6,046 research outputs found
TRIDEnT: Building Decentralized Incentives for Collaborative Security
Sophisticated mass attacks, especially when exploiting zero-day
vulnerabilities, have the potential to cause destructive damage to
organizations and critical infrastructure. To timely detect and contain such
attacks, collaboration among the defenders is critical. By correlating
real-time detection information (alerts) from multiple sources (collaborative
intrusion detection), defenders can detect attacks and take the appropriate
defensive measures in time. However, although the technical tools to facilitate
collaboration exist, real-world adoption of such collaborative security
mechanisms is still underwhelming. This is largely due to a lack of trust and
participation incentives for companies and organizations. This paper proposes
TRIDEnT, a novel collaborative platform that aims to enable and incentivize
parties to exchange network alert data, thus increasing their overall detection
capabilities. TRIDEnT allows parties that may be in a competitive relationship,
to selectively advertise, sell and acquire security alerts in the form of
(near) real-time peer-to-peer streams. To validate the basic principles behind
TRIDEnT, we present an intuitive game-theoretic model of alert sharing, that is
of independent interest, and show that collaboration is bound to take place
infinitely often. Furthermore, to demonstrate the feasibility of our approach,
we instantiate our design in a decentralized manner using Ethereum smart
contracts and provide a fully functional prototype.Comment: 28 page
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences
In this survey, we first briefly review the current state of cyber attacks,
highlighting significant recent changes in how and why such attacks are
performed. We then investigate the mechanics of malware command and control
(C2) establishment: we provide a comprehensive review of the techniques used by
attackers to set up such a channel and to hide its presence from the attacked
parties and the security tools they use. We then switch to the defensive side
of the problem, and review approaches that have been proposed for the detection
and disruption of C2 channels. We also map such techniques to widely-adopted
security controls, emphasizing gaps or limitations (and success stories) in
current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages.
Listing abstract compressed from version appearing in repor
Randomized Anagram Revisited
When compared to signature-based Intrusion Detection Systems (IDS), anomaly detectors present the potential advantage of detecting previously unseen attacks, which makes them an attractive solution against zero-day exploits and other attacks for which a signature is unavailable. Most anomaly detectors rely on machine learning algorithms to derive a model of normality that is later used to detect suspicious events. Such algorithms, however, are generally susceptible to evasion by means of carefully constructed attacks that are not recognized as anomalous. Different strategies to thwart evasion have been proposed over the last years, including the use of randomization to make somewhat uncertain how each packet will be processed. In this paper we analyze the strength of the randomization strategy suggested for Anagram, a well-known anomaly detector based on n-gram models. We show that an adversary who can interact with the system for a short period of time with inputs of his choosing will be able to recover the secret mask used to process packets. We describe and discuss an efficient algorithm to do this and report our experiences with a prototype implementation. Furthermore, we show that the specific form of randomization suggested for Anagram is a double-edged sword, as knowledge of the mask makes evasion easier than in the non-randomized case. We finally discuss a simple countermeasure to prevent our attacks.Publicad
- …