteristics. Afterward, implementing several ML methods:

Tehnicki vjesnik - Technical Gazette, 2018

Botnets are considered as the primary threats on the Internet and there have been many research efforts to detect and mitigate them. Today, Botnet uses a DNS technique fast-flux to hide malware sites behind a constantly changing network of compromised hosts. This technique is similar to trustworthy Round Robin DNS technique and Content Delivery Network (CDN). In order to distinguish the normal network traffic from Botnets different techniques are developed with more or less success. The aim of this paper is to improve Botnet detection using an Intrusion Detection System (IDS) or router. A novel classification method for online Botnet detection based on DNS traffic features that distinguish Botnet from CDN based traffic is presented. Botnet features are classified according to the possibility of usage and implementation in an embedded system. Traffic response is analysed as a strong candidate for online detection. Its disadvantage lies in specific areas where CDN acts as a Botnet. A new feature based on search engine hits is proposed to improve the false positive detection. The experimental evaluations show that proposed classification could significantly improve Botnet detection. A procedure is suggested to implement such a system as a part of IDS.