23,644 research outputs found
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
Comparison of System Call Representations for Intrusion Detection
Over the years, artificial neural networks have been applied successfully in
many areas including IT security. Yet, neural networks can only process
continuous input data. This is particularly challenging for security-related
non-continuous data like system calls. This work focuses on four different
options to preprocess sequences of system calls so that they can be processed
by neural networks. These input options are based on one-hot encoding and
learning word2vec or GloVe representations of system calls. As an additional
option, we analyze if the mapping of system calls to their respective kernel
modules is an adequate generalization step for (a) replacing system calls or
(b) enhancing system call data with additional information regarding their
context. However, when performing such preprocessing steps it is important to
ensure that no relevant information is lost during the process. The overall
objective of system call based intrusion detection is to categorize sequences
of system calls as benign or malicious behavior. Therefore, this scenario is
used to evaluate the different input options as a classification task. The
results show, that each of the four different methods is a valid option when
preprocessing input data, but the use of kernel modules only is not recommended
because too much information is being lost during the mapping process.Comment: 12 pages, 1 figure, submitted to CISIS 201
CONDOR: A Hybrid IDS to Offer Improved Intrusion Detection
Intrusion Detection Systems are an accepted and very
useful option to monitor, and detect malicious activities.
However, Intrusion Detection Systems have inherent limitations which lead to false positives and false negatives; we propose that combining signature and anomaly based IDSs should be examined. This paper contrasts signature and anomaly-based IDSs, and critiques some proposals about hybrid IDSs with signature and heuristic capabilities, before considering some of their contributions in order to include them as main features of a new hybrid IDS named CONDOR (COmbined Network intrusion Detection ORientate), which is designed to offer superior pattern analysis and anomaly detection by reducing false positive rates and administrator intervention
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