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High-performance computing (HPC) relies heavily on network security, particularly when supercomputing services are provided via public networks. As supercomputer operators, we introduced several security devices, such as anti-DDoS, intrusion prevention systems (IPSs), firewalls, and web application firewalls, to ensure the secure use of supercomputing resources. Potential threats are identified based on predefined security policies and added to the firewall rules for access control after detecting abnormal behavior through anti-DDoS, IPS, and system access logs. After analyzing the status change patterns for rule policies added owing to human errors among these added firewall log events, 289,320 data points were extracted over a period of four years. Security experts and operators must go through a strict verification process to rectify policies that were added incorrectly owing to human error, which adds to their workload. To address this challenge, our research applies various machine- and deep-learning algorithms to autonomously determine the normalcy of detection without requiring administrative intervention. Machine-learning algorithms, including naïve Bayes, K-nearest neighbor (KNN), OneR, a decision tree called J48, support vector machine (SVM), logistic regression, and the implemented neural network (NN) model with the cross-entropy loss function, were tested. The results indicate that the KNN and NN models exhibited an accuracy of 97%. Additional training and feature refinement led to even better improvements, increasing the accuracy to 98%, a 1% increase. By leveraging the capabilities of machine-learning and deep-learning technologies, we have provided the basis for a more robust, efficient, and autonomous network security infrastructure for supercomputing services. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) relies heavily on network security, particularly when supercomputing services are provided via public networks. As supercomputer operators, we introduced several security devices, such as anti-DDoS, intrusion prevention systems (IPSs), firewalls, and web application firewalls, to ensure the secure use of supercomputing resources. Potential threats are identified based on predefined security policies and added to the firewall rules for access control after detecting abnormal behavior through anti-DDoS, IPS, and system access logs. After analyzing the status change patterns for rule policies added owing to human errors among these added firewall log events, 289,320 data points were extracted over a period of four years. Security experts and operators must go through a strict verification process to rectify policies that were added incorrectly owing to human error, which adds to their workload. To address this challenge, our research applies various machine- and deep-learning algorithms to autonomously determine the normalcy of detection without requiring administrative intervention. Machine-learning algorithms, including naïve Bayes, K-nearest neighbor (KNN), OneR, a decision tree called J48, support vector machine (SVM), logistic regression, and the implemented neural network (NN) model with the cross-entropy loss function, were tested. The results indicate that the KNN and NN models exhibited an accuracy of 97%. Additional training and feature refinement led to even better improvements, increasing the accuracy to 98%, a 1% increase. By leveraging the capabilities of machine-learning and deep-learning technologies, we have provided the basis for a more robust, efficient, and autonomous network security infrastructure for supercomputing services.

Software-defined networking (SDN) is an evolving technology providing proper segregation between the control part and data-forwarding domain of network devices. The expansion of the Internet of Things (IoTs) and embedded mobile devices increases the volume of traffic at the network backbone and causes processing costs in the control plane. This directly affects the Ternary Content Addressable Memory (TCAM) of the switches because insufficient space makes it more challenging to manage the flow-entries. In this situation, providing services to specific users who newly authenticate after the successful handoff from the previous SDN domain is challenging. This paper proposes a method for implanting the users' primary domain's flow-rules in the serving SDN domain. As the TCAM is already suffering from a short space, it is hard to handle the flow-tables of multiple SDN domains in limited TCAM storage. The SDN-based Integration of the Interdomain Flow-rule in the SDN (IIF-SDN) scheme maximizes the proficiency of the switches by effectively storing flow-table and flow-entries. The effectiveness of the proposed scheme is benchmarked with proactive and reactive SDN approaches. [ABSTRACT FROM AUTHOR]

In this study we identify the differences in goal realisation when applying two conflicting paradigms regarding rule perception and management. We gathered more than 30 scenarios where goal conflicts were apparent in a military operational unit. We found that operators repetitively utilized certain routines in executing their tasks in an effort to realize several conflicting goals. These routines were not originally intended nor designed into the rules and not explicitly included in documentation. They were not necessarily at odds with the literal wording and/or the intent of rules and regulations, although we did find examples of this. Our data showed that local ingenuity was created innovatively within the frame of existing rules or kept invisible to those outside the unit. The routines were introduced and passed on informally, and we found no evidence of testing for the introduction of new risks, no migration into the knowledge base of the organisation, and no dissemination as new best practices. An explanation for this phenomenon was found in the fact that the military organisation was applying a top-down, classical, rational approach to rules. In contrast, the routines were generated by adopting a constructivist view of rules as dynamic, local, situated constructions with operators as experts. The results of this study suggest that organisations are more effective in solving goal conflicts and creating transparency on local ingenuity if they adopt a constructivist paradigm instead of, or together with, a classical paradigm. [ABSTRACT FROM AUTHOR]

In this study we identify the differences in goal realisation when applying two conflicting paradigms regarding rule perception and management. We gathered more than 30 scenarios where goal conflicts were apparent in a military operational unit. We found that operators repetitively utilized certain routines in executing their tasks in an effort to realize several conflicting goals. These routines were not originally intended nor designed into the rules and not explicitly included in documentation. They were not necessarily at odds with the literal wording and/or the intent of rules and regulations, although we did find examples of this. Our data showed that local ingenuity was created innovatively within the frame of existing rules or kept invisible to those outside the unit. The routines were introduced and passed on informally, and we found no evidence of testing for the introduction of new risks, no migration into the knowledge base of the organisation, and no dissemination as new best practices. An explanation for this phenomenon was found in the fact that the military organisation was applying a top-down, classical, rational approach to rules. In contrast, the routines were generated by adopting a constructivist view of rules as dynamic, local, situated constructions with operators as experts. The results of this study suggest that organisations are more effective in solving goal conflicts and creating transparency on local ingenuity if they adopt a constructivist paradigm instead of, or together with, a classical paradigm.

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

Event data analysis is becoming increasingly of interest to academic researchers looking for patterns in the data. Unlike domain experts working in large companies who have access to IT staff and expensive software infrastructures, researchers find it harder to efficiently manage their event data analysis by themselves. Particularly, user-driven rule management is a challenge especially when analysis rules increase in size and complexity over time. In this paper, we propose an event data analysis platform called EP-RDR intended for non-IT experts that facilitates the evolution of event processing rules according to changing requirements. This platform integrates a rule learning framework called Ripple-Down Rules (RDR) operating in conjunction with an event pattern detection component invoked as a service (EPDaaS). We have built a prototype to demonstrate this solution on real-life scenario involving financial data analysis. [ABSTRACT FROM AUTHOR]

There is a strong political consensus in a number of countries that occupational safety and health regulation is stifling industrial innovation and development and is feeding a culture of damaging risk aversion and petty bureaucracy. In a number of countries this has led to proposals to repeal regulations and reduce the regulatory burden. The authors were commissioned to prepare a discussion paper on this issue by the Mercatus Center of George Mason University in Arlington, Virginia, aimed particularly at an American audience. This paper is based on that report (Hale et al., 2011). The paper is based on previous work of the first two authors, developing a framework of occupational safety rule management at the workplace level (Hale and Borys, 2012a,b). Based on a literature study, this paper analyses the similarities and differences between rules at the workplace level and the development, use and enforcement of regulations at the national level to influence and control organisational behavior. It traces the forces encouraging the growth of regulatory detail and hence the bureaucratic burden of compliance and the options open to reduce that burden without loosening control so much that the level of safety declines. The analysis uses the hierarchy of rules from goals, through process (risk management) rules to detailed action rules as framework for predicting the level of ownership and responsibility felt by the regulated. [ABSTRACT FROM AUTHOR]

Abstract: Rules are increasingly becoming an important form of knowledge representation on the Semantic Web. There are currently few methods that can ensure that the acquisition and management of rules can scale to the size of the Web. We previously developed methods to help manage large rule bases using syntactical analyses of rules. This approach did not incorporate semantics. As a result, rule categorization based on syntactic features may not be effective. In this paper, we present a novel approach for grouping rules based on whether the rule elements share relationships within a domain ontology. We have developed our method for rules specified in the Semantic Web Rule Language (SWRL), which is based on the Web Ontology Language (OWL) and shares its formal underpinnings. Our method uses vector space modeling of rule atoms and an ontology-based semantic similarity measure. We apply a clustering method to detect rule relatedness, and we use a statistical model selection method to find the optimal number of clusters within a rule base. Using three different SWRL rule bases, we evaluated the results of our semantic clustering method against those of our syntactic approach. We have found that our new approach creates clusters that better match the rule bases’ logical structures. Semantic clustering of rule bases may help users to more rapidly comprehend, acquire, and manage the growing numbers of rules on the Semantic Web. [Copyright &y& Elsevier]

Abstract: Part 1, the companion paper to this paper (Hale and Borys, this issue) reviews the literature from 1986 on the management of those safety rules and procedures which relate to the workplace level in organisations. It contrasts two different paradigms of how work rules and their development and use are perceived and managed. The first is a top-down classical, rational approach in which rules are seen as static, comprehensive limits of freedom of choice, imposed on operators at the sharp end and violations are seen as negative behaviour to be suppressed. The second is a bottom-up constructivist view of rules as dynamic, local, situated constructions of operators as experts, where competence is seen to a great extent as the ability to adapt rules to the diversity of reality. That paper explores the research underlying and illustrating these two paradigms. In this second paper we draw on that literature study to propose a framework of rule management which attempts to draw the lessons from both paradigms. It places the monitoring and adaptation of rules central to its management process and emphasises the need for participation of the intended rule followers in the processes of rule-making, but more importantly in keeping those rules alive and up to date in a process of regular and explicit dialogue with first-line supervision, and through them with the technical, safety and legal experts on the system functioning. The framework is proposed for testing in the field as a benchmark for good practice. [Copyright &y& Elsevier]

The complexity of large computer systems has raised unprecedented challenges for system management. In practice, operators often collect large volume of monitoring data from system components and set up many rules to check data and trigger alerts. However, the alerts from various rules usually have different problem reporting accuracy because their thresholds are often manually set based on operators' experience and intuition. Meantime, due to system dependencies, a single problem may trigger many alerts at the same time in large systems and the critical question is which alert should be analyzed first in the following problem determination process. In this paper, we propose a novel peer review mechanism to rank the importance of alerts and the top ranked alerts are more likely to be true positives. After comparing a metric value against its threshold to generate alerts, we also compare the value with the equivalent thresholds from many other rules to determine the importance of alerts. Our approach is evaluated with a real test bed system and experimental results are also included to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Advanced applications in fields such as CAD, software engineering, real-time process control, corporate repositories and digital libraries require the construction, efficient access and management of large, shared knowledge bases. Such knowledge bases cannot be built using existing tools such as expert system shells, because these do not scale up, nor can they be built in terms of existing database technology, because such technology does not support the rich representational structure and inference mechanisms required for knowledge-based systems. This paper proposes a generic architecture for a knowledge base management system intended for such applications. The architecture assumes an object-oriented knowledge representation language with an assertional sublanguage used to express constraints and rules. It also provides for general-purpose deductive inference and special-purpose temporal reasoning. Results reported in the paper address several knowledge base management issues. For storage management, a new method is proposed for generating a logical schema for a given knowledge base. Query processing algorithms are offered for semantic and physical query optimization, along with an enhanced cost model for query cost estimation. On concurrency control, the paper describes a novel concurrency control policy which takes advantage of knowledge base structure and is shown to outperform two-phase locking for highly structured knowledge bases and update-intensive transactions. Finally, algorithms for compilation and efficient processing of constraints and rules during knowledge base operations are described. The paper describes original results, including novel data structures and algorithms, as well as preliminary performance evaluation data. Based on these results, we conclude that knowledge base management systems which can accommodate large knowledge bases are feasible. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches' flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning. [ABSTRACT FROM AUTHOR]