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Leading international artificial intelligence experts have collaborated with researchers from Flinders University and Brazil to develop an advanced AI model dedicated to safeguarding crucial technological infrastructure, such as power grids, water systems, and communication networks.
The innovative model offers early detection capabilities for a range of potential threats, including software virus attacks, hacker intrusions, and system failures. By leveraging the power of artificial intelligence, this cutting-edge solution aims to provide timely and accurate identification of any malicious activities or vulnerabilities within these indispensable networks, which millions of individuals worldwide depend on for their daily operations.
Dr Paulo Santos, an Associate Professor specialising in Artificial Intelligence and Robotics at Flinders University’s College of Science and Engineering, has introduced a groundbreaking algorithm designed to identify failures in data networks. This algorithm exhibits remarkable resilience in the face of inconsistencies within sensor data, enabling it to effectively detect the onset of significant disruptions that could potentially have widespread ramifications.
The algorithm’s potential applications extend beyond data networks to encompass electrical systems and other critical infrastructure, presenting a viable alternative to conventional diagnostic methods. By employing this innovative approach, the algorithm serves as a robust safeguard against equipment failures within these complex networks.
Notably, this research marks one of the initial comprehensive investigations into testing paraconsistent analysers within large-scale simulations of intricate electrical systems.
A notable instance of a breach in critical systems occurred in 2010 with the Stuxnet worm attack, which specifically aimed to infiltrate and disrupt industrial control systems, particularly those employed in Iran’s nuclear program.
Dr Paulo Santos, in collaboration with Hyghor Miranda Côrtes from Centro Universitário da FEI and João Inácio da Silva Filho from Universidade Santa Cecília Brazil, has recently published their research outcomes in an article featured in the esteemed journal Expert Systems with Applications, published by Elsevier. This publication sheds light on their findings, offering valuable insights into their study.
According to the researchers, artificial intelligence (AI) has potential to enhance software applications and fault diagnostic systems, which play a crucial role in mitigating errors within intricate engineering systems, manufacturing plants, and other critical infrastructure.
Currently, data analysis, machine learning, and rule-based learning methods are already employed to build fault diagnostic systems. However, the researchers propose that AI can further enhance these existing approaches, enabling more accurate and efficient identification of faults and errors within complex systems.
By leveraging AI technologies, such as advanced algorithms and models, these diagnostic systems can be strengthened, offering improved capabilities in detecting and preventing potential issues in critical infrastructure.
Associate Professor Santos explains that they have extended the existing approaches by incorporating an “evidence filter” into the system diagnostics process. This additional filter takes into consideration conflicting evidence by assigning a degree of trust to the sensor data.
The research team has developed a novel analytical model called the “Cubic Paraconsistent Analyser with Evidence Filter and Temporal Analysis” (CPAet). With further advancements, this model has the potential to evolve into a comprehensive solution capable of addressing increasingly complex technological failures within critical systems.
These systems are vital in supporting major industries, entire urban networks, and other essential infrastructure. By leveraging the CPAet model, researchers aim to enhance the resilience and reliability of these critical systems, safeguarding their seamless operation.
The collaboration between international AI experts, Flinders University, and Brazilian researchers is expected to significantly advance the field of safeguarding critical infrastructure. By harnessing the power of artificial intelligence, particularly through the development of innovative algorithms and models, these experts have paved the way for early detection of potential threats and failures within vital networks.
The integration of AI into fault diagnostic systems and software applications holds immense promise for improving the resilience, efficiency, and reliability of complex engineering systems, manufacturing plants, and other critical infrastructure.
As further advancements are made, such as the integration of evidence filters and temporal analysis, the potential for addressing sophisticated technological failures becomes more attainable. This research not only contributes to the realm of AI but also carries significant implications for the protection and security of essential systems that millions of people depend upon every day.
