Dendritic Cell Algorithm with Optimised Parameters using Genetic Algorithm

Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, Dendritic Cell Algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated

Fault Detection and Isolation of Wind Turbines using Immune System Inspired Algorithms

Recently, the research focus on renewable sources of energy has been growing intensively. This is mainly due to potential depletion of fossil fuels and its associated environmental concerns, such as pollution and greenhouse gas emissions. Wind energy is one of the fastest growing sources of renewable energy, and policy makers in both developing and developed countries have built their vision on future energy supply based on and by emphasizing the wind power. The increase in the number of wind turbines, as well as their size, have led to undeniable care and attention to health and condition monitoring as well as fault diagnosis of wind turbine systems and their components. In this thesis, two main immune inspired algorithms are used to perform Fault Detection and Isolation (FDI) of a Wind Turbine (WT), namely the Negative Selection Algorithm (NSA) as well as the Dendritic Cell Algorithm (DCA). First, an NSA-based fault diagnosis methodology is proposed in which a hierarchical bank of NSAs is used to detect and isolate both individual as well as simultaneously occurring faults common to the wind turbines. A smoothing moving window filter is then utilized to further improve the reliability and performance of the proposed FDI scheme. Moreover, the performance of the proposed scheme is compared with the state-of-the-art data-driven technique, namely Support Vector Machine (SVM) to demonstrate and illustrate the superiority and advantages of the proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate the proposed methodology with that of the SVM under various fault severities. In the second part, another immune inspired methodology, namely the Dendritic Cell Algorithm (DCA) is used to perform online sensor fault FDI. A noise filter is also designed to attenuate the measurement noise, resulting in better FDI results. The proposed DCA-based FDI scheme is then compared with the previously developed NSA-based FDI scheme, and a nonparametric statistical comparison test is also performed. Both of the proposed immune inspired frameworks are applied to a well-known wind turbine benchmark model in order to validate the effectiveness of the proposed methodologies

Anomaly Detection for Internet of Things (IoT) Using an Artificial Immune System

Internet of Things (IoT) have demonstrated significant impact on all aspects of human daily lives due to their pervasive applications in areas such as telehealth, home appliances, surveillance, and wearable devices. The number of IoT devices and sensors connected to the Internet across the world is expected to reach over 50 billion by the end of 2020. The connection of such rapidly increasing number of IoT devices to the Internet leads to concerns in cyber-attacks such as malware, worms, denial of service attack (DoS) and distributed DoS attack (DDoS). To prevent these attacks from compromising the performance of IoT devices, various approaches for detecting and mitigating cyber security threats have been developed. This paper reports an IoT attack and anomaly detection approach by using the dendritic cell algorithm (DCA). In particular, DCA is an artificial immune system (AIS), which is developed from the inspiration of the working principles and characteristic behaviours of the human immune system (HIS), specifically for the purpose of detecting anomalies in networked systems. The performance of the DCA on detecting IoT attacks is evaluated using publicly available IoT datasets, including DoS, DDoS, Reconnaissance, Keylogging, and Data exfiltration. The experimental results show that, the DCA achieved a comparable detection performance to some of the commonly used classifiers, such as decision trees, random forests, support vector machines, artificial neural network and naïve Bayes, but with reasonably high computational efficiency

A decentralised secure and privacy-preserving e-government system

Electronic Government (e-Government) digitises and innovates public services to businesses, citizens, agencies, employees and other shareholders by utilising Information and Communication Technologies. E-government systems inevitably involves finance, personal, security and other sensitive information, and therefore become the target of cyber attacks through various means, such as malware, spyware, virus, denial of service attacks (DoS), and distributed DoS (DDoS). Despite the protection measures, such as authentication, authorisation, encryption, and firewalls, existing e-Government systems such as websites and electronic identity management systems (eIDs) often face potential privacy issues, security vulnerabilities and suffer from single point of failure due to centralised services. This is getting more challenging along with the dramatically increasing users and usage of e-Government systems due to the proliferation of technologies such as smart cities, internet of things (IoTs), cloud computing and interconnected networks. Thus, there is a need of developing a decentralised secure e-Government system equipped with anomaly detection to enforce system reliability, security and privacy. This PhD work develops a decentralised secure and privacy-preserving e-Government system by innovatively using blockchain technology. Blockchain technology enables the implementation of highly secure and privacy preserving decentralised applications where information is not under the control of any centralised third party. The developed secure and decentralised e-Government system is based on the consortium type of blockchain technology, which is a semi-public and decentralised blockchain system consisting of a group of pre-selected entities or organisations in charge of consensus and decisions making for the benefit of the whole network of peers. Ethereum blockchain solution was used in this project to simulate and validate the proposed system since it is open source and supports off-chain data storage such as images, PDFs, DOCs, contracts, and other files that are too large to be stored in the blockchain or that are required to be deleted or changed in the future, which are essential part of e-Government systems. This PhD work also develops an intrusion detection system (IDS) based on the Dendritic cell algorithm (DCA) for detecting unwanted internal and external traffics to support the proposed blockchain-based e-Government system, because the blockchain database is append-only and immutable. The IDS effectively prevent unwanted transactions such as virus, malware or spyware from being added to the blockchain-based e-Government network. Briefly, the DCA is a class of artificial immune systems (AIS) which was introduce for anomaly detection in computer networks and has beneficial properties such as self-organisation, scalability, decentralised control and adaptability. Three significant improvements have been implemented for DCA-based IDS. Firstly, a new parameters optimisation approach for the DCA is implemented by using the Genetic algorithm (GA). Secondly, fuzzy inference systems approach is developed to solve nonlinear relationship that exist between features during the pre processing stage of the DCA so as to further enhance its anomaly detection performance in e-Government systems. In addition, a multiclass DCA capable of detection multiple attacks is developed in this project, given that the original DCA is a binary classifier and many practical classification problems including computer network intrusion detection datasets are often associated with multiple classes. The effectiveness of the proposed approaches in enforcing security and privacy in e- Government systems are demonstrated through three real-world applications: privacy and integrity protection of information in e Government systems, internal threats detection, and external threats detection. Privacy and integrity protection of information in the proposed e- Government systems is provided by using encryption and validation mechanism offered by the blockchain technology. Experiments demonstrated the performance of the proposed system, and thus its suitability in enhancing security and privacy of information in e-Government systems. The applicability and performance of the DCA-based IDS in e Government systems were examined by using publicly accessible insider and external threat datasets with real world attacks. The results show that, the proposed system can mitigate insider and external threats in e-Government systems whilst simultaneously preserving information security and privacy. The proposed system also could potentially increase the trust and accountability of public sectors due to the transparency and efficiency which are offered by the blockchain applications

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

No abstract availabl

新たな進化的及びニューロン計算による分類問題に関する研究

富山大学・富理工博甲第172号・銭孝孝・2020/3/24富山大学202

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces