{"id":54152,"date":"2023-10-16T14:14:52","date_gmt":"2023-10-16T14:14:52","guid":{"rendered":"https:\/\/packetstormsecurity.com\/news\/view\/35123\/Academics-Devise-Cyber-Intrusion-Detection-System-For-Unmanned-Robots.html"},"modified":"2023-10-16T14:14:52","modified_gmt":"2023-10-16T14:14:52","slug":"academics-devise-cyber-intrusion-detection-system-for-unmanned-robots","status":"publish","type":"post","link":"https:\/\/www.threatshub.org\/blog\/academics-devise-cyber-intrusion-detection-system-for-unmanned-robots\/","title":{"rendered":"Academics Devise Cyber Intrusion Detection System For Unmanned Robots"},"content":{"rendered":"<div><img decoding=\"async\" src=\"https:\/\/www.securityweek.com\/wp-content\/uploads\/2023\/01\/Cybersecurity_News-SecurityWeek.jpg\" class=\"ff-og-image-inserted\"><\/div>\n<p><strong>Two Australian academic researchers have devised a new cyber intrusion detection system that relies on AI to help unmanned military robots identify man-in-the-middle (MitM) cyberattacks.<\/strong>\n<\/p>\n<p>Relying on deep learning convolutional neural networks (CNNs), the new cyber-physical system is meant to reduce the vulnerabilities of the robot operating system (ROS), which is used in both civilian and military robots.\n<\/p>\n<p>Tested on a US Army GVT-BOT ground vehicle, the algorithm demonstrated a 99% accuracy, Fendy Santoso of Charles Sturt University and Anthony Finn of the University of South Australia (UniSA) note in their<a href=\"https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&amp;arnumber=10210500\"> research paper<\/a> (PDF).\n<\/p>\n<p>The cyber-intrusion detection framework primarily focuses on detecting MitM attacks, but vulnerabilities in ROS make it prone to breaches, hijacking, denial-of-service (DoS), and other types of cyberattacks, the academics say.\n<\/p>\n<p>These robots, the academics point out, are highly networked, because their different components, including sensors, actuators, and controllers rely on cloud services to transfer information and communicate.\n<\/p>\n<p>\u201cRobotic systems can be compromised at multiple different levels, namely, at the system, sub-system, component, or sub-component levels. Preventing these attacks is by no means trivial, especially for sophisticated, complex, and modern robots, which can work even under a fault-tolerant mode, blurring the line between normal operations and fault conditions,\u201d the researchers note.\n<\/p>\n<p>The MitM-detection algorithm was tested on ground robots that were connected to two separate computers over a Wi-Fi network. Once the cyberattack occurred, the robot became unresponsive, because the guidance signal it was supposed to receive was overwritten with unintended traffic data.<\/p>\n<div class=\"zox-post-ad-wrap\"><span class=\"zox-ad-label\">Advertisement. Scroll to continue reading.<\/span><\/div>\n<p>\u201cAs such, from the systems and control point of view, the robot was made blind with respect to the legitimate reference signal. This way, the attacker can also inject false data regarding the command signal to compromise the intended trajectory of the system,\u201d the researchers note.\n<\/p>\n<p>The collected data was used to train the cyber-intrusion system both under legitimate and cyberattack conditions, which ultimately yielded not only high accuracy, but also higher performance compared to other detection techniques in use, the academics claim.\n<\/p>\n<p>The researchers are considering testing their algorithm on different robotic platforms, including unmanned aerial vehicles.\n<\/p>\n<p>\u201cUnder the umbrella of deep learning (supervised and unsupervised) systems, we are also keen to study the relative merits of our CNN intrusion detection algorithm with respect to similar detection techniques such as using evolving type-2 fuzzy systems, that can accommodate the footprint-of-uncertainties,\u201d the researchers say.\n<\/p>\n<p><strong>Related:<\/strong><a href=\"https:\/\/www.securityweek.com\/researchers-extract-sounds-from-still-images-on-smartphone-cameras\/\"> Researchers Extract Sounds From Still Images on Smartphone Cameras<\/a>\n<\/p>\n<p><strong>Related:<\/strong><a href=\"https:\/\/www.securityweek.com\/new-wi-fi-attack-allows-traffic-interception-security-bypass\/\"> New Wi-Fi Attack Allows Traffic Interception, Security Bypass<\/a>\n<\/p>\n<p><strong>Related:<\/strong><a href=\"https:\/\/www.securityweek.com\/ics-vulnerabilities-chained-for-deep-lateral-movement-and-physical-damage\/\"> ICS Vulnerabilities Chained for Deep Lateral Movement and Physical Damage<\/a>\n<\/p>\n<p>READ MORE <a href=\"https:\/\/packetstormsecurity.com\/news\/view\/35123\/Academics-Devise-Cyber-Intrusion-Detection-System-For-Unmanned-Robots.html\">HERE<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>READ MORE HERE&#8230;<\/p>\n","protected":false},"author":2,"featured_media":54153,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"colormag_page_layout":"default_layout","footnotes":""},"categories":[60],"tags":[8869],"class_list":["post-54152","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-packet-storm","tag-headlinehackerbotnet"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Academics Devise Cyber Intrusion Detection System For Unmanned Robots 2026 | ThreatsHub Cybersecurity News<\/title>\n<meta name=\"description\" content=\"ThreatsHub Cybersecurity News | ThreatsHub.org | Cloud Security &amp; Cyber Threats Analysis Hub. 100% Free OSINT Threat Intelligent and Cybersecurity News.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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