Upgrading your analog security cameras to IP security cameras has plenty of benefits, including improved image quality and advanced features. Most IP surveillance systems can make use of existing network infrastructure that is in good condition, decreasing costs for installation. Whether you are looking to upgrade because your analog system is reaching end-of-life for support or because your needs have changed, an IP surveillance system is a smart decision.
Now, the actual task of transitioning from analog to IP security cameras should not be taken lightly. You want to be sure that you take all things into consideration to ensure that you choose the right IP video surveillance system and that it performs sufficiently. Here are a few aspects you should not overlook:
Goals & Challenges
If you are looking to achieve ROI, you must fully understand how your IP security system will be used. Operational goals and potential challenges should be determined beforehand. Think about what types of cameras and how much resolution you need, as well as how long the footage needs to be stored and which areas need coverage. Proper planning is crucial to the success of your security system.
No one wants to pay an arm and a leg for a mediocre surveillance system. If done correctly, you don’t need to. By defining a security budget, you can find the right cameras and video management software (VMS) to fulfill your needs and achieve your goals.
As much as a quick transition sounds ideal, it is not always feasible. Understand that a proper transition will take some time, and it may be in your best interest to plan a phased migration. This will help to accommodate budget availability and operational disruptions. Prioritize which area needs immediate attention and begin there.
Going from analog to IP improves video quality, but also requires more storage. Advanced VMS can help to effectively optimize your network resources and bandwidth consumption, thus decreasing networking and storage costs over time.
A new IP video system may need additional staffing, so you should think about this and how you will train the new and existing staff. This will impact both overall costs and ROI of your system, and may affect cameras and software selection. For example, casinos require live monitoring around the clock while parking lot surveillance may use video analytics to alert security personnel of incidents or events that need attention.
Numerous third-party integrations can help to increase the efficiency of your system as well as manage costs. While most current systems have an IP-based interface for integration, leading suppliers also have a wide range of integrations which are tested and ready to apply. These can offer functionality, automation, and other enhancements to solve project needs.
Cybersecurity is of utmost importance, especially these days. If not addressed properly, going from analog to IP opens up your system, and any indirectly connected networks, to endless vulnerabilities. Be sure to discuss your specific network safeguards, policies, and strategies with your installer. Also, enlist a new IP security system that provides the appropriate cybersecurity architecture, software, devices, and policies.
Pay attention to licensing requirements and Software Upgrade Plans (SUPs) or Service Level Agreements (SLAs) that come with most VMS systems. These cover everything from higher tiers of support to future upgrades. For example, third-party cameras may require a license for each IP address, and these licensing requirements can add additional costs.
These include extreme heat or cold, humidity, corrosion, and high dust levels, along with ambient light levels, existing power sources, and network infrastructure. All of these can impact which security cameras and VMS equipment are necessary for you.
Because your security system should be operational and accessible at all times, it is important plan provisions for redundancy and back up for primary resources in case they fail. For most systems, simple RAID-5 or -6 redundancy in storage is sufficient. However, you should also consider budgeting for “failover” recorders and other server hardware, and have spare cameras on hand in case of failure.
It is only a matter of time until IP surveillance is the norm and analog security cameras are a thing of the past. But when the day comes, it is ever important to understand your security needs and what you expect from your IP surveillance system. Even a small mistake or misstep along the way can compromise your system.
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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.