34,002 research outputs found
STOP-IT: strategic, tactical, operational protection of water infrastructure against cyberphysical threats
Water supply and sanitation infrastructures are essential for our welfare, but vulnerable to several attack types facilitated by the ever-changing landscapes of the digital world. A cyber-attack on critical infrastructures could for example evolve along these threat vectors: chemical/biological contamination, physical or communications disruption between the network and the supervisory SCADA. Although conceptual and technological solutions to security and resilience are available, further work is required to bring them together in a risk management framework, strengthen the capacities of water utilities to systematically protect their systems, determine gaps in security technologies and improve risk management approaches. In particular, robust adaptable/flexible solutions for prevention, detection and mitigation of consequences in case of failure due to physical and cyber threats, their combination and cascading effects (from attacks to other critical infrastructure, i.e. energy) are still missing. There is (i) an urgent need to efficiently tackle cyber-physical security threats, (ii) an existing risk management gap in utilities’ practices and (iii) an un-tapped technology market potential for strategic, tactical and operational protection solutions for water infrastructure: how the H2020 STOP-IT project aims to bridge these gaps is presented in this paper.Postprint (published version
Applying Lessons from Cyber Attacks on Ukrainian Infrastructures to Secure Gateways onto the Industrial Internet of Things
Previous generations of safety-related industrial control systems were ‘air gapped’. In other words, process control
components including Programmable Logic Controllers (PLCs) and smart sensor/actuators were disconnected and
isolated from local or wide area networks. This provided a degree of protection; attackers needed physical access to
compromise control systems components. Over time this ‘air gap’ has gradually been eroded. Switches and
gateways have subsequently interfaced industrial protocols, including Profibus and Modbus, so that data can be
drawn from safety-related Operational Technology into enterprise information systems using TCP/IP. Senior
management uses these links to monitor production processes and inform strategic planning. The Industrial Internet
of Things represents another step in this evolution – enabling the coordination of physically distributed resources
from a centralized location. The growing range and sophistication of these interconnections create additional
security concerns for the operation and management of safety-critical systems. This paper uses lessons learned
from recent attacks on Ukrainian critical infrastructures to guide a forensic analysis of an IIoT switch. The intention
is to identify and mitigate vulnerabilities that would enable similar attacks to be replicated across Europe and North
America
Medical Cyber-Physical Systems Development: A Forensics-Driven Approach
The synthesis of technology and the medical industry has partly contributed
to the increasing interest in Medical Cyber-Physical Systems (MCPS). While
these systems provide benefits to patients and professionals, they also
introduce new attack vectors for malicious actors (e.g. financially-and/or
criminally-motivated actors). A successful breach involving a MCPS can impact
patient data and system availability. The complexity and operating requirements
of a MCPS complicates digital investigations. Coupling this information with
the potentially vast amounts of information that a MCPS produces and/or has
access to is generating discussions on, not only, how to compromise these
systems but, more importantly, how to investigate these systems. The paper
proposes the integration of forensics principles and concepts into the design
and development of a MCPS to strengthen an organization's investigative
posture. The framework sets the foundation for future research in the
refinement of specific solutions for MCPS investigations.Comment: This is the pre-print version of a paper presented at the 2nd
International Workshop on Security, Privacy, and Trustworthiness in Medical
Cyber-Physical Systems (MedSPT 2017
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
Modern urban railways extensively use computerized sensing and control
technologies to achieve safe, reliable, and well-timed operations. However, the
use of these technologies may provide a convenient leverage to cyber-attackers
who have bypassed the air gaps and aim at causing safety incidents and service
disruptions. In this paper, we study false data injection (FDI) attacks against
railways' traction power systems (TPSes). Specifically, we analyze two types of
FDI attacks on the train-borne voltage, current, and position sensor
measurements - which we call efficiency attack and safety attack -- that (i)
maximize the system's total power consumption and (ii) mislead trains' local
voltages to exceed given safety-critical thresholds, respectively. To
counteract, we develop a global attack detection (GAD) system that serializes a
bad data detector and a novel secondary attack detector designed based on
unique TPS characteristics. With intact position data of trains, our detection
system can effectively detect the FDI attacks on trains' voltage and current
measurements even if the attacker has full and accurate knowledge of the TPS,
attack detection, and real-time system state. In particular, the GAD system
features an adaptive mechanism that ensures low false positive and negative
rates in detecting the attacks under noisy system measurements. Extensive
simulations driven by realistic running profiles of trains verify that a TPS
setup is vulnerable to the FDI attacks, but these attacks can be detected
effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System
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
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