2 research outputs found
NXNSAttack: Recursive DNS Inefficiencies and Vulnerabilities
This paper exposes a new vulnerability and introduces a corresponding attack,
the NoneXistent Name Server Attack (NXNSAttack), that disrupts and may paralyze
the DNS system, making it difficult or impossible for Internet users to access
websites, web e-mail, online video chats, or any other online resource. The
NXNSAttack generates a storm of packets between DNS resolvers and DNS
authoritative name servers. The storm is produced by the response of resolvers
to unrestricted referral response messages of authoritative name servers. The
attack is significantly more destructive than NXDomain attacks (e.g., the Mirai
attack): i) It reaches an amplification factor of more than 1620x on the number
of packets exchanged by the recursive resolver. ii) In addition to the negative
cache, the attack also saturates the 'NS' section of the resolver caches. To
mitigate the attack impact, we propose an enhancement to the recursive resolver
algorithm, MaxFetch(k), that prevents unnecessary proactive fetches. We
implemented the MaxFetch(1) mitigation enhancement on a BIND resolver and
tested it on real-world DNS query datasets. Our results show that MaxFetch(1)
degrades neither the recursive resolver throughput nor its latency. Following
the discovery of the attack, a responsible disclosure procedure was carried
out, and several DNS vendors and public providers have issued a CVE and patched
their systems
Frequent Elements with Witnesses in Data Streams
Detecting frequent elements is among the oldest and most-studied problems in
the area of data streams. Given a stream of data items in , the objective is to output items that appear at least times, for some
threshold parameter , and provably optimal algorithms are known today.
However, in many applications, knowing only the frequent elements themselves is
not enough: For example, an Internet router may not only need to know the most
frequent destination IP addresses of forwarded packages, but also the
timestamps of when these packages appeared or any other meta-data that
"arrived" with the packages, e.g., their source IP addresses.
In this paper, we introduce the witness version of the frequent elements
problem: Given a desired approximation guarantee and a desired
frequency , where is the frequency of the most frequent
item, the objective is to report an item together with at least
timestamps of when the item appeared in the stream (or any other meta-data that
arrived with the items). We give provably optimal algorithms for both the
insertion-only and insertion-deletion stream settings: In insertion-only
streams, we show that space is
necessary and sufficient for every integral . In
insertion-deletion streams, we show that space is necessary and sufficient, for every .Comment: Fixed the statement of Lemma 5.1, introduction update