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Today’s organisations face numerous diverse threats to their people, places and property, sometimes simultaneously. Security leaders now know all too well how a pandemic can cripple a company’s ability to produce goods and services, or force production facilities to shut down, disrupting business continuity. For example, a category three hurricane barreling towards the Gulf of Mexico could disable the supplier’s facilities, disrupt the supply chain and put unexpected pressure on an unprepared local power grid.
Tracking such risk is hard enough, but managing it is even more difficult. A swift response depends on delivering the right information to the right people, at the right time. And, it’s not as easy as it sounds. Indeed, 61 percent of large enterprises say critical information came too late for them, in order to mitigate the impact of a crisis, according to Aberdeen Research (Aberdeen Strategy & Research).
These challenges are accelerating the hype around Artificial Intelligence (AI)
These challenges are accelerating the hype around Artificial Intelligence (AI). The technology promises to help us discover new insights, predict the future and take over tasks that are now handled by humans. Maybe even cure cancer.
But is AI really living up to all this hype? Can it really help security professionals mitigate risk? After all, there’s a serious need for technology to provide fast answers to even faster-moving issues, given the proliferation of data and the speed at which chaos can impact operations.
Risk managers face three major obstacles to ensuring business continuity and minimising disruptions. These include:
Although Artificial Intelligence can help us automate simple tasks, such as alert us to breaking news, it requires several Machine Learning systems to deliver actionable risk intelligence. Machine Learning is a branch of AI that uses algorithms to find hidden insights in data, without being programmed where to look or what to conclude. More than 90 percent of risk intelligence problems use supervised learning, a Machine Learning approach defined by its use of labelled datasets.
The benefit of supervised learning is that it layers several pre-vetted datasets, in order to deliver context-driven AI
The benefit of supervised learning is that it layers several pre-vetted datasets, in order to deliver context-driven AI. Reading the sources, it can determine the category, time and location, and cluster this information into a single event. As a result, it can correlate verified events to the location of the people and assets, and notify in real time. It’s faster, more customised and more accurate than simple Artificial Intelligence, based on a single source of data.
How does this work in the real world? One telecommunications company uses AI and ML to protect a mobile workforce, dispersed across several regions.
An AI-powered risk intelligence solution provides their decision makers with real-time visibility into the security of facilities, logistics and personnel movements. Machine Learning filters out the noise of irrelevant critical event data, allowing their security teams to focus only on information specific to a defined area of interest. As a result, they’re able to make informed, proactive decisions and rapidly alert employees who are on the move.
To gain real actionable risk intelligence, an AI solution should support four key capabilities:
The ability to minimise disruptions and ensure business continuity depends on speed, relevance and usability. AI and ML aren’t simply hype. Instead, they’re vital tools that make it possible for security professionals to cut through the noise faster and protect their people, places and property.
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Intrusion alarm systems are currently facing a growing number of potential error sources in the environment. At the same time, alarm systems must comply with increasingly demanding legal requirements for sensors and motion detectors. As a future-proof solution, detectors equipped with Sensor Data Fusion technology raise the level of security while reducing the risk of cost- and time-intensive false alarms. This article provides a comprehensive overview of Sensor Data Fusion technology. Anti-masking alarms A cultural heritage museum in the South of Germany for decades, the installed intrusion alarm system has provided reliable protection on the premises. But suddenly, the detectors trigger false alarms every night after the museum closes. The system integrators are puzzled and conduct extensive tests of the entire system. When they finally identify the culprit, it’s unexpected: As it turns out, the recently installed LED lighting system in the museum’s exhibition spaces radiates at a wavelength that triggers anti-masking alarms in the detectors. Not an easy fix situation, since a new lighting system would prove far too costly. Ultimately, the integrators need to perform extensive detector firmware updates and switch to different sensor architecture to eliminate the error source. This scenario is by no means an isolated incident, but part of a growing trend. Need for reliable detector technology Legal requirements for anti-masking technology are becoming stringent in response to tactics by criminals The number of potential triggers for erroneous alarms in the environment is on the rise. From the perspective of system operators and integrators, it’s a concerning development because every false alarm lowers the credibility of an intrusion alarm system. Not to mention steep costs: Every false call to the authorities comes with a price +$200 tag. Aside from error sources in the environment, legal requirements for anti-masking technology are becoming more stringent in response to ever more resourceful tactics employed by criminals to sidestep detectors. What’s more, today’s detectors need to be fortified against service outages and provide reliable, around-the-clock operability to catch intruders in a timely and reliable fashion. Sensor Data Fusion Technology In light of these demands, one particular approach has emerged as a future-proof solution over the past few years: Sensor Data Fusion technology, the combination of several types of sensors within one detector – designed to cross-check and verify alarm sources via intelligent algorithms – holds the keys to minimising false alarms and responding appropriately to actual alarm events. This generation of detectors combines passive infrared (PIR) and microwave Doppler radar capabilities with artificial intelligence (AI) to eliminate false alarm sources without sacrificing catch performance. Motion detectors equipped with Sensor Data Fusion technology present a fail-proof solution for building security “It’s not about packing as many sensors as possible into a detector. But it’s about including the most relevant sensors with checks and balances through an intelligent algorithm that verifies the data for a highly reliable level of security. The result is the highest-possible catch performance at the minimum risk for erroneous alarms,” said Michael Reimer, Senior Product Manager at Bosch Security Systems. Motion detectors with sensor data fusion Looking ahead into the future, motion detectors equipped with Sensor Data Fusion technology not only present a fail-proof solution for building security. The comprehensive data collected by these sensors also unlock value beyond security: Constant real-time information on temperature and humidity can be used by intelligent systems and devices in building automation. Integrated into building management systems, the sensors provide efficiency improvements and lowering energy costs Integrated into building management systems, the sensors provide the foundation for efficiency improvements and lowering energy costs in HVAC systems. Companies such as Bosch support these network synergies by constantly developing and optimising intelligent sensors. On that note, installers must be familiar with the latest generation of sensor technology to upgrade their systems accordingly, starting with a comprehensive overview of error sources in the environment. Prominent false alarm triggers in intrusion alarm systems The following factors emerge as frequent triggers of false alarms in conventional detectors: Strong temperature fluctuations can be interpreted by sensors as indicators of a person inside the building. Triggers range from floor heating sources to strong sunlight. In this context, room temperatures above 86°F (30°C) have proven particularly problematic. Dust contamination of optical detectors lowers the detection performance while raising susceptibility to false alarms. Draft air from air conditioning systems or open windows can trigger motion sensors, especially when curtains, plants, or signage attached to the ceilings (e.g. in grocery stores) are put in motion. Strong light exposure directly on the sensor surface, e.g. caused by headlights from passing vehicles, floodlights, reflected or direct sunlight – all of which sensors may interpret as a flashlight from an intruder. Extensive bandwidth frequencies in Wi-Fi routers can potentially confuse sensors. Only a few years ago, wireless routers operated on a bandwidth of around 2.7GHz while today’s devices often exceed 5GHz, thereby catching older detectors off guard. LED lights radiating at frequencies beyond the spectrum of visible light may trigger sensors with their infrared signals. Regarding the last two points, it’s important to note that legislation provides clear guidelines for the maximum frequency spectrum maintained by Wi-Fi routers and LED lighting. Long-term security But the influx of cheap and illegal products in both product groups – products that do not meet the guidelines – continues to pose problems when installed near conventional detectors. For this reason, Sensor Data Fusion technology provides a reliable solution by verifying alarms with data from several types of sensors within a single detector. Beyond providing immunity from false alarm triggers, the new generation of sensors also needs to comply with the current legislature. These guidelines include the latest EN50131-grade 3, and German VdS class C standards with clear requirements regarding anti-masking technology for detecting sabotage attempts. This is exactly where Sensor Data Fusion technology provides long-term security. Evolution of intrusion detector technology Initially, motion detectors designed for intrusion alarm systems were merely equipped with a single type of sensor; namely passive infrared technology (PIR). Upon their introduction, these sensors raised the overall level of building security tremendously in automated security systems. But over time, these sensors proved limited in their catch performance. As a result, manufacturers began implementing microwave Doppler radar capabilities to cover additional sources of intrusion alarms. First step detection technology In Bosch sensors, engineers added First Step detection to trigger instant alarms upon persons entering a room Over the next few years, sensors were also equipped with sensors detecting visible light to catch flashlights used by burglars, as well as temperature sensors. In Bosch sensors, engineers added proprietary technologies such as First Step detection to trigger instant alarms upon persons entering a room. But experience in the field soon proved, especially due to error sources such as rats and other animals, that comprehensive intrusion detection demands a synergetic approach: A combination of sensors aligned to cross-check one another for a proactive response to incoming signals. At the same time, the aforementioned bandwidth expansion in Wi-Fi routers and LED lighting systems required detectors to implement the latest circuit technology to avoid serving as ‘antennas’ for undesired signals. Sensor data fusion approach At its very core, Sensor Data Fusion technology relies on the centralised collection of all data captured by the variety of different sensors included in a single detector. These data streams are directed to a microprocessor capable of analysing the signals in real-time via a complex algorithm. This algorithm is the key to Sensor Data Fusion. It enables the detector to balance active sensors and adjust sensitivities as needed, to make truly intelligent decisions regarding whether or not the data indicates a valid alarm condition – and if so, trigger an alarm. Advanced verification mechanisms The current generation of Sensor Data Fusion detectors, for instance from Bosch, feature advanced verification mechanisms, including Microwave Noise Adaptive Processing to easily differentiate humans from false alarm sources (e.g. ceiling fans or hanging signs). For increased reliability, signals from PIR and microwave Doppler radar are compared to determine whether an actual alarm event is taking place. Additionally, the optical chamber is sealed to prevent drafts and insects from affecting the detector, while the detector is programmed for pet and small animal immunity. Sensor cross-verification Further types of sensors embedded in current and future generations of Sensor Data Fusion detectors include MEM-sensors as well as vibration sensors and accelerometers. Ultimately, it’s important to keep in mind that the cross-verification between sensors serves to increase false alarm immunity without sacrificing the catch performance of actual intruders. It merely serves to cover various indicators of intrusion. Protecting UNESCO World Cultural Heritage in China Intelligent detectors equipped with Sensor Data Fusion are protecting historic cultural artifacts in China from theft and damage. At the UNESCO-protected Terracotta Warriors Museum site, one hundred TriTech motion detectors from Bosch with PIR and microwave Doppler radar technology safeguard the invaluable treasures against intruders. To provide comprehensive protection amid the specific demands of the museum site, the detectors have been installed on walls and ceilings to safeguard the 16,300-square-meter museum site. To ensure an optimal visitor experience without interference from glass walls and other barriers, many detectors are mounted at a height of 4.5 meters (15 feet) above ground under the ceiling. Despite their height, the detectors provide accurate data around the clock while exceeding the performance limits of conventional motion detectors, which clock out at a mere 2 meters (6 feet) catchment area. Integrated video systems The site also presents additional error sources such as large amounts of dust that can contaminate the sensors, as well as visitors accidentally dropping their cameras or mobile phones next to museum exhibits. To distinguish these events from actual criminal activity, the intrusion alarm system is integrated with the museum’s video security system. This allows for verifying alarm triggers with real-time video footage at a fast pace: In the case of an actual alarm event, the system alerts the on-site security personnel in the control room in less than two seconds. Added value beyond security Sensor Data Fusion technology provides a viable solution for the rising number of error sources in the environment As of today, Sensor Data Fusion technology already provides a viable solution for the rising number of error sources in the environment while providing legally compliant building security against intruders. In light of future developments, operators can leverage significant added value from upgrading existing systems – possibly without fundamentally replacing current system architecture – to the new detector standard. Added value how? On one hand, the detectors can integrate with access control, video security, voice alarm, and analytics for a heightened level of security. These synergetic effects are especially pronounced on end-to-end platforms like the Bosch Building Management system. On the other hand, the data streams from intelligent detectors also supply actionable intelligence to building automation systems, for instance as the basis for efficiency improvements and lowering energy consumption in HVAC systems. New backward-compatible detectors Bosch will release a new series of commercial detectors by end of 2021, based on the latest research on risk factors for false alarm sources in the environment and line with current legislation and safety standards. Throughout these developments, installers can rest assured that all new detectors are fully backward compatible and work with existing networking/architecture. With that said, Sensor Data Fusion technology emerges as the key to more secure intrusion alarm systems today and in the future. TriTech detectors from Bosch For reliable, fail-proof alarms the current series of TriTech detectors from Bosch relies on a combination of different sensor data streams, evaluated by an integrated algorithm. These Sensor Data Fusion detectors from Bosch combine up to five different sensors in a single unit, including: Long-range passive infrared (PIR) sensor Short-range PIR sensor Microwave sensor White light sensor Temperature sensor Equipped with these sensors, TriTech detectors are capable of detecting the most frequent sources of false alarms; from headlights on passing cars to a mouse passing across the room at a 4.5-meter distance to the detector. What’s more, TriTech detectors provide reliable performance at room temperatures above 86°F (30°C) while fully guarding against actual intrusion and sabotage attempts from criminals.
From analogue to digital, from stand-alone to interlinked, building systems are in a state of transition. Moreover, the rate of change shows no sign of slowing, which can make it difficult to keep up to date with all the latest developments. If asked to pinpoint the single biggest driver of this revolution, one could point out the growing clamour for platform convergence. A security guard in a building doesn’t want to use different systems to check video cameras, fire alarms or if someone has entered a restricted area: – it simply isn’t efficient. For similar reasons, a building manager wants a single interface to control heating and lighting to match fluctuating occupancy levels, particularly in a hybrid working model. Applying the digital glue The demand from end-users for system convergence is growing, but to achieve full interoperability you still need to apply some ‘digital glue’ and that requires expertise. Yet bringing together disparate systems from different manufacturers can be problematic. Just as you get things to work, someone upgrades their solution and your carefully implemented convergence can start to come unstuck. Managing an implementation can quickly become more complicated, today’s quick-fix can become tomorrow’s headache This is one of the principal issues with all types of new technology; not everyone will choose the same path to reach the desired goal – it’s the old VHS/Betamax argument updated for building management and security systems. Managing and maintaining an implementation can quickly become more complicated than it first appears and without proper oversight, today’s quick-fix can become tomorrow’s technical headache. Effective support for a hybrid workforce Today’s hybrid workforce is a response to the pandemic that looks set to become an established part of working life for many companies across the world. Security systems have a massive role to play in facilitating this transformation that goes beyond simple intrusion detection, access control, and video monitoring. They can identify the most densely populated areas in a building to comply with social distancing guidelines and provide efficient use of space. The insights gathered from a security system can also be used to identify patterns of behaviour, which can then be used for planning and directing the use of building space to help create the best possible working environment while also minimising heating, lighting, and air conditioning expenditures. Identity credentials can help manage compliance with industry regulations by limiting access to certain areas Similarly, identity credentials – either biometric or mobile-based – can help manage compliance to industry regulations by limiting access to certain areas only to approved employees. Creating and maintaining the appropriate level of functionality requires a combination of innovative solutions and industry experience. The complete security package It’s not just physical security that’s important – cybersecurity is a major focus, too. Bringing together both the physical security and cybersecurity realms is increasingly becoming a ‘must have’ capability. What is evident is that the pace of technological change is faster than ever. Today’s functionality simply wouldn’t have been possible just a few years ago, while today’s leading-edge developments may seem commonplace in five years.
Today, we live in a technology-obsessed age. Whichever way you look, it’s hard to avoid the increasing number applications, products and solutions that continue to redefine the boundaries of what we previously thought possible. From autonomous vehicles and edge computing to 5G and the Internet of Things, all facets of our lives are continuing to evolve, thanks to an endless stream of differentiated innovations. In this article, we’ll be focusing on the latter of these – the Internet of Things (IoT). Deployment of IoT technologies Smart homes, smart utilities, smart retail, smart farming, smart supply chains and many of the other ‘smart’ versions of sectors that we’re already familiar with, are all called as such because of the implications of IoT. Indeed, it is a technology that has manifested itself in billions of devices, which today underpin the truly transformational levels of connectivity that we see across industries of all shapes and sizes. The statistics speak for themselves. According to Statista, over US$ 1 trillion is expected to be spent on IoT technology worldwide, in 2022. Be it added convenience, efficiency, productivity or intelligence, many benefits are poised to emerge from this spike in IoT-related activities. Yet to say this digital transition is going to be entirely positive would be naïve. Threats faced by smart cities It is said that by 2040, 65 per cent of the world’s population will be living in cities Let’s consider smart cities. It is said that by 2040, 65 per cent of the world’s population will be living in cities. To accommodate such an influx, without facing significant logistical issues, with limited space and infrastructure, policy makers have begun to recognise that these urban environments need to become not only larger, but smarter as well. As a result, the global smart cities market is on the rise. Statista states that, globally, technology spending on smart city initiatives is expected to double from US$ 81 billion in 2018 to US$ 189.5 billion in 2023. Threat of attackers with expanding IoT landscape The challenge here is that such a stark uptick will drastically expand the IoT landscape, presenting more opportunities than ever to threat actors. As connectivity and computing power is distributed more widely across large-scale outdoor networks, hackers will scale-up their own operations in tandem. According to a Nokia report from October 2020 (based on data aggregated from monitoring network traffic on more than 150 million devices globally), IoT devices now account for roughly 33 per cent of all infected devices, up from the 16 per cent estimated in 2019. What’s more concerning is how these figures are translating into real world events. 2021 alone has already witnessed an attack on a water plant in Oldsmart, Florida, which was designed to poison residents’ drinking water. Furthermore, Colonial Pipeline, one of the largest fuel pipelines in the US was also hacked, earlier this year, resulting in major shortages across the country’s East Coast. Security through IoT authentication From weak password protection, a lack of regular patch updates and insecure interfaces, to insufficient data protection, poor IoT devices management and an IoT skills gap, there are plenty of weaknesses existing within the IoT ecosystem, which continue to provide open goals for attackers. To defend against such lethal threats, security-by-design and open standards should be the guiding principles of IoT, working to prioritise security, interoperability and robust, internet-based protocols to mitigate risks. Device authentication and encryption A sound place to start is to make device authentication and encryption the central pillars of your IoT security architecture A sound place to start, in this regard, is to make device authentication and encryption the central pillars of your IoT security architecture. The goal is to be able to prove that each and every device joining a network is not malicious, with tell-tale signs being rogue code, for example. By ensuring each device is uniquely identifiable with digital certificates and therefore, properly authenticated when joining a network, you can ensure no tampered devices are able to infiltrate your overarching network. Using technologies, such as Hardware Secure Element Critically, passwords should be avoided altogether, these vulnerable to being stolen and cracked. And, while a similar vulnerability lies in the fact that all secure devices contain a private key, you can leverage technologies, such as Hardware Secure Element (a chip designed specifically to protect against unauthorised access, even if the attacker has physical access to the device), as an extra layer of defence. Digital certificates are not the only option available in protecting those IoT devices that, if tampered with, could become the cause of physical threats. Physical Unclonable Function (PUF) can also be used to prevent tampering. Physical Unclonable Function (PUF) Through Physical Unclonable Function (PUF), a form of IoT device fingerprint is developed from the unique make up of a piece of silicon, which can be used to create a unique cryptographic key. Unlike digital certificates, a secure infrastructure can be achieved through PUF, without the need for any additional hardware, as the key is not only stored securely, but it also becomes invisible to hackers, when the device is not running. The importance of encryption Use of AES encryption within radio chips, to scramble messages on the move, is the method adopted at Wi-SUN Alliance Now, let’s turn attentions to encryption. The use of AES encryption within radio chips, to scramble messages on the move, is the method that we have adopted here at Wi-SUN Alliance. It’s a means of maximising data security, but also reducing power consumption in the devices themselves. Beyond AES encryption, it’s also worth considering topography at the design stage. Indeed, mesh networks are advantageous for several reasons. They are more reliable, allowing data to be re-routed, should devices lose contact unexpectedly. Transmissions usually travel shorter distances, which improves power efficiency and performance, and frequency hopping functionality prevents attackers from jamming signals, which could deny the service altogether. Open standards and interoperability But where do open, interoperable standards fit in? As is defined by the European Committee for Interoperable Systems (ECIS), interoperability enables a computer programme to communicate and exchange information with other computer programmes, allowing all programmes to use that information. Open standards then allow any vendor of communications equipment or services to implement all standards necessary, to interoperate with other vendors. This is incredibly useful from a security perspective. It means that all specs are stress-tested and verified by many users, and that any vulnerabilities are quickly detected, and remediated, enhancing security and reliability. Need for open standards Equally, open standards can accelerate time-to-market, reduce costs and ensure products are usable, with a variety of manufacturers’ processors and radios, with a steam of publicly available protocol stacks, design information and reference implementations available that can help build and future-proof secure products. Indeed, large-scale corporate IoT networks alongside smart cities, smart utilities, and other key smart infrastructure will only continue to evolve, in the coming years. With the immense threats of attackers in mind, these systems must prioritise security-by-design, both now and in the future.
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