Leveraging client-side DNS failure patterns to identify malicious behaviors (original) (raw)

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.

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.

DomainProfiler: toward accurate and early discovery of domain names abused in future

International Journal of Information Security

Domain names are at the base of today's cyber-attacks. Attackers abuse the domain name system (DNS) to mystify their attack ecosystems; they systematically generate a huge volume of distinct domain names to make it infeasible for blacklisting approaches to keep up with newly generated malicious domain names. To solve this problem, we propose DomainProfiler for discovering malicious domain names that are likely to be abused in future. The key idea with our system is to exploit temporal variation patterns (TVPs) of domain names. The TVPs of domain names include information about how and when a domain name has been listed in legitimate/popular and/or malicious domain name lists. On the basis of this idea, our system actively collects historical DNS logs, analyzes their TVPs, and predicts whether a given domain name will be used for malicious purposes. Our evaluation revealed that DomainProfiler can predict malicious domain names 220 days beforehand with a true positive rate of 0.985. Moreover, we verified the effectiveness of our system in terms of the benefits from our TVPs and defense against cyber-attacks. Keywords Network-level security and protection • Domain name • DNS • Malware • Temporal variation pattern This paper is the extended version of the paper presented at IEEE/IFIP DSN 2016 [15].

An empirical reexamination of global DNS behavior

Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM, 2013

The performance and operational characteristics of the DNS protocol are of deep interest to the research and network operations community. In this paper, we present measurement results from a unique dataset containing more than 26 billion DNS query-response pairs collected from more than 600 globally distributed recursive DNS resolvers. We use this dataset to reaffirm findings in published work and notice some significant differences that could be attributed both to the evolving nature of DNS traffic and to our differing perspective. For example, we find that although characteristics of DNS traffic vary greatly across networks, the resolvers within an organization tend to exhibit similar behavior. We further find that more than 50% of DNS queries issued to root servers do not return successful answers, and that the primary cause of lookup failures at root servers is malformed queries with invalid TLDs. Furthermore, we propose a novel approach that detects malicious domain groups using temporal correlation in DNS queries. Our approach requires no comprehensive labeled training set, which can be difficult to build in practice. Instead, it uses a known malicious domain as anchor, and identifies the set of previously unknown malicious domains that are related to the anchor domain. Experimental results illustrate the viability of this approach, i.e. , we attain a true positive rate of more than 96%, and each malicious anchor domain results in a malware domain group with more than 53 previously unknown malicious domains on average.

Detecting algorithmically generated malicious domain names

Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, 2010

Recent Botnets such as Conficker, Kraken and Torpig have used DNS based "domain fluxing" for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such "domain fluxes" in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP-addresses. We present and compare the performance of several distance metrics, including KL-distance, Edit distance and Jaccard measure. We train by using a good data set of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad data sets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives.

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.

Analysis and Investigation of Malicious DNS Queries Using CIRA-CIC-DoHBrw-2020 Dataset

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:

Detecting Algorithmically Generated Domain-Flux Attacks With DNS Traffic Analysis

IEEE/ACM Transactions on Networking, 2012

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers

Sensors, 2016

The Domain Name System (DNS) is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS). The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting) and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers.

Tracking and Characterizing Botnets Using Automatically Generated Domains

Modern botnets rely on domain-generation algorithms (DGAs) to build resilient command-and-control infrastructures. Recent works focus on recognizing automatically generated domains (AGDs) from DNS traffic, which potentially allows to identify previously unknown AGDs to hinder or disrupt botnets' communication capabilities.

Leveraging client-side DNS failure patterns to identify malicious behaviors (original) (raw)

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