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The purpose of this paper is to build a new hierarchical intrusion detection system (IDS) based on a binary tree of different types of classifiers. The proposed IDS model must possess the following characteristics: combine a high detection rate and a low false alarm rate, and classify any connection in a specific category of network connection.

To build the binary tree, the authors cluster the different categories of network connections hierarchically based on the proportion of false-positives and false-negatives generated between each of the two categories. The built model is a binary tree with multi-levels. At first, the authors use the best classifier in the classification of the network connections in category A and category G2 that clusters the rest of the categories. Then, in the second level, they use the best classifier in the classification of G2 network connections in category B and category G3 that represents the different categories clustered in G2 without category B. This process is repeated until the last two categories of network connections. Note that one of these categories represents the normal connection, and the rest represent the different types of abnormal connections.

The experimentation on the labeled data set for flow-based intrusion detection, NSL-KDD and KDD’99 shows the high performance of the authors' model compared to the results obtained by some well-known classifiers and recent IDS models. The experiments’ results show that the authors' model gives a low false alarm rate and the highest detection rate. Moreover, the model is more accurate than some well-known classifiers like SVM, C4.5 decision tree, MLP neural network and naïve Bayes with accuracy equal to 83.26 per cent on NSL-KDD and equal to 99.92 per cent on the labeled data set for flow-based intrusion detection. As well, it is more accurate than the best of related works and recent IDS models with accuracy equal to 95.72 per cent on KDD’99.

This paper proposes a novel hierarchical IDS based on a binary tree of classifiers, where different types of classifiers are used to create a high-performance model. Therefore, it confirms the capacity of the hierarchical model to combine a high detection rate and a low false alarm rate.

This study investigates the level of variance in the real time demand for bagged cement, induced in response to the climatic sequence of the humid tropics, to support best practice calls for a weather-responsive supply chain strategy.

Data on the consumption of cement and site works for 100 ongoing building construction sites were gathered for a period of 12 months. The variance partitioning capabilities of the Ordinary Least Squares and Hierarchical Linear Modelling forms of regression analysis are comparatively used to evaluate the sensitivity of cement demand to the meteorological profile of wet-humid climate

The study outcome provides statistical evidence demonstrating that the meteorological profile of wet-humid climate induces a significantly high percentage of the variance in the real-time demand for bagged cement on construction sites. However, nested within this variance, are the fixed effects of the cement footprint of the building architecture inherent in the locality. Particularly, positive changes to reduce the wet trade composition of buildings or compensating changes in technological bias, are necessary to combat weather interference in the humid tropics.

The findings are exploratory, and not for the purposes of holistically forecasting cement demand, and can therefore only form part of a more comprehensive decision support system, bespoke to the study area.

The study outcome provides a back-end view to climatic adaptation in wet humid settings, making a compelling case for localized climate-risk adaptive supply chain strategies and policies geared towards sustainability in cement usage.

The study delineates the confounding impact of weather, distinct from local building architecture and technological bias, thus creating a methodological platform for replication and comparative productivity studies in diverse geographical areas.

The purpose of this paper is twofold. In view of the importance of process platform‐based production configuration (PPbPC) in sustaining product family production efficiency, it is to study the underlying logic for configuring production processes for a product family based on a process platform. Second, it is to apply the Petri nets (PNs) techniques to model PPbPC, in attempting to shed light on the underlying logic.

The authors first identify the fundamental issues in PPbPC, including variety handling, process variation accommodation, configuration at different abstraction levels, and constraint satisfaction. To accommodate the corresponding modelling difficulties, the authors develop a formalism of hierarchical colored timed PNs (HCTPNs) based on the principles of hierarchical PNs, timed PNs, and colored PNs. In the formalism, three types of nets together with a system of HCTPNs are defined to address the modelling of PPbPC.

Applying HCTPNs to vibration motors' case has revealed the logic of specifying complete production processes of final products at different levels of abstraction to achieve production configuration. The preliminary results also further demonstrate the feasibility of modelling PPbPC based on HCTPNs.

Traditional approaches to planning production processes for individual products may limit production performance improvement when companies need to timely produce a high variety of customized products. Systematic methods should be developed to plan production processes for product families so as to achieve production efficiency while utilizing the existing manufacturing resources.

By integrating the advantages of existing PN techniques, the HCTPNs formalism is developed to shed light on planning production processes for product families. The resulting production configuration model can facilitate practitioners to achieve production efficiency in producing large numbers of customized products.

Restructuring a business process (BP) model may enhance the BP performance and improve its understandability. So-far proposed restructuring methods use either refactoring which focuses on structural aspects, social network discovery which uses semantic information to guide the affiliation process during its analysis, or social network rediscovery which uses structural information to identify clusters of actors according to their relationships. The purpose of this paper is to propose a hybrid method that exploits both the semantic and structural aspects of a BP model.

The proposed method first generates a social network from the BP model. Second, it applies hierarchical clustering to determine the performers’ partitions; this step uses the social context which specifies features related to performers, and two new distances that account for semantic and structural information. Finally, it applies a set of behavioral and organizational restructuring rules adapted from the graph optimization domain; each rule uses the identified performers’ partitions and the business context to reduce particular quality metrics.

The efficiency of the proposed method is illustrated through well-established complexity metrics. The illustration is made through the development of a tool that fully supports the proposed method and proposes a strategy for the application of the restructuring rules.

The proposed method has the merit of combining the semantic and structural aspects of a Business Process Modeling Notation model to identify restructuring operations whose ordered application reduces the complexity of the initial model.

Wireless multi-hop ad hoc networks are becoming very attractive and widely deployed in many kinds of communication and networking applications. However, distributed and collaborative routing in such networks makes them vulnerable to various security attacks. This paper aims to design and implement a new efficient intrusion detection and prevention framework, called EIDPF, a host-based framework suitable for mobile ad hoc network’s characteristics such as high node’s mobility, resource-constraints and rapid topology change. EIDPF aims to protect an AODV-based network against routing attacks that could target such network.

This detection and prevention framework is composed of three complementary modules: a specification-based intrusion detection system to detect attacks violating the protocol specification, a load balancer to prevent fast-forwarding attacks such as wormhole and rushing and adaptive response mechanism to isolate malicious node from the network.

A key advantage of the proposed framework is its capacity to efficiently avoid fast-forwarding attacks and its real-time detection of both known and unknown attacks violating specification. The simulation results show that EIDPF exhibits a high detection rate, low false positive rate and no extra communication overhead compared to other protection mechanisms.

It is a new intrusion detection and prevention framework to protect ad hoc network against routing attacks. A key strength of the proposed framework is its ability to guarantee a real-time detection of known and unknown attacks that violate the protocol specification, and avoiding wormhole and rushing attacks by providing a load balancing route discovery.

This paper introduces a solution for employing intrusion detection technology across organisational boundaries by using knowledge grid technology.

Employment of intrusion detection technology is currently limited to inside organisation deployments. By setting up communities, which maintain trust relationships between network nodes anywhere in the internet, security event data, structured into a common XML‐based format, can be exchanged in a secure and reliable manner.

A modular architecture has been developed which provides functionality to integrate different audit data generating applications and share knowledge about incidents, vulnerabilities and countermeasures from all over the internet. A security policy, based on the Chinese Wall Security Policy, ensures the protection of information inserted into the network.

The solution is currently in a preliminary stage, providing the description of the design only. Implementation as well as evaluation is under development.

Trusting communities everywhere in the internet will be brought into being so that people may establish trust relationships between each other. Participants may decide themselves whom they trust as a source for security‐related information rather than depending on centralised approaches.

No approach is known combining the two technologies – intrusion detection and grid – as described in this paper. The decentralised, peer‐to‐peer based grid approach together with the introduction of trust relationships and communities results in a new way of thinking about distributing security audit data.

The purpose of this study is to develop a building information modelling (BIM)-based mixed reality (MR) application to enhance and facilitate the process of managing bridge inspection and maintenance works remotely from office. It aims to address the ineffective decision-making process on maintenance tasks from the conventional method which relies on documents and 2D drawings on visual inspection. This study targets two key issues: creating a BIM-based model for bridge inspection and maintenance; and developing this model in a MR platform based on Microsoft Hololens.

Literature review is conducted to determine the limitation of MR technology in the construction industry and identify the gaps of integration of BIM and MR for bridge inspection works. A new framework for a greater adoption of integrated BIM and Hololens is proposed. It consists of a bridge information model for inspection and a newly-developed Hololens application named “HoloBridge”. This application contains the functional modules that allow users to check and update the progress of inspection and maintenance. The application has been implemented for an existing bridge in South Korea as the case study.

The results from pilot implementation show that the inspection information management can be enhanced because the inspection database can be systematically captured, stored and managed through BIM-based models. The inspection information in MR environment has been improved in interpretation, visualization and visual interpretation of 3D models because of intuitively interactive in real-time simulation.

The proposed framework through “HoloBridge” application explores the potential of integrating BIM and MR technology by using Hololens. It provides new possibilities for remote inspection of bridge conditions.

The aim of this research is to secure the sensitive outsourced data with minimum encryption within the cloud provider. Unfaithful solutions for providing privacy and security along with performance issues by encryption usage of outsourced data are the main motivation points of this research.

This paper presents a method for secure and confidential storage of data in the cloud environment based on fragmentation. The method supports minimal encryption to minimize the computations overhead due to encryption. The proposed method uses normalization of relational databases, tables are categorized based on user requirements relating to performance, availability and serviceability, and exported to XML as fragments. After defining the fragments and assigning the appropriate confidentiality levels, the lowest number of Cloud Service Providers (CSPs) is used required to store all fragments that must remain unlinkable in separate locations.

Particularly in the cloud databases are sometimes de‐normalised (their normal form is decreased to lower level) to increase the performance.

The paper proposes a methodology to minimize the need for encryption and instead focus on making data entities unlinkable so that even in the case of a security breach for one set of data, the privacy impact on the whole is limited. The paper would be relevant to those people whose main concern is to preserve data privacy in distributed systems.

In the field of cryptography, authentication, secrecy and identification can be accomplished by use of secret keys for any computer-based system. The need to acquire certificates endorsed through CA to substantiate users for the barter of encoded communications is one of the most significant constraints for the extensive recognition of PKC, as the technique takes too much time and susceptible to error. PKC’s certificate and key management operating costs are reduced with IBC. IBE is a crucial primeval in IBC. The thought behind presenting the IBE scheme was to diminish the complexity of certificate and key management, but it also gives rise to key escrow and key revocation problem, which provides access to unauthorised users for the encrypted information.

This paper aims to compare the result of IIBES with the existing system and to provide security analysis for the same and the proposed system can be used for the security in federated learning.

Furthermore, it can be implemented using other encryption/decryption algorithms like elliptic curve cryptography (ECC) to compare the execution efficiency. The proposed system can be used for the security in federated learning.

As a result, a novel enhanced IBE scheme: IIBES is suggested and implemented in JAVA programming language using RSA algorithm, which eradicates the key escrow problem through eliminating the need for a KGC and key revocation problem by sing sub-KGC (SKGC) and a shared secret with nonce. IIBES also provides authentication through IBS as well as it can be used for securing the data in federated learning.

For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks.

We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model.

We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries.

Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results.

In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.