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Register a new userUnilateral Antidotes to DNS Cache Poisoning
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Added by
Haya Shulman
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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We investigate defenses against DNS cache poisoning focusing on mechanisms that can be readily deployed unilaterally by the resolving organisation, preferably in a single gateway or a proxy. DNS poisoning is (still) a major threat to Internet security; determined spoofing attackers are often able to circumvent currently deployed antidotes such as port randomisation. The adoption of DNSSEC, which would foil DNS poisoning, remains a long-term challenge. We discuss limitations of the prominent resolver-only defenses, mainly port and IP randomisation, 0x20 encoding and birthday protection. We then present two new (unilateral) defenses: the sandwich antidote and the NAT antidote. The defenses are simple, effective and efficient, and can be implemented in a gateway connecting the resolver to the Internet. The sandwich antidote is composed of two phases: poisoning-attack detection and then prevention. The NAT antidote adds entropy to DNS requests by switching the resolvers IP address to a random address (belonging to the same autonomous system). Finally, we show how to implement the birthday protection mechanism in the gateway, thus allowing to restrict the number of DNS requests with the same query to 1 even when the resolver does not support this.
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In spite of the availability of DNSSEC, which protects against cache poisoning even by MitM attackers, many caching DNS resolvers still rely for their security against poisoning on merely validating that DNS responses contain some unpredictable values, copied from the re- quest. These values include the 16 bit identifier field, and other fields, randomised and validated by different patches to DNS. We investigate the prominent patches, and show how attackers can circumvent all of them, namely: - We show how attackers can circumvent source port randomisation, in the (common) case where the resolver connects to the Internet via different NAT devices. - We show how attackers can circumvent IP address randomisation, using some (standard-conforming) resolvers. - We show how attackers can circumvent query randomisation, including both randomisation by prepending a random nonce and case randomisation (0x20 encoding). We present countermeasures preventing our attacks; however, we believe that our attacks provide additional motivation for adoption of DNSSEC (or other MitM-secure defenses).
The Domain Name System (DNS) was created to resolve the IP addresses of the web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g. for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that can act as potential malicious insiders, and allows only the data owners to perform queries over these data. We evaluated our proposed solution by creating a proof-of-concept experimental setup that passively collects DNS data from a network and then uses the distributed ledger technology to store the data in an immutable ledger, thus providing a full historical overview of all the records.
Recommender systems play a crucial role in helping users to find their interested information in various web services such as Amazon, YouTube, and Google News. Various recommender systems, ranging from neighborhood-based, association-rule-based, matrix-factorization-based, to deep learning based, have been developed and deployed in industry. Among them, deep learning based recommender systems become increasingly popular due to their superior performance. In this work, we conduct the first systematic study on data poisoning attacks to deep learning based recommender systems. An attackers goal is to manipulate a recommender system such that the attacker-chosen target items are recommended to many users. To achieve this goal, our attack injects fake users with carefully crafted ratings to a recommender system. Specifically, we formulate our attack as an optimization problem, such that the injected ratings would maximize the number of normal users to whom the target items are recommended. However, it is challenging to solve the optimization problem because it is a non-convex integer programming problem. To address the challenge, we develop multiple techniques to approximately solve the optimization problem. Our experimental results on three real-world datasets, including small and large datasets, show that our attack is effective and outperforms existing attacks. Moreover, we attempt to detect fake users via statistical analysis of the rating patterns of normal and fake users. Our results show that our attack is still effective and outperforms existing attacks even if such a detector is deployed.
Virtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations such as Google and Cloudflare are deploying recently standardized protocols that encrypt DNS traffic between end users and recursive resolvers such as DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). In this paper, we examine whether encrypting DNS traffic can protect users from traffic analysis-based monitoring and censoring. We propose a novel feature set to perform the attacks, as those used to attack HTTPS or Tor traffic are not suitable for DNS characteristics. We show that traffic analysis enables the identification of domains with high accuracy in closed and open world settings, using 124 times less data than attacks on HTTPS flows. We find that factors such as location, resolver, platform, or client do mitigate the attacks performance but they are far from completely stopping them. Our results indicate that DNS-based censorship is still possible on encrypted DNS traffic. In fact, we demonstrate that the standardized padding schemes are not effective. Yet, Tor -- which does not effectively mitigate traffic analysis attacks on web traffic -- is a good defense against DoH traffic analysis.
We demonstrate the first practical off-path time shifting attacks against NTP as well as against Man-in-the-Middle (MitM) secure Chronos-enhanced NTP. Our attacks exploit the insecurity of DNS allowing us to redirect the NTP clients to attacker controlled servers. We perform large scale measurements of the attack surface in NTP clients and demonstrate the threats to NTP due to vulnerable DNS.
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