The HSS/SNiC : a conceptual framework for collapsing security down to the physical layer

This work details the concept of a novel network security model called the Super NIC (SNIC) and a Hybrid Super Switch (HSS). The design will ultimately incorporate deep packet inspection (DPI), intrusion detection and prevention (IDS/IPS) functions, as well as network access control technologies therefore making all end-point network devices inherently secure. The SNIC and HSS functions are modelled using a transparent GNU/Linux Bridge with the Netfilter framework

Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field

An extended Takagi–Sugeno–Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi-Sugeno-Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: 1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base, and 2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases

Experience-based rule base generation and adaptation for fuzzy interpolation

Fuzzy modelling has been widely and successfully applied to control problems. Traditional fuzzy modelling requires either complete experts’ knowledge or large data sets to generate rule bases such that the input spaces can be fully covered. Although fuzzy rule interpolation (FRI) relaxes this requirement by approximating rules using their neighbouring ones, it is still difficult for some real world applications to obtain sufficient experts’ knowledge and/or data to generate a reasonable sparse rule base to support FRI. Also, the generated rule bases are usually fixed and therefore cannot support dynamic situations. In order to address these limitations, this paper presents a novel rule base generation and adaptation system to allow the creation of rule bases with minimal a priori knowledge. This is implemented by adding accurate interpolated rules into the rule base guided by a performance index from the feedback mechanism, also considering the rule’s previous experience information as a weight factor in the process of rule selection for FRI. In particular, the selection of rules for interpolation in this work is based on a combined metric of the weight factors and the distances between the rules and a given observation, rather than being simply based on the distances. Two digitally simulated scenarios are employed to demonstrate the working of the proposed system, with promising results generated for both rule base generation and adaptation

Pedestrian detection and tracking

This report presents work on the detection and tracking of people in digital images. The employed detection technique is based on image processing and classification techniques. The work uses an object detection process to detect object candidate locations and a classification method using a Self-Organising Map neural network to identify the pedestrian head positions in an image. The proposed tracking technique with the support of a novel prediction method is based on the association of Cellular Automata (CA) and a Backpropagation Neural Network (BPNN). The tracking employs the CA to capture the pedestrian's movement behaviour, which in turn is learned by the BPNN in order to the estimated location of the pedestrians movement without the need to use empirical data. The report outlines this method and describes how it detects and identifies the pedestrian head locations within an image. Details of how the proposed prediction technique is applied to support the tracking process are then provided. Assessments of each component of the system and on the system as a whole have been carried out. The results obtained have shown that the novel prediction technique described is able to provide an accurate forecast of the movement of a pedestrian through a video image sequence.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Intelligent Home Heating Controller Using Fuzzy Rule Interpolation

The reduction of domestic energy waste helps in achieving the legal binding target in the UK that CO2 emissions needs to be reduced by at least 34% below base year (1990) levels by 2020. Space heating consumes about 60% of the household energy consumption, and it has been reported by the Household Electricity Survey from GOV.UK, that 23% of residents leave the heating on while going out. To minimise the waste of heating unoccupied homes, a number of sensor-based and programmable controllers for central heating system have been developed, which can successfully switch off the home heating systems when a property is unoccupied. However, these systems cannot automatically preheat the homes before occupants return without manual inputs or leaving the heating on unnecessarily for longer time, which has limited the wide application of such devices. In order to address this limitation, this paper proposes a smart home heating controller, which enables a home heating system to efficiently preheat the home by successfully predicting the users’ home time. In particular, residents’ home time is calculated by employing fuzzy rule interpolation, supported by users’ historic and current location data from portable devices (commonly smart mobile phones). The proposed system has been applied to a real-world case with promising results shown

Reviews

Review of Private Pension Policies in Industrialized Countries: A Comparative Analysis, Reframing Human Resource Management: Power, Ethics and the Subject of Work, Skill Formation in Japan: An Overview and Enterprise Case Studies, Psychological Contracts in Organizations: Understanding Written and Uttwritten Agreements, The Responsive Employee: the road toward organisational citizenship in the workplace and Industrial Relations: Theory and Practice In Britai

Intrusion Detection System by Fuzzy Interpolation

Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated

Towards Efficient, Scalable and Coordinated On-the-move EV Charging Management

Unlike traditional Internal Combustion Engine Vehicles ICEVs), the introduction of Electric Vehicles (EVs) is a significant step towards green environment. Public Charging Stations (CSs) are essential for providing charging services for on-the-move EVs (e.g., EVs moving on the road during their journeys). Key technologies herein involve intelligent selection of CSs to coordinate EV drivers’ charging plans, and provisioning of cost-efficient and scalable communication infrastructure for information exchange between power grid and EVs. In this article, we propose an efficient and scalable Publish/Subscribe (P/S) communication framework, in line with a coordinated onthe-move EV charging management scheme. The case study under the Helsinki city scenario shows the advantage of proposed CS-selection scheme, in terms of reduced charging waiting time and increased number of charged EVs, as charging performance metrics at EV and CS sides. Besides, the proposed P/S communication framework shows its low communication cost (in terms of signallings involved for charging management), meanwhile with great scalability for supporting increasing EVs’ charging demands

Automatic generation of alignments for 3D QSAR analyses

Many 3D QSAR methods require the alignment of the molecules in a dataset, which can require a fair amount of manual effort in deciding upon a rational basis for the superposition. This paper describes the use of FBSS, a pro-ram for field-based similarity searching in chemical databases, for generating such alignments automatically. The CoMFA and CoMSIA experiments with several literature datasets show that the QSAR models resulting from the FBSS alignments are broadly comparable in predictive performance with the models resulting from manual alignments