Trustworthy and sustainable smart city services at the edge

Palm oil is a renewable resource that has the potential to replace fossil fuel and petrochemical for a better sustainable system. However, there is room for improvement in the current operation of the palm oil industry to achieve better sustainability development. The industrial revolution toward automation and artificial intelligence (AI) is the new trend known as the fourth industrial revolution (Industry 4.0). Unfortunately, the palm oil industry has been moving slowly in this revolution. This paper aims to conduct a detailed review of the current state of the palm oil industry development toward Industry 4.0. A novel Hazard and Operability Analysis (HAZOP) approach is adopted i) to ensure a detailed evaluation of the existing problems, and ii) to identify potential implementation of Industry 4.0 technologies in the palm oil industry. HAZOP is a common approach used in chemical engineering to systematically evaluate process safety and identify the possible improvement of the existing system. The same concept is applied in this paper to investigate the possible adaptation of Industry 4.0 technologies to improve the palm oil industry. Existing Industry 4.0 technologies and features were evaluated to identify feasible adaptation in the industry. The HAZOP review proposed 23 recommendations to improve the palm oil industry with Industry 4.0 technologies to achieve a higher standard in sustainable production. A total of 13 specific Industry 4.0 features were identified as the potential development gaps for palm oil industry stakeholders, which included the adaptation of Internet-of-Things sensors, cloud computing, blockchain, and smart imaging processing technologies.

A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach

A cloud-edge based data security architecture for sharing and analysing cyber threat information

The cyber security landscape is fundamentally changing over the past years While technology is evolving and new sophisticated applications are being developed, a new threat scenario is emerging in alarming proportions Sophisticated threats with multi-vectored, multi-staged and polymorphic characteristics are performing complex attacks, making the processes of detection and mitigation far more complicated Thus, organizations were encouraged to change their traditional defense models and to use and to develop new systems with a proactive approach Such changes are necessary because the old approaches are not effective anymore to detect advanced attacks Also, the organizations are encouraged to develop the ability to respond to incidents in real-time using complex threat intelligence platforms However, since the field is growing rapidly, today Cyber Threat Intelligence concept lacks a consistent definition and a heterogeneous market has emerged, including diverse systems and tools, with different capabilities and goals This work aims to provide a comprehensive evaluation methodology of threat intelligence standards and cyber threat intelligence platforms The proposed methodology is based on the selection of the most relevant candidates to establish the evaluation criteria In addition, this work studies the Cyber Threat Intelligence ecosystem and Threat Intelligence standards and platforms existing in state-of-the-art

A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence

Enabling efficient and secure energy cloud using edge computing and 5G

Large enterprises and organizations from both private and public sectors typically outsource a platform solution, as part of the Managed Security Services (MSSs), from 3rd party providers (MSSPs) to monitor and analyze their data containing cyber security information. Sharing such data among these large entities is believed to improve their effectiveness and efficiency at tackling cybercrimes, via improved analytics and insights. However, MSS platform customers currently are not able or not willing to share data among themselves because of multiple reasons, including privacy and confidentiality concerns, even when they are using the same MSS platform. Therefore any proposed mechanism or technique to address such a challenge need to ensure that sharing is achieved in a secure and controlled way. In this paper, we propose a new architecture and use case driven designs to enable confidential, flexible and collaborative data sharing among such organizations using the same MSS platform. MSS platform is a complex environment where different stakeholders, including authorized MSSP personnel and customers' own users, have access to the same platform but with different types of rights and tasks. Hence we make every effort to improve the usability of the platform supporting sharing while keeping the existing rights and tasks intact. As an innovative and pioneering attempt to address the challenge of data sharing in the MSS platform, we hope to encourage further work to follow so that confidential and collaborative sharing eventually happens among MSS platform customers.

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Enabling Cyber Security Data Sharing for Large-scale Enterprises Using Managed Security Services

Network threats often come from multiple sources and affect a variety of domains. Collaborative sharing and analysis of Cyber Threat Information (CTI) can greatly improve the prediction and prevention of cyber-attacks. However, CTI data containing sensitive and confidential information can cause privacy exposure and disclose security risks, which will deter organisations from sharing their CTI data. To address these concerns, the consortium of the EU H2020 project entitled Collaborative and Confidential Information Sharing and Analysis for Cyber Protection (C3ISP) has designed and implemented a framework (i.e. C3ISP Framework) as a service for cyber threat management. This paper focuses on the design and development of an API Gateway, which provides a bridge between end-users and their data sources, and the C3ISP Framework. It facilitates end-users to retrieve their CTI data, regulate data sharing agreements in order to sanitise the data, share the data with privacy-preserving means, and invoke collaborative analysis for attack prediction and prevention. In this paper, we report on the implementation of the API Gateway and experiments performed. The results of these experiments show the efficiency of our gateway design, and the benefits for the end-users who use it to access the C3ISP Framework.

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Enabling Privacy-Preserving Sharing of Cyber Threat Information in the Cloud

The detection and prevention of cyber-attacks is one of the main challenges in Vehicle-to-Everything (V2X) autonomous platooning scenarios. A key tool in this activity is the measurement report that is generated by User Equipment (UE), containing received signal strength and location information. Such data is effective in techniques to detect Rogue Base Stations (RBS) or Subscription Permanent Identifier SUPI/5G-GUTI catchers. An undetected RBS could result in unwanted consequences such as Denial of Service (DoS) attacks and subscriber privacy attacks on the network and UE. Motivated by this, this paper presents the novel simulation of a 5G cellular system to generate a realistic dataset of signal strength measurements that can later be used in the development of techniques to identify and prevent RBS interventions. The results show that the tool can create a large dataset of realistic measurement reports which can be used to develop and validate RBS detection techniques.

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Ian Herwono

Papers

A cloud-edge based data security architecture for sharing and analysing cyber threat information

Enabling Cyber Security Data Sharing for Large-scale Enterprises Using Managed Security Services

Enabling Privacy-Preserving Sharing of Cyber Threat Information in the Cloud

Generation of realistic signal strength measurements for a 5G Rogue Base Station attack scenario

Multi-Stage Attack Modelling: your new weapon against sophisticated cyber attacks