Mission-critical Infrastructure Research Articles

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

Ambient intelligence approach: Internet of Things based decision performance analysis for intrusion detection

In recent infrastructures, Internet of Things (IoT) have become an important technology for connecting various actuators and sensors over wireless networks. Due to increase in mission-critical infrastructures, we make use of these new technologies for reliable communication but their security is always not promising in terms of availability, confidentiality, integrity, and privacy of network services. Users can be compromised and vulnerable by a motivated malicious opponent unless they are not adequately protected by a robust defense. Due to this reason, an ambient intelligence approach for Intrusion Detection System (IDS) is required. In this research, ww proposed Ambient Approach based on Reinforcement Learning Integrated Deep Q-Neural Network (RL-DQN) model for WSNs and IoT in which it leverages the Markov decision process (MDP) formalism to enhance the decision performance in IDS. We deploy RL-DQN-IDS over Edge-cloud intrusion detection infrastructure in which binary attack classification of the network traffic is performed at the edge network while multi-attack classification is performed at the cloud network. To identify intrusions, we use a two-phase process that includes an initial learning phase that relies on RL, followed by a detection and classification phase that relies on DQN. We used four datasets namely UNSW-NB-15, BoTNeTIoT-L01, CICIDS2017 and IoTID20 with a smart house simulation environment configured with WSN and IoT technologies to evaluate performance. Accuracy, precision, and recall were all considered while assessing the dataset under consideration. When compared to five other machine learning models, the RL-DQN model method has demonstrated superior performance. This model outperforms the other five that were tested.

Cyber security threats in the microbial genomics era: implications for public health.

Next generation sequencing (NGS) is becoming the new gold standard in public health microbiology. Like any disruptive technology, its growing popularity inevitably attracts cyber security actors, for whom the health sector is attractive because it combines mission-critical infrastructure and high-value data with cybersecurity vulnerabilities. In this Perspective, we explore cyber security aspects of microbial NGS. We discuss the motivations and objectives for such attack, its feasibility and implications, and highlight policy considerations aimed at threat mitigation. Particular focus is placed on the attack vectors, where the entire process of NGS, from sample to result, could be vulnerable, and a risk assessment based on probability and impact for representative attack vectors is presented. Cyber attacks on microbial NGS could result in loss of confidentiality (leakage of personal or institutional data), integrity (misdetection of pathogens) and availability (denial of sequencing services). NGS platforms are also at risk of being used as propagation vectors, compromising an entire system or network. Owing to the rapid evolution of microbial NGS and its applications, and in light of the dynamics of the cyber security domain, frequent risk assessments should be carried out in order to identify new threats and underpin constantly updated public health policies.

A Novel Methodology to Improve Cooling Efficiency at Data Centers

Data centers are mission-critical infrastructures. There are strict service level requirements and standards imposed on operators and maintainers to ensure reliable run-the-clock operation. In the context of thermal management and data hall environmental control, the formation of hot and cold spots around server cabinets are especially undesirable, and can result in overheating, lifespan reductions, and performance throttling in the former and condensation damage in the latter. In this paper, we present a comprehensive multi-pronged methodology in data center environmental control, comprising computational fluid dynamics (CFD) simulation-aided predictive design first-stage approach, and a complementing Internet of Things (IoT) reactive management system that autonomously monitors and regulates fluctuations in thermal parameters. The novel hybrid methodology is demonstrated on various test scenarios derived from real-world context, and prototypes of the IoT system have been experimentally validated. The approach is shown to be efficient in eliminating unfavourable environmental variations and provides an enhanced understanding of common design problems and respective mitigation measures.

Resilient End-to-End Message Protection for Cyber-Physical System Communications

Cyber-physical system (CPS) communications for safely and effectively operating a mission-critical infrastructure must be securely protected to prevent the infrastructure from becoming vulnerable. The protection scheme used must be resilient and light-weighted for CPS field devices having constrained computing and communicating resources, and also scalable for control servers associating with a large number of the field devices. In addition, CPS applications such as smart metering require end-to-end privacy protection. However, as shown in this paper, none of conventional security schemes comprehensively meets the above requirements; group security schemes scale well for a massive number of devices but are weak in terms of privacy protection and resilience; point-to-point security schemes such as IPsec inherently have resilience but are limited to address scalability and thinness requirements. Motivated by the limitations of conventional security schemes, we design new group security scheme, resilient end-to-end message protection (REMP), exploiting the following notions: long-term keys per-node that are given by REMP authentication server, encryption keys per message sent that are probabilistically derived from a long-term key, and end-to-end authenticators per message sent that consist of a message sender's identity and a message authentication code. Compared with conventional group security schemes, we improve end-to-end security strength in terms of confidentiality, integrity, message source authentication, and key exposure resilience, while preserving scalability and extensibility.

Characterizing and Modeling Patching Practices of Industrial Control Systems

Industrial Control Systems (ICS) are widely deployed in mission critical infrastructures such as manufacturing, energy, and transportation. The mission critical nature of ICS devices poses important security challenges for ICS vendors and asset owners. In particular, the patching of ICS devices is usually deferred to scheduled production outages so as to prevent potential operational disruption of critical systems. Unfortunately, anecdotal evidence suggests that ICS devices are riddled with security vulnerabilities that are not patched in a timely manner, which leaves them vulnerable to exploitation by hackers, nation states, and hacktivist organizations. In this paper, we present the results from our longitudinal measurement and characterization study of ICS patching behavior. Our study is based on IP scan data collected from Shodan over the duration of three years for more than 500 known industrial ICS protocols and products. Our longitudinal measurements reveal the impact of vulnerability disclosures on ICS patching. Our analysis of more than 100 thousand Internet-exposed ICS devices reveals that about 50% upgrade to newer patched versions within 60 days of a vulnerability disclosure. Based on our measurement and analysis, we further propose a variation of the Bass model to forecast the patching behavior of ICS devices. The evaluation shows that our proposed models have comparable prediction accuracy when contrasted against traditional ARIMA timeseries forecasting models, while requiring less parameters and being amenable to direct physical interpretation.

Characterizing and Modeling Patching Practices of Industrial Control Systems

Industrial Control Systems (ICS) are widely deployed in mission critical infrastructures such as manufacturing, energy, and transportation. The mission critical nature of ICS devices poses important security challenges for ICS vendors and asset owners. In particular, the patching of ICS devices is usually deferred to scheduled production outages so as to prevent potential operational disruption of critical systems. In this paper, we present the results from our longitudinal measurement and characterization study of ICS patching behavior. Our analysis of more than 100 thousand Internet-exposed ICS devices reveals that fewer than 30% upgrade to newer patched versions within 60 days of a vulnerability disclosure. Based on our measurement and analysis, we further propose a model to forecast the patching behavior of ICS devices.

Understanding the threats to DNS and how to secure it

Essentially the address book of the Internet, the Domain Name System (DNS) is a mission-critical infrastructure used by all organisations and without which they cannot function. Yet, despite its importance, it remains a vulnerable network component that is frequently used as an attack vector by cyber-criminals, and is inadequately protected by traditional security solutions. The Domain Name System (DNS) is a mission-critical infrastructure that all organisations use and can't function without. But it is a highly vulnerable network component. External DNS servers may be subject to attacks such as DNS DDoS, exploits and reconnaissance, but threats may not always be outside an organisation's firewall. Chris Marrison of Infoblox discusses the importance of securing external DNS from cyber-attacks, as well as securing internal DNS from infrastructure attacks, APTs and malware that exploit DNS, and data exfiltration via DNS.

Effect of bond length on the behaviour of CFRP strengthened concrete-filled steel tubes under transverse impact

Concrete-filled steel tubular (CFST) columns have shown great potential as axial load carrying member and used widely in many mission critical infrastructures. However, attention is needed to strengthen these members where transverse impact force is expected to occur due to vehicle collisions. In this work, finite element (FE) model of carbon fibre reinforced polymer (CFRP) strengthened CFST columns are developed and the effect of CFRP bond length is investigated under transverse impact loading. Initially the numerical models have been validated by comparing impact test results from literature. The validated models are then used for detail parametric studies by varying the length of externally bonded CFRP composites. The parameters considered for this research are impact velocity, impact mass, CFRP modulus, adhesive type, and axial static loading. It has been observed that the effect of CFRP strengthening is consistent after an optimum effective bond length of CFRP wrapping. The effect of effective bond length has been studied for above parameters. The results show that, under combined axial static and transverse impact loads CFST columns can successfully prevent global buckling failure by strengthening only 34% of column length. Therefore, estimation of effective bond length is essential to utilise the CFRP composites cost effectively.

What would NASA do? Mission-critical infrastructure for species exploration

Three House of Lords reports and many essays and articles have lamented the loss of prestige and support for taxonomy in the UK, Europe and the United States, but little has been done to stop the haemorrhaging of expertise, continued neglect of natural history collections, and erosion of fundamental descriptive taxonomic scholarship. We have tolerated ignorance of earth's species so long that science policy makers seem to have forgotten that this need not be so. Advances in theory, technology, and practices have positioned taxonomy for a renaissance in species exploration. What is needed is to look at the role and significance of taxonomy through fresh eyes and see its true potential without the assumptions and biases of the past. The ‘big questions’ of taxonomy have changed little in more than two centuries, but our ability to answer them has dramatically improved. Looking at taxonomy, as if it were a NASA mission to some other habitable planet, points to both the importance of taxonomy and the folly of current neglect for its unique needs.

Effects of Large Cumulative Travel on the Behavior of Lead-Rubber Seismic Isolation Bearings

Lead-rubber bearings are seismic isolators that have been used extensively to protect buildings, bridges, and mission-critical infrastructure from the damaging effects of earthquake shaking. In bridges, lead-rubber bearings are subjected to continuous movement due to service (traffic) and temperature effects. This paper presents data on the effects of cumulative travel on the mechanical properties of lead-rubber bearings based on an experimental investigation of both medium- and small-sized bearings. A theoretical interpretation of the test results is presented.

Enabling Highly Survivable Automated On-Demand Dynamic Network Services with Intelligent Optical Control Planes

Globalization has changed the face and relevance of communication networks dramatically. Communication systems have become the most significant element of mission-critical infrastructure for consumers, businesses, and governments worldwide. Minimizing the risk of disruptions from human error, acts of war, terrorism, or natural disasters, while simultaneously creating a flexible, rapidly provisioned, on- demand network has become a business imperative, and requires the automation of key functions within network operations. Consequently, network architects are building survivable optical networks using intelligent optical control planes.

Experiences in deploying a wireless mesh network testbed for traffic control

Wireless mesh networks (WMN) have attracted considerable interest in recent years as a convenient, flexible and low-cost alternative to wired communication infrastructures in many contexts. However, the great majority of research on metropolitan-scale WMN has been centered around maximization of available bandwidth, suitable for non-real-time applications such as Internet access for the general public. On the other hand, the suitability of WMN for mission-critical infrastructure applications remains by and large unknown, as protocols typically employed in WMN are, for the most part, not designed for real-time communications. In this paper, we describe the Smart Transport and Roads Communications (STaRComm) project at National ICT Australia (NICTA), which sets a goal of designing a wireless mesh network architecture to solve the communication needs of the traffic control system in Sydney, Australia. This system, known as SCATS (Sydney Coordinated Adaptive Traffic System)and used in over 100 cities around the world, connects a hierarchy of several thousand devices -- from individual traffic light controllers to regional computers and the central Traffic Management Centre (TMC)-- and places stringent requirements on the reliability and latency of the data exchanges. We discuss our experience in the deployment of an initial testbed consisting of 7 mesh nodes placed at intersections with traffic lights, and share the results and insights learned from our measurements and initial trials in the process.

A DSS Design Model for complex problems: Lessons from mission critical infrastructure

This paper presents a new DSS Design Model for complex, mission critical decision-making situations and its technical, conceptual, and partial empirical evaluation. The new model was derived from conceptual design research and through a deep qualitative field research study at an electric power utility control center—a typical arena for “wicked” problems. The model suggests an iterative process of theory, simulation, and decision-making interactions. The DSS Design Model is validated using a tool instantiation, microgrid mini-case, and current research of a KMDSS for telecommunications. Main findings suggest that broader and more integrated approaches are necessary to design DSS for complex domains.

Correctness-preserving derivation of concurrent garbage collection algorithms

Constructing correct concurrent garbage collection algorithms is notoriously hard. Numerous such algorithms have been proposed, implemented, and deployed - and yet the relationship among them in terms of speed and precision is poorly understood, and the validation of one algorithm does not carry over to others.As programs with low latency requirements written in garbagecollected languages become part of society's mission-critical infrastructure, it is imperative that we raise the level of confidence in the correctness of the underlying system, and that we understand the trade-offs inherent in our algorithmic choice.In this paper we present correctness-preserving transformations that can be applied to an initial abstract concurrent garbage collection algorithm which is simpler, more precise, and easier to prove correct than algorithms used in practice--but also more expensive and with less concurrency. We then show how both pre-existing and new algorithms can be synthesized from the abstract algorithm by a series of our transformations. We relate the algorithms formally using a new definition of precision, and informally with respect to overhead and concurrency.This provides many insights about the nature of concurrent collection, allows the direct synthesis of new and useful algorithms, reduces the burden of proof to a single simple algorithm, and lays the groundwork for the automated synthesis of correct concurrent collectors.