8,039 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
Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review
Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs’ security practices in various scenarios including big data. The intention is to determine the gap between staffs’ security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs’ security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies
Threat modelling with UML for cybersecurity risk management in OT-IT integrated infrastructures
A strong cybersecurity threat management can provide a good security situation against malicious attacks designed to access, modify, delete, destroy or capture user or organization systems and sensitive data. In this work, first the issue of cybersecurity is described, then the common attacks of OT-IT integrated systems as target systems are examined. The concentration area of this thesis is about the security of OT-IT systems. The purpose of this thesis is to provide a Cybersecurity risk management solution fundamentally focused on detecting common cybersecurity intrusions which are widely being used by the malicious attacks to forcefully abuse or take advantage of preciously a computer network. The main idea of this project is to providing a solution which can help the cybersecurity experts of OT-IT companies to catch the abnormalities of the network practically by the time a pre-defined intrusion is being executed by an attacker, in order to give more defensive power against the possible threats. In chapter 3 There will be proposed model is designed with UML and SysML in Eclipse Papyrus software which is a great tool to model a system. Here, I presented a threat modeling detection system which is practically an IDS. Finally, the model will be implemented using the PCA methods and the SVM, which are part of machine learning techniques. The Intrusion Detection System is implemented and the results show the high efficiency of the proposed method
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 naĂŻve 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
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Anomaly detection for IoT networks using machine learning
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning.
This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets.
This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%.
This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
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