Latest news with #anomalyDetection
National Post
08-07-2025
- Business
- National Post
Tosibox Redefines OT Network Control with Launch of Advanced Network Traffic Analytics (TosiANTA)
Article content Purpose-built solution delivers real-time visibility and anomaly detection for operational technology environments as industrial cyberattacks surge 49% year-over-year Article content OULU, Finland & IRVING, Texas — Tosibox, the global pioneer in providing solutions to connect, protect and control OT networks, today announced the launch of TosiANTA (Tosibox Advanced Network Traffic Analytics), a breakthrough solution that fundamentally redefines what comprehensive OT network control means for industrial organizations facing an unprecedented cyber threat landscape. Article content Redefining Control in an Era of Escalating Threats Article content Industrial organizations today face a cybersecurity crisis that demands a complete redefinition of network control. Recent industry data reveals that 73% of organizations experienced intrusions impacting OT systems in 2024 – a dramatic 49% increase from 2023. With 83% of OT leaders reporting at least one security breach in the past three years and critical infrastructure experiencing over 420 million attacks between January 2023 and January 2024, traditional approaches to OT network management are failing. Article content ' Our customers tell us they need real control over their industrial networks, and today we are redefining what that means,' said Sakari Suhonen, CEO of Tosibox US. ' TosiANTA delivers visibility into every device, protocol, and data flow—something most organizations have never achieved. This launch advances our mission to provide customers the fastest way to connect, protect, and control their critical infrastructure.' Article content OT Network Control: Beyond Traditional Monitoring Article content TosiANTA addresses the fundamental problem that has left organizations without comprehensive OT network control: the inability to see, understand, and respond to their operational technology environments in real-time. While 45% of organizations now report financial impacts exceeding $500,000 from OT cyberattacks, and 49% experience more than 12 hours of operational downtime, traditional monitoring approaches create visibility gaps across OT infrastructure. Article content TosiANTA Redefines OT Network Control by Delivering: Article content Purpose-Built for Industrial Reality Article content Unlike traditional security tools adapted from IT environments, TosiANTA operates as a native module within the Tosibox Platform, requiring no additional appliances or infrastructure. This approach directly addresses the architecture gaps that prevent organizations from achieving true network control, enabling deployment in days rather than months. Article content ' We selected TosiANTA for beta testing because we need granular visibility for both security investigations and operational optimization,' said Chris Isbell, OT Manager at Howard Energy Partners. ' The ability to extend our cybersecurity governance program into the OT environment with detailed reporting and integration capabilities aligns very well with our network monitoring goals.' Article content ' We're testing TosiANTA to enhance our network visibility and operational insights,' said Nate Ferrara, I&E and SCADA Consultant at Civitas Resources. ' The potential to automatically discover assets and improve our network intelligence could significantly optimize our field operations, especially as we continue expanding through acquisitions. ' Article content Addressing the Control Crisis Article content With ransomware remaining the dominant threat – including 68 documented cyberattacks in 2023 that caused physical consequences to industrial control systems – and only 56% of organizations maintaining OT-specific incident response plans, the need for redefined network control has never been more urgent. TosiANTA enables organizations to move from reactive security postures to proactive network governance. Article content The solution eliminates the three fundamental barriers preventing comprehensive OT network control: Article content Immediate Availability and Implementation Article content TosiANTA is available immediately as part of the Tosibox Platform. Current Tosibox customers can deploy the solution in days with no additional hardware required, as TosiANTA activates on existing Tosibox infrastructure – a critical advantage in today's threat environment. Article content For more information or to schedule a demonstration of how TosiANTA redefines OT network control, visit Article content About Tosibox Article content Tosibox is the global pioneer in providing solutions to connect, protect and control OT networks. Since 2011, the company has deployed solutions to manage hundreds of thousands of end point devices and physical infrastructure, securing the associated data. With U.S. headquarters in Irving, Texas and global headquarters in Oulu, Finland, Tosibox serves 800+ direct customers globally and works with 200+ partners. The company's purpose-built OT cybersecurity platform enables rapid deployment, comprehensive visibility, and robust security that simplifies compliance and delivers measurable cost savings. Article content Article content Article content Article content Article content Contacts Article content Media Contact: Article content Article content Margaret Herndon Article content Article content Article content
Khaleej Times
04-06-2025
- Science
- Khaleej Times
UAE: Student develops AI system to help police detect crimes before they happen
A member of Dubai Police, and inspired researcher, has developed a homegrown system that could take crime prevention one step further — by detecting it before it happens. Dr Salem AlMarri, the first Emirati to earn a Ph.D. from the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), has designed a video anomaly detection (VAD) system capable of identifying unusual behaviour in real-time. The technology could, in theory, alert authorities to suspicious or harmful activity before a formal complaint is ever made, or even before a crime is committed. 'Today, we understand how a human or object looks and moves. But how do we understand something that breaks the pattern, [like] an anomaly?' AlMarri said in an interview with Khaleej Times. 'A person walking in a very weird manner could mean something is going on. It could be an accident, or a hazard, or a fight unfolding. Anomalies have different meanings in real life; and we're training AI to recognise them.' While the field of anomaly detection has existed for decades, AlMarri's research brings the concept into the realm of video and audio. Using AI, his model is trained to distinguish between normal and abnormal footage. For example, learning to identify when an incident like a robbery or assault is taking place, even if it unfolds in a subtle or non-violent manner. As an example, he cited a hypothetical scene where a man walks up to a cashier and asks for money, politely. 'A normal camera won't know what's happening, it will just see a generous cashier handing money to somebody.' But beneath the surface, the AI model may detect subtle cues like body posture, tone, micro-behaviours — that point to coercion or threat. The model must first 'understand what is normal and what is abnormal,' by being trained on large amounts of labelled footage, he explained. 'We need to show it footage of people just handling money in the normal fashion. And then we tell it, okay, this is where something bad happens — robbery, burglary, or whatever. It learns to tell the differences, like a human child. And if it predicts correctly, it gets rewarded.' Thousands of experiments AlMarri's research, carried out during his secondment from Dubai Police, involved thousands of training experiments using real-world datasets. To overcome a key challenge — that many videos don't clearly indicate when an abnormal event begins, he designed a new approach. 'I shuffled different segments of videos to create a custom dataset, one moment showing a road accident, the next showing people walking normally in a mall, then a street fight,' he explained, 'this way, the model learned to recognise when something shifted from normal to abnormal.' His work also tackled real-world obstacles that could hinder performance. He developed a benchmark that allows the model to function even when one input, audio or video, is corrupted. This has major implications in the UAE, where weather conditions like fog can obstruct video clarity. 'If there's heavy fog or noise distortion, many models fail. So we trained ours to rely on one modality if the other is compromised. This is crucial for environments like autonomous driving or surveillance during poor visibility,' he pointed. The flagship findings are part of his Ph.D. thesis at MBZUAI, conducted under the supervision of Professor Karthik Nandakumar in the Sprint AI lab, which focuses on security, privacy, and preservation technologies. Like father, like son AlMarri's journey is rooted in a childhood filled with invention. His father, an engineer, built a screw-free wind turbine in the 1990s, a computer interface for people with no limbs, and a digital attendance system for police officers — long before such technologies were mainstream. 'It was a personal challenge for me, to at least try to come close to his achievements, to carry on his legacy.' After joining Dubai Police in 2016 and working on robotics and drones, he pursued further education in AI to stay relevant as the department transformed into a data-driven force. 'Within the police, our department went from being a smart service department to an AI department. I felt like I was being outpaced,' he recalled. Following a master's in electrical engineering at Rochester Institute of Technology, he was selected for MBZUAI's first Ph.D. cohort in computer vision - a move he describes as transformative. "MBZUAI humbled me,' described the 30-year-old. 'I had won competitions and worked on great projects, but this was something different. I was challenged over and over. When I walked out the door, I thought I didn't know anything. But when I came into reality, I realised I had been equipped to face any challenge.' The road ahead AlMarri is now preparing to return to Dubai Police and hopes to present his work to senior leadership. While the system has not yet been implemented by the police, he believes it could have significant value.'They have done exceptionally,' he said, referring to the force's AI capabilities. '[The technology] works. It can be deployed. It's up to them how they want to use it.' He expressed confidence that Dubai Police, a recognised leader in smart policing, would be well-positioned to integrate the research. 'They've reached a high level of maturity in AI. I believe I'm returning to an entity that can make effective use of what I've worked on, and I hope to contribute to their development journey. If we have this conversation in a year, the impact will be evident,' he said confidently. As for what's next, AlMarri hopes to publish research regularly, mentor young talent, and continue innovating - always with the goal of giving back to his country. 'I've been blessed to be the first Emirati Ph.D. from MBZUAI,' he noted. 'That comes with responsibility. Research is one way to give back, not just to science, but to the UAE.'
Auto Express
27-05-2025
- Automotive
- Auto Express
AI means your car can now detect a fault… before it happens
Yes you read that right. New tech has been developed using artificial intelligence (AI) meaning your car can detect a fault before it even occurs, giving the driver a nudge to take it to the garage in order to get it checked out before something serious develops. The latest Porsche Macan benefits from new software called 'AI Preventative Anomaly Detection' which essentially sends real-time car diagnostics data to the cloud. This is then analysed by artificial intelligence to pick out anomalies in the battery system, which could potentially lead to a problem developing down the line. Advertisement - Article continues below Irregularities can be pinpointed to individual cells of the battery, with shifts in capacity or a change in the balancing characteristics potentially pointing to a looming issue. Once the data has been scrutinised and if a potential problem is suspected, the owner will be notified on their 'My Porsche' app, and advised to take their car in to be inspected in their local Porsche centre. Speaking to Auto Express, Porsche's head of data-driven quality, Nora Lobenstein, explained how this type of technology could become revolutionary because 'up until now, [manufacturers] have been reactionary to problems. With this preventative concept, we can detect a problem, even if for the customer it's not possible.' 'It's really interesting what we learn from all of this,' Lobenstein continued, 'especially in how high-voltage cells and battery systems behave. It's not something that we could monitor before.' Of course, as is always the case when 'AI' and 'collecting data' are mentioned, questions arise surrounding information security. However, Porsche was keen to reassure us that only car-related information is collected – no personal, nor locatory data is sent out – and customers can opt out at any time. While this AI tech is only available in the Macan for now, it could eventually come to the Taycan – this already benefits from non-AI-assisted monitoring – as well as future electric Porsche models, including the forthcoming electric Porsche 718 sports car. Lobenstein also hinted it could come to ICE Porsche cars, such as the 911, but said 'for now, we are concentrating on high-voltage battery technology'. Now you can buy a car through our network of top dealers around the UK. Search for the latest deals…
