Qognify – the trusted advisor and technology solution provider for physical security and enterprise incident management – today anounce that the latest release of its video management system (VMS) Cayuga R14, places a major focus on cyber security.
With the rise of IP solutions for video surveillance systems in recent years, dealing with cybersecurity issues to safeguard video installations has become of utmost importance. One of the best ways to reduce network vulnerabilities associated with video surveillance systems is to make sure that the communication between the different components of the video solution is securely encrypted. A comprehensive role-based access management must also be in place in order to elevate the level of protection, while meeting the compliance requirements of mission-critical environments.
Cayuga R14 is focusing on just that: the security architecture of the VMS has been revamped to make sure that internal communication is even safer than before. State-of-the-art encryption is used for the export of videos, so that they can only be accessed by authorized people. Fundamental Active Directory (AD) optimizations make sure that the login procedure for Cayuga and the administration of AD roles are now fully aligned with industry standards. As Single Sign On is now fully supported, there is no need to explicitly sign in to the Cayuga application – once the Windows login was successful.
Aside from all the security improvements, Cayuga now offers the option to start the standard client in Viewer mode. This way the user benefits from most of the standard features in the client – even without a connection to the Cayuga server. The new offline Client is also available in a light-weight portable version to be distributed with exported video files.
Furthermore, Cayuga now supports more than 5,000 different devices from more than 80 different hardware manufacturers. New camera functions have also been introduced into its smart drivers, giving customers even more choice when selecting the right hardware for a video security application.
Cayuga R14 is available now from Qognify. Current customers with a valid Software Maintenance Agreement (SMA) can download and install the update manually, or if updated from an installed base via the built-in Auto Updater.
Advance notice: 32 bit support for Cayuga will end with R15.
Cayuga so far has been available as 64- and 32 bit version. However, we already want to inform you today that Cayuga will only be available as a 64-bit version from the upcoming version R15, which is expected to be released in the 2nd quarter of 2020. Cayuga R14 is the last version for which – on request – a 32-bit version will be available.
Cayuga R14 is also the latest version to support Object Video. Customers using Object Video have already been personally informed about this change and their future migration options. Please contact us, should you have further questions about this change and what it might mean to your installation.
In order to make use of the Auto Update function in both, Cayuga and BVI in the future, we highly recommend following the procedures explained in the PDF that you’ll find here.
Due to the conversion of the communication protocol between Update Server and Update Client, it is mandatory to install the patches for both components manually.
As this patch changes the basic communication between the Auto Updater components (Update Server and Update Client), it will not be possible to have a mix of different version in one system.
DILIP VERMA, REGIONAL VP, INDIA // JANUARY 02, 2018
Technology alone doesn’t make a city safe or smart
Safety is a basic human requirement. This is why most cities have at very least a plan – and in most cases an existing program – to make themselves safe cities, and meeting that fundamental need often requires the use of technology. Now, many cities are undergoing a transition to become smart cities: urban areas where security solutions work in unison with other systems, extending the benefits of technology beyond security and into other city operations. Even though this transformation from safe to smart has yet to become a widespread reality, the next crucial transition – from smart city to cognitive city – is already appearing on the horizon. In the first of three posts about this 3-level transition, we’ll focus on “smart” and explain why “smart” means much more than technology.
The world is becoming increasingly urban. Three years ago in its World Urbanization Prospects report, the United Nations reported that 54% of the world’s population lived in cities. That same report projected that by 2050, that number will hit 66%. From New York City to New Delhi, density follows development. There are many reasons for this: cities tend to provide more opportunities for jobs and education, as well as greater access to amenities like public transportation, sports, and cultural events.
These advantages result in growth, and with growth comes strain on existing public services, infrastructure, and resources. Not to mention keeping the city’s residents safe by preventing crime from growing with – or even outpacing – the population.
This basic need for urban public safety is one of the biggest forces driving the adoption of “smart city” solutions: approaches which seek to solve urban challenges through technological means. The thinking behind these initiatives is that with enough Internet connectivity and real-time data, surely environmental, social, economic, and public health issues should become more manageable. If technology can transform entire industries, why can’t it also make our power grids more resilient, transportation systems efficient and municipal water supplies more sustainable? Surely, more data can only lead to better outcomes… right?
To quote a sharp American journalist and satirist – H. L. Mencken, “For every complex problem there is an answer that is clear, simple, and wrong”. In this context, you’d think the answer would be: “just add more technology”, right? Although tech is necessary for an urban area to transition to being a safe and smart city, tech alone isn’t sufficient. Truly smart cities are savvy cities, and that includes how they employ software, sensing, communications and other technologies to meet their needs.
There are types of problems which connected sensors, data, and software can provide straightforward and effective solutions. One example of these includes network-connected traffic cameras which can relay real-time traffic conditions to both city managers and the public at large, data which morning commuters can then access from a mobile app and adjust their route accordingly.
Smart electricity meters are another example. By monitoring and reporting energy usage in real time, residents can get instant feedback on how their lifestyle choices impact their energy consumption and their monthly bill. Utilities can also benefit from this data, as it could highlight both specific times and areas of high demand, as well as identify sections of the distribution network that are under heavy strain.
Both of these examples highlight the obvious need to collect the relevant data first, and thus explain why smart city initiatives have focused on the widespread collection of data (especially video) through the deployment of large numbers of monitoring and recording devices, like CCTV cameras and license plate readers. Some of those initiatives, however, like red light cameras or computerized flight passenger screening systems, have amounted to little more than “security theater”, which might waste limited resources and further delay the smart city transition due to over-hyped solutions and unrealistic projected ROI.
In other words, technology doesn’t necessarily result in more safety.
This new era of surveillance technologies can also assist law enforcement in maintaining public order and safety. The thought is the more areas we observe, the longer we observe them, and the more surveillance data we store and index, the more likely we are to be in possession of the information we need. But does this mean we are also more likely to quickly find what we need? Cities need solutions that help find what you need (e.g. a missing child or a suspect) and convert the “too much information” into “actionable intelligence”.
Here’s the takeaway: even in smart cities, dialogue, public input, careful analysis, and consensus are still more critical than any technology. This is because city residents are not only consumers of public services and amenities, but also citizens with legal rights. In our next post, How a smart city can benefit both consumers and citizens, we’ll see how smart cities can benefit both.
DILIP VERMA, REGIONAL VP, INDIA // JANUARY 09, 2018
Smart city initiatives can get tricky. Amazon is able to accurately recommend other products you might want to buy because the company meticulously records and analyzes your order history and browsing behavior on its site. Facebook’s behemoth “free” social networking platform is made possible by generating revenue through advertising from the information you freely (and unknowingly) hand over to the company, including your age, gender, political views, and education level. Users benefit from the free service, and companies earn revenue from the data those users give up in exchange.
Urban residents, however, aren’t mere consumers, they are citizens. Consumers provide revenue in return for a vendor giving them the goods or services they ordered. Citizens have defined legal rights, as well as responsibilities. This is one of the key reasons why the tech transformation that has occurred in the private sector has yet to have an equal impact on city life. Their governments likewise have specified legal authority, but no overriding profit motive like Google, Apple, Microsoft, or Salesforce.
While many informed consumers may balk at the privacy they forfeit for free or enhanced web services, asking citizens to volunteer data to their government in return for safety or more convenient access to public services is often a different calculus than trusting Facebook with a very complete and quantified digital portrait.
Even though many current smart city approaches depend on what are fundamentally surveillance technologies (as we pointed out in our previous article), the current transition to smart cities can benefit not only the city government and municipal managers but also all residents – both as consumers and citizens.
For example, lower cost, better service, and quicker resolution times for services such as transit and utilities (gas, water, sewage, electricity) appeal to consumers. On the other hand, skipping waiting in line for legal forms and proceedings (transfer of title, car registration, birth certificate, voter registration, voting, etc.) appeal to citizens. Since these groups largely overlap, a smart city must provide for the needs of both.
Due to the privacy issues surrounding government collection and storage of data, all smart city initiatives must effectively convey those benefits to all stakeholders (business community, non-profits, community organizations, the general public) in a compelling way, and put in place appropriate safeguards for the protection and use of all collected data, as Europe is about to do with the GDPR.
In a smart city, a lot of data flows from residents to the government. In one of our clients, a large city that has been using a combination of Qognify’s Situation Management solution (Situator), and video management together with video analytics, every citizen can approach the authorities and ask for a video clip (useful for traffic accidents, lost wallets, and the like). The security solution is then used to retrieve the precise clip and assist in resolving the situation. Obviously, this calls for clear permission levels as for who can see the footage and what it can be used for. As an external control, citizens can vote to provide feedback to the government (e.g. throw out all the officials who approved the technology that is deemed too intrusive).
Consumers provide feedback too, most notably through voting with their wallet. Additionally, they can provide the kind of continuous feedback and interaction that’s integral to modern tech-enabled businesses and do so in a way which augments their legal power as citizens.
In our third and final post in this series, Cognitive cities: correlation and constant citizen interaction, we’ll discuss why.
DILIP VERMA, REGIONAL VP, INDIA // JANUARY 23, 2018
Before we delve too deeply into just how this can happen in cognitive cities, we first need to ground our discussion in a more solid definition of what a cognitive city really is. Wikipedia’s article on the subject provides a good starting point by defining a cognitive city as one which learns through the constant engagement of its citizens and advanced technologies. Together, these two features enable the cognitive city to become more efficient and resilient by enabling information exchange within the city.
Safe cities protect their residents. Smart cities collect data from their residents and technologies in order improve safety as well as the efficiency of public services. Cognitive cities can effectively use that data for improving the health, safety, well-being, and prosperity of its citizens, because the whole city is involved in the gathering, sharing, and use of the data. In cognitive cities, data now flows not only from the citizens to and from city management (as in smart cities), but also from citizen to citizen, and citizen to system. Not only does the whole city generate and consume information, but the whole city learns continuously and adapts as the city learns from it.
The cognition in a cognitive city happens not only in an administrative office or in a control center, but in and across the complex web of systems which comprise an urban area: social networks, local industries, transportation networks, utility systems, communications infrastructure and services, non-profit organizations, and political parties and movements. In a truly cognitive city, all members in this complex ecosystem are able to not only disperse data but also store memories, like how your brain records memories by connecting individual synapses.
With that adaptability comes the resiliency necessary to take on the list of the current urban challenges we cataloged in the first article of this series – rapid growth, limited space and resources, crime prevention – while at the same time surviving and managing rarer crises like earthquakes, floods, and other disasters.
So how do we build this cognitive city of the future?
The first step is to fully realize the potential of a smart city because cognitive cities build on their foundation. In practical terms, this includes constant citizen interaction via multiple touchpoints: in-person at government offices, over the phone with city personnel, and online through social media, city government websites, online chat, email, and mobile apps. Of those, perhaps social media is the technology which has the best potential to quickly foster the harvesting of information from citizens and the citizen to citizen interaction we mentioned above, and which is an indispensable part of a cognitive city. In addition, the rapidity of social media communications naturally lends itself to crowdsourced feedback.
Once that feedback comes in through social, other tech can step in. Predictive analytics, natural language processing, and cloud computing can not only help analyze citizen sentiment, cross reference citizens data with systems and sensors but to also proactively respond to behavioral patterns and deviations in them. For example, if there’s a sudden surge of tweets which favorably mention the hashtag of the local hockey team by city residents, the city administration may want to allocate more police to directing traffic on the streets around the rink during the next home game, in order to accommodate the throngs of fans who’ve come out to see their team. That city could then also put out advance warnings of traffic restrictions special parking changes on Twitter, tagging those tweets with the same hashtag, thus contributing relevant and useful information back to those same fans.
In that simple example, we see the essence of the smart city: seamlessly knitting together communication technology (Twitter) with how city personnel (the police) are deployed to make existing infrastructure (the roads around the rink) function more efficiently.
Once those tools are in place, the cognitive city can emerge. Cognitive cities learn from the data over time, and that learning will require more sophisticated tools for finding correlations between different data streams, trends within each of them, and instantly detecting anomalies in them and in the complex city system. Machine learning and the cloud will be key enablers in this transition from smart to cognitive.
A cognitive city would be able to learn from our above example, and generalize from the individual data points to valuable lessons which can then be used to make predictions of road traffic conditions and the number of police personnel required for any major sporting event, in any neighborhood, given historical data and real-time social media trending. This fundamentally shifts all urban operations away from situation management and towards real-time adaptation.
In the first post in this series, we saw how advanced technology alone is unable to make a city safe or smart. In our second installment, we discussed why: city residents are more than consumers, they’re citizens, and thus tech must incorporate and augment existing legal and civic mechanisms. In this final post, we demonstrated how cognitive cities use extensive data analytics to make the city smarter over time, and one of the ways this intelligence shows is in predictive, proactive adaptations (as opposed to reactive, rushed crisis management).
Even though smart cities are still maturing, cognitive cities will be the future of efficient, connected urban areas which employ technology and human intelligence to foster community, innovation, and prosperity. Both elements are necessary because tech alone can’t make a city safe, nor are city residents mere consumers of goods and services. Constant citizen engagement, ubiquitous data collection, and sophisticated analytics can combine to produce the best kind of cognitive city: the kind someone would actually want to live in.
IFTACH DRORI, DIGITAL MARKETING MANAGER, QOGNIFY // FEBRUARY 07, 2018
The ultimate goal of a smart city is to improve quality of life by using technology. In a recent article in Public Sector Executive, author Eddie Copeland talks about how governments should be focused on addressing urban challenges. Of course, safety and security are always urban challenges, because they are always primary objectives.
According to Copeland, what makes a city smart isn’t the technology, but what it can do to better the lives of those living and visiting. The real value of a smart city is what it enables: a place where people and business can thrive, prosper and enjoy life.
A defining trait of smart cities is their interconnectedness. Unfortunately, the more connectivity there is, the less privacy we’ve got. This situation has been referred to as the “cost of luxury”. Minimizing that cost becomes an imperative so that the benefits of a smart city outweigh the potential vulnerabilities.
The potential risk of cyber breaches extends beyond those associated with personal privacy. Think of the consequences of a hacked utility or smart app that controls traffic flow; those could easily result in business disruption and potential physical harm to citizens. For example, the ShotSpotter smart city application initiative in New York City is a “gunfire detection system that can detect different types of weaponry as it is being fired.” The nefarious hacking of a system like this could have dangerous results.
A critical element of cybersecurity is physical security – they are interdependent. You’ve got to protect physical access to cyber assets in order for them to be secure. That makes many of the elements of a safe city necessary for not only the actual operation of smart city applications but also for their cybersecurity.
Moreover, as concerns of personal privacy increase, city governments and smart city app vendors will need to be able to demonstrate that maximum cyber (and thus physical) protection is in place.
So much of the technology that enables smart city apps are from or used in safe city solutions: video, sensors, analytics, information management software, communication tools and more. Based on the two reasons discussed above – having a strategic smart city strategy and ensuring privacy and cybersecurity – it simply doesn’t make sense not to leverage safe city technology as a foundation for smart city applications.
As in any new field, buzz words will keep coming out. However, when you strip out all the hype, the main objective of any smart city initiative is the value it provides citizens and governments. And there simply is no value to what could be the smartest application without it being based on a foundation of personal and public safety and security.
Call them safe, smart or safe smart cities, at the end of the day it’s all about making technology work to improve aspects of our lives – and safety and security are always first and foremost.
HAGAR LEV, MARKETING DIRECTOR, QOGNIFY // FEBRUARY 14, 2018
IFTACH DRORI, DIGITAL MARKETING MANAGER, QOGNIFY // FEBRUARY 19, 2018
There are a few issues that will need to be addressed prior to seeing the mass adoption that has been predicted.
While the market is working on improving the battery life of a drone, currently, the average commercial drone has about a 25-minute flight time capacity. For certain applications, this is fine. Firefighters effectively use drones to gain a birds-eye view of the fire they’re battling to great benefit.
For applications such as guard duty or perimeter protection, which require 24/7 capacity, short battery life poses an issue.
Like with all technology that matures, the cost of drones is on a downward trend. The issue is not necessarily with the cost of a single drone, it’s the number of drones needed in order to effectively carry out many of its potential applications.
As mentioned above, a drone could be very effective in perimeter protection. In theory, you could overcome the battery-life issue by rotating a fleet of drones, but this would be expensive, especially if manned guards are still necessary.
Like a lot of technology, the origin of drones comes from the military with their invention of UAVs (unmanned aerial aircraft). Developed for battlefield use, as its name implies, a drone is an aircraft. As such, there are regulatory considerations when using drones for security or commercial purposes, including aviation; but also because of their capabilities, governance concerning consumer data protection and privacy also must be addressed.
While governments and industry are pushing forward to apply relevant regulations in order not to stifle this important technology, until that time, the market is seeking clarity prior to moving ahead with full-scale adoption.
Here’s how a civilian drone almost shut down Gatwick Airport last July:
As mentioned, firefighters are already using this technology to gain a perspective they otherwise wouldn’t have, making their efforts more effective. Drones are also being used detect movement and armed with video monitoring capabilities can transmit real-time footage of what is happening at the scene. Drones are here to stay.
The market is rapidly figuring out how to expand the use of drones for the security applications mentioned above. With global powerhouses like Amazon investing in drone technology for their own commercial use, the security market will certainly benefit from the attention and resources being put forth by other industries. In the meantime, security vendors and users of the technology are working together to leverage drones within the existing parameters.
ANDREW SELDON, GUEST BLOGGER // MARCH 05, 2018
You can’t step out of the door these days without hearing about Artificial Intelligence (AI) and how companies are using it to change the world of security. this is a short article, we will simply use AI as a holdall to refer to Artificial Intelligence, Deep Learning, and Machine Learning, although these are really distinct, yet connected disciplines.
The problem with all this talk is that it’s easy to be caught up in the hype without understanding what AI actually is or the benefits it is supposed to bring. I would suggest we have yet to see real AI at work in the security industry.
From traditional line crossing to the far more complex facial recognition systems out there, as well as the ability to differentiate between humans and animals or inanimate objects, there are some impressive products on the market (and a few not quite as impressive).
But is this AI? The short answer is ‘sort of yes’, and no. The advances in software that led to these features are impressive, but there is much more to come.
Real AI is built on masses of data that is analyzed and categorized (or ‘sliced and diced’ in old Business Intelligence parlance). More importantly, real AI learns from the data it analyses and can make predictions and inferences based on that analysis.
Video analytics today offers the ability to detect when a human is approaching a restricted area and raise an alert. Some vendors offer solutions that can tell the difference between animals and a human, and a dog, for example, won’t generate an alarm. But what if there is heavy rainfall or mist so that even a thermal camera can’t generate images the system can clearly identify as human?
This is where AI comes into the picture, so to speak. As a simple example: by using the historic data as a learning experience, the system would be able to infer that although it can’t clearly identify the object moving into a restricted area as human, it seems to move on two legs and therefore can be classified as human and this warrants raising the alarm.
Similarly, take facial recognition in a retail environment. It’s easy (assuming you have the right equipment) to build up a database of faces and to denote certain faces as VIP guests and others as unwanted guests. What an AI system will do is take that facial database, combine it with other information from the retail environment, and provide the security team with actionable information.
Another simple example. The retail system will alert security that face ‘X’ has entered the premises and that on the past six occasions X was around, someone had their handbag stolen and X left the premises within 10 minutes of the theft. This is not to say X is a thief or that there is video or any other evidence of wrongdoing, but it says there is a correlation between face X and a security event. With this information, perhaps a security operative should be sent to hang around near X, or perhaps the control room should keep an eye on him/her through virtual surveillance.
AI goes far beyond security, of course. Businesses can use the information for operational, human resources, marketing, and other purposes as well, with direct results observable on the bottom line.
The key here is data. Not simply video surveillance data, but any and all data, whether structured or unstructured and the ability to gain access to it easily in a format the system can read and analyze, with the ultimate goal of making connections between seemingly unrelated events.
A real AI system will use multiple sources of data to predict probable events or to infer outcomes based on the patterns it detects in data. It’s far more than simply crossing a line or recognizing a face, it’s what you do with information that defines intelligence – artificial or not.