12,610 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
Autoencoders: A Low Cost Anomaly Detection Method for Computer Network Data Streams
Computer networks are vulnerable to cyber attacks that can affect the confidentiality, integrity and availability of mission critical data. Intrusion detection methods can be employed to detect these attacks in real-time. Anomaly detection offers the advantage of detecting unknown attacks in a semi-supervised fashion. This paper aims to answer the question if autoencoders, a type of semisupervised feedforward neural network, can provide a low cost anomaly detector method for computer network data streams.
Autoencoder methods were evaluated online with the KDD’99 and UNSW-NB15 data sets, demonstrating that running time and labeling cost are significantly reduced compared to traditional online classification techniques for similar detection performance.
Further research would consider the trade-off between single vs stacked networks, multi-label classification, concept drift detection and active learning
Machine learning approach for detection of nonTor traffic
Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset
A cognitive based Intrusion detection system
Intrusion detection is one of the primary mechanisms to provide computer
networks with security. With an increase in attacks and growing dependence on
various fields such as medicine, commercial, and engineering to give services
over a network, securing networks have become a significant issue. The purpose
of Intrusion Detection Systems (IDS) is to make models which can recognize
regular communications from abnormal ones and take necessary actions. Among
different methods in this field, Artificial Neural Networks (ANNs) have been
widely used. However, ANN-based IDS, has two main disadvantages: 1- Low
detection precision. 2- Weak detection stability. To overcome these issues,
this paper proposes a new approach based on Deep Neural Network (DNN. The
general mechanism of our model is as follows: first, some of the data in
dataset is properly ranked, afterwards, dataset is normalized with Min-Max
normalizer to fit in the limited domain. Then dimensionality reduction is
applied to decrease the amount of both useless dimensions and computational
cost. After the preprocessing part, Mean-Shift clustering algorithm is the used
to create different subsets and reduce the complexity of dataset. Based on each
subset, two models are trained by Support Vector Machine (SVM) and deep
learning method. Between two models for each subset, the model with a higher
accuracy is chosen. This idea is inspired from philosophy of divide and
conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to
reduce the error from the previous step, an ANN model is trained to gain and
use the results in order to be able to predict the attacks. We can reach to
95.4 percent of accuracy. Possessing a simple structure and less number of
tunable parameters, the proposed model still has a grand generalization with a
high level of accuracy in compared to other methods such as SVM, Bayes network,
and STL.Comment: 18 pages, 6 figure
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data
The Industrial Internet of Things drastically increases connectivity of
devices in industrial applications. In addition to the benefits in efficiency,
scalability and ease of use, this creates novel attack surfaces. Historically,
industrial networks and protocols do not contain means of security, such as
authentication and encryption, that are made necessary by this development.
Thus, industrial IT-security is needed. In this work, emulated industrial
network data is transformed into a time series and analysed with three
different algorithms. The data contains labeled attacks, so the performance can
be evaluated. Matrix Profiles perform well with almost no parameterisation
needed. Seasonal Autoregressive Integrated Moving Average performs well in the
presence of noise, requiring parameterisation effort. Long Short Term
Memory-based neural networks perform mediocre while requiring a high training-
and parameterisation effort.Comment: Extended version of a publication in the 2018 IEEE International
Conference on Data Mining Workshops (ICDMW
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