Advanced Cybersecurity Algorithm Halts Military Robot Attacks with 99% Accuracy
The post Advanced Cybersecurity Algorithm Halts Military Robot Attacks with 99% Accuracy appeared on BitcoinEthereumNews.com. In a groundbreaking leap for cybersecurity, Australian researchers from Charles Sturt University and the University of South Australia have introduced an algorithm that promises to redefine the security landscape for unmanned military robots. The team employed deep learning neural networks, emulating the intricacies of the human brain, to train the robot’s operating system in identifying and halting man-in-the-middle (MitM) cyberattacks. In essence, this innovative approach involves interrupting an ongoing conversation or data transfer, a vulnerability that attackers exploit. The algorithm underwent a real-time trial on a replica of a United States army combat ground vehicle, and the results are nothing short of extraordinary. With a staggering 99% success rate in preventing malicious attacks and a false positive rate of less than 2%, the algorithm showcased its prowess. These groundbreaking findings have been documented in IEEE Transactions on Dependable and Secure Computing, a testament to the significance of this achievement. The cybersecurity algorithm’s test and triumph In collaboration with the US Army Futures Command, Professor Anthony Finn and Dr. Fendy Santoso from Charles Sturt Artificial Intelligence and Cyber Futures Institute orchestrated a sophisticated experiment. They replicated a man-in-the-middle cyberattack on a GVT-BOT ground vehicle, training its operating system, known as the robot operating system (ROS), to recognize and counteract such attacks. Professor Finn highlights the susceptibility of ROS to data breaches and electronic hijacking due to its extensive networking. This vulnerability arises from the demand of Industry 4, where collaborative work among robots via cloud services exposes them to cyber threats. Professor Finn underscores the impact of Industry 4, emphasizing the collaborative nature demanded from robots in this era of robotics, automation, and the Internet of Things. The need for sensors, actuators, and controllers to seamlessly communicate and exchange information via cloud services is highlighted as a pivotal aspect of this…
The post Advanced Cybersecurity Algorithm Halts Military Robot Attacks with 99% Accuracy appeared on BitcoinEthereumNews.com.
In a groundbreaking leap for cybersecurity, Australian researchers from Charles Sturt University and the University of South Australia have introduced an algorithm that promises to redefine the security landscape for unmanned military robots. The team employed deep learning neural networks, emulating the intricacies of the human brain, to train the robot’s operating system in identifying and halting man-in-the-middle (MitM) cyberattacks. In essence, this innovative approach involves interrupting an ongoing conversation or data transfer, a vulnerability that attackers exploit. The algorithm underwent a real-time trial on a replica of a United States army combat ground vehicle, and the results are nothing short of extraordinary. With a staggering 99% success rate in preventing malicious attacks and a false positive rate of less than 2%, the algorithm showcased its prowess. These groundbreaking findings have been documented in IEEE Transactions on Dependable and Secure Computing, a testament to the significance of this achievement. The cybersecurity algorithm’s test and triumph In collaboration with the US Army Futures Command, Professor Anthony Finn and Dr. Fendy Santoso from Charles Sturt Artificial Intelligence and Cyber Futures Institute orchestrated a sophisticated experiment. They replicated a man-in-the-middle cyberattack on a GVT-BOT ground vehicle, training its operating system, known as the robot operating system (ROS), to recognize and counteract such attacks. Professor Finn highlights the susceptibility of ROS to data breaches and electronic hijacking due to its extensive networking. This vulnerability arises from the demand of Industry 4, where collaborative work among robots via cloud services exposes them to cyber threats. Professor Finn underscores the impact of Industry 4, emphasizing the collaborative nature demanded from robots in this era of robotics, automation, and the Internet of Things. The need for sensors, actuators, and controllers to seamlessly communicate and exchange information via cloud services is highlighted as a pivotal aspect of this…
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