The use of artificial intelligence in security systems provides more flexibility, especially with new cyber threats always emerging. Namely, machine learning has garnered much attention for its involvement and improvement of security systems.
Most people use the term “artificial intelligence” loosely these days, but it traditionally refers to the theory and development of computer systems that may perform human tasks. Machine learning is a type of AI that allows a computer to learn, grow, and change when presented with new data.
The evolution of AI can be best described in three stages. First is the basic expert system. If we used this system to help distinguish between a dog and a cat, for example, it would use a single feature such as number of teeth to make the decision. Second is the probability-based system, which evaluates different factors (ex. number of teeth, weight, size) to determine the probability (expressed as a percentage) of the object being a cat or dog. Lastly is deep learning, which uses seemingly endless amounts of labeled samples to differentiate between cats and dogs.
If we applied these to antivirus systems, you could understand how a basic expert system would be weak and need constant updating for new threats. The probability-based system would be a bit stronger, however, only so many features would prove relevant resulting in disregarded data. Deep learning seems the most promising, and a startup called Deep Instinct is looking to develop this approach for cyber security.
Within the Deep Instinct laboratory, the deep learning system is trained on all the known samples of malware, which takes about a day to complete. The process requires heavy-duty graphical processing units to analyze the data, and the end result is a trained system about a gigabyte in size. It is then reduced to about 20 megabytes and can be installed on any endpoint device (including mobile). It works to analyze any incoming threats within a few milliseconds to keep your devices safe.
To keep the system up -to-date, new malware samples are added every few months, and updates are automatically sent to the end point devices. But even if the system is not updated for months, the small brains within the end point devices remain vigilant and can detect new files. The success rate is promising and deep learning systems will likely gain more popularity over time.
While deep learning systems are great for detecting threats, they are not the best for explaining how they did it. Eureqa is a proprietary AI engine from Nutonian whose main job is to find out why things happen. It has proven very valuable for researchers and journal publications, but it also plays a role in cyber security by helping to determine the anatomy of a cyber attack.
Still, cyber security can be a tricky mess. Constant updates are necessary thanks to appearance of new threats and attacks daily. Even though you are employing security systems to protect your data, there are still vulnerabilities between updates. And during that time, hackers can use the security software to test their attacks until something breaks through, leaving numerous customers at risk.
Tailoring your cyber security approaches can help to combat this. For example, Masergy Communications is a managed networking company which uses a combination of both local and global factors to predict and prevent cyber security issues or attacks. The unique local indicators help to improve accuracy.
Acuity Solutions offers the BluVector appliance which uses machine learning for cyber threats, and also uses a local and global approach. The pre-trained engine learns what a benign code looks like, receives updates based on global data, but also engages in new learning based on the individual customer. While the global data is shared, the customer-specific data is not, creating a more unique and secure experience.
As discussed, artificial intelligence and machine learning can greatly benefit different aspects of cyber security. What are your predictions on the future of AI in security solutions? Share with us on Facebook, Google+, Twitter, LinkedIn, and Pinterest!
For a wide range of security cameras and surveillance equipment, please visit SecurityCamExpert.com. To speak with a representative or request a site survey, please call 1-888-203-6294.
Despite how it may be depicted on television or movie screens, reviewing surveillance footage to find evidence is a lengthy process. Sifting through hours, sometimes days, of footage is costly, time consuming, and, when done by humans, is rarely effective. Luckily, advanced technologies can save the day.
Originally, CCTV video footage was used to monitor retail stores or businesses to prevent theft, damage, or employee misconduct, and provide evidence if something were to happen. If nothing occurred, the storage would be overwritten because space was limited and the footage proved useless.
These days, storage capacity has increased and new data processing techniques make this footage extremely useful. The accessibility of recording devices with advanced features is changing the value of videos. And thanks to machine learning and video analytics, surveillance footage can be sorted and evaluated in a timely manner.
Rather than wasting time and resources having humans evaluate footage, video analytics can take care of it. Video analytics is the process of extracting pertinent information from video footage. It basically works like image analytics, but goes a step further.
Image analytics can look at a still image to find patterns, anomalies, and identify faces. Video analytics can do the same, plus measure and track behaviors. Because of this, video analytics has a promising future within different industries.
The Use Of Video Analytics
Because this technology is great for identification, behavior analysis, and situational awareness, various businesses and industries can benefit greatly. Video analytics allows business owners to evaluate who visits their stores, identify peak hours, analyze customer behavior, and more. This gives businesses insight into how they can improve customer service and which deals or displays attract more customers. These types of insights can also benefit the marketing departments, as they can better understand customer demographic and tailor ads to those groups.
Video analytics can even be applied for security and law enforcement. Since body cameras for police are becoming widely adopted, these produce lots of video footage. Video analytics could make the recordings useful by adding rich tagging and indexing, making it easier to search through footage. Parsing through certain time periods and identifying persons with specific characteristics can help to develop leads and even recognize and predict different patterns.
For airports, stadiums and other major event and transportation venues, video analytics can evaluate footage and help to relieve congestion and lines. By monitoring these venues, more workers can be deployed to decrease wait times and improve customer service.
Video-Based Predictive Analytics
While still in the early stages, a new algorithm, as reported by MIT, allows a computer to predict human actions and interactions based on behaviors seconds before the action. The outlook for this algorithm is promising. As it develops, computers could eventually be taught to predict when a crime or injury may take place.
And as artificial intelligence (AI) and robotics become more feasible in our everyday lives, this type of machine learning and predictive analytics will be necessary for robots to interact with humans naturally.
An excellent example of these video analytics in action is Veenome for marketing. Its YouTube analytics tool helps advertisers choose which videos are better suited for them to display ads. Another example is Prozone for sports analytics. By analyzing video footage of the field, players’ stats can be recorded and more effective plays can be planned and executed.
These video-based predictive analytics can also help with decision-making in industries such as aviation, air traffic control, ship navigation, power plant operation, and emergency services. Accidents and crimes can be prevented, thus, potentially saving lives.
Video Gray Area
Of course, as it goes with all surveillance, privacy concerns arise. Currently, analytics where data collection does not require consent is still a gray area. Until laws are in place to protect the public as well as businesses, companies should consider employing video analytics ethically, with respect and privacy to the data and its consumers alike.
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