A model and framework for online security benchmarking

The variety of threats and vulnerabilities within the online business environment are dynamic and thus constantly changing in how they impinge upon online functionality, compromise organizational or customer information, contravene security implementations and thereby undermine online customer confidence. To nullify such threats, online security management must become proactive, by reviewing and continuously improving online security to strengthen the enterpriseis online security measures and policies, as modelled. The benchmarking process utilises a proposed benchmarking framework to guide both the development and application of security benchmarks created in the first instance, from recognized information technology (IT) and information security standards (ISS) and then their application to the online security measures and policies utilized within online business. Furthermore, the benchmarking framework incorporates a continuous improvement review process to address the relevance of benchmark development over time and the changes in threat focus.<br /

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

“Ten strikes and you're out”: Increasing the number of login attempts can improve password usability

Many users today are struggling to manage an increasing number of passwords. As a consequence, many organizations face an increasing demand on an expensive resource – the system administrators or help desks. This paper suggests that re-considering the “3- strikes” policy commonly applied to password login systems would be an immediate way of reducing this demand. We analyzed 10 weeks worth of system logs from a sample of 386 users, whose login attempts were not restricted in the usual manner. During that period, only 10% of login attempts failed. We predict that requests for password reminders could be reduced by up to 44% by increasing the number of strikes from 3 to ten

Security Incident Response Criteria: A Practitioner's Perspective

Industrial reports indicate that security incidents continue to inflict large financial losses on organizations. Researchers and industrial analysts contend that there are fundamental problems with existing security incident response process solutions. This paper presents the Security Incident Response Criteria (SIRC) which can be applied to a variety of security incident response approaches. The criteria are derived from empirical data based on in-depth interviews conducted within a Global Fortune 500 organization and supporting literature. The research contribution of this paper is twofold. First, the criteria presented in this paper can be used to evaluate existing security incident response solutions and second, as a guide, to support future security incident response improvement initiatives

Integrating security and usability into the requirements and design process

According to Ross Anderson, 'Many systems fail because their designers protect the wrong things or protect the right things in the wrong way'. Surveys also show that security incidents in industry are rising, which highlights the difficulty of designing good security. Some recent approaches have targeted security from the technological perspective, others from the human–computer interaction angle, offering better User Interfaces (UIs) for improved usability of security mechanisms. However, usability issues also extend beyond the user interface and should be considered during system requirements and design. In this paper, we describe Appropriate and Effective Guidance for Information Security (AEGIS), a methodology for the development of secure and usable systems. AEGIS defines a development process and a UML meta-model of the definition and the reasoning over the system's assets. AEGIS has been applied to case studies in the area of Grid computing and we report on one of these

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset