AI For Advanced Security And Surveillance

In this article, we will discuss how the use of AI for advanced security and surveillance help us detect threats and take effective measures.

In 2016, three million Yahoo accounts were hacked, which is considered to be one of the biggest breaches of all time. As technology is evolving at a rapid pace, the nature of the security attacks are also evolving and newer threats are being fired at organizations worldwide. In this context, traditional security surveillance systems are not efficient and may not be able to act quickly. By integrating AI into the security systems, threats can be detected more effectively and take actions to prevent it. AI-based security solutions can provide assistance to a wide range of sector from cyber-security to military operations. Let’s take a look at how AI can improve the security and surveillance sector.

How AI Can Improve Cyber Security?

For some years now, we’ve been reading case after case of cybersecurity breaches. Just last year, security researchers raised alerts about Russian hackers to gain illicit access to the United States’ utility companies. This is merely one of the many such cyber-security breaches and everyone including the government agencies, corporations and individuals are vulnerable to such attacks. The situation is getting worse as the hackers are devising new ways to threaten internet security. In fact, it’s estimated that by 2020, global spending on cyber security will reach  $170 billion.

As cyber warfare is becoming more sophisticated, fast-paced and dangerous, it is important that the security agencies are well equipped. The human mind will not be effective as it cannot handle the scale or speed required to respond to cyber operations and this is where AI comes in. Cyber experts believe that the cyber operations, with machine-on-machine engagements, will become the norm, especially to counter low-order or routine threats. Relevant tools will need to be put in place to detect and prevent the use of encryption to mask malicious activity, as is usually the case with malware. It is often transferred within encrypted web traffic, and the sensitive data sent through cloud systems. A report published by Cisco proposes that over the course of time AI will be able to detect unusual patterns in encrypted web traffic and Internet of things (IoT) environments. With an automated understanding of the intended state of an application and leveraging the pattern detection, AI/ML systems can detect cyber attacks and shrink the attack surface at scale.

Security Data Analysis With AI

A problem faced by almost every organization is the analysis of security data. Security data can be the footages taken from CCTV camera, log records of servers, recorded information of sensors etc. Analyzing these data for detecting breaches or anomalies would be a tedious work as the volume of data is usually in terra bytes. Moreover, the analysis is highly prone to human errors and the processing time is very high. AI applications can speed up this data interpretation process, and thereby save a lot of time and effort which can be then be used for other higher level tasks.

The United States military uses an application called Project Maven which use algorithms to more rapidly interpret imagery from drone surveillance feeds. Integration of AI into the security systems help to interpret information more quickly and accurately thereby leading to better and more timely decisions.

Real-time Video Surveillance

Computer Vision is invaluable for organizations that are tasked with ensuring safety and security in public places. The system is capable of gathering and detecting visual data from cameras set in public areas and helping law enforcement officials identify suspects and attain a greater level of control. A computer vision enabled software analyzes video streams on a real-time basis and recognizes the objects and people around. With an ever-growing need for surveillance, computer vision can help create a nationwide surveillance network with an enhanced facial recognition that can monitor persons of interest through public cameras, as well as through their smart devices at home.

In addition to helping law enforcement, computer vision can be used by companies and government institutions to provide enhanced video surveillance to keep track of individuals’ entry and exit. A similar system can be scaled to incorporate facial recognition feature as well. This would help you to conveniently check for people who enter a building along with the time.

Countering Illicit Financing With AI

Illicit money flowing through the global financial system provides financial support for terrorism and the proliferation of WMDs. After 9/11, U.S officials have expanded the anti-money laundering and counter-terrorist financing tools. However, despite these efforts, it is estimated that only one percent of these funds were seized by law enforcement.

By implementing AI in the financial system, constant attention can be given to these threats, even when it is not prioritized by financial institutions. AI’s anomaly detection and pattern recognition features can help a system learn from the unstructured data collected by financial institutions. One of the regulatory technology company who is integrating AI tools found a correlation between users who had changed their browser language and a type of fraud. This analysis led to the discovery of a metric which is not usually used by financial investigators which expanded the definition of usable data. The information sorting process can be made simple using better pattern recognition tools which can reduce false positives that would otherwise result in alerts. For example, false positives can be reduced in “high-risk” jurisdictions by replacing vague geographic input with a more effective red flag. Time and manpower will be saved due to fewer alerts.

Onsite Applications

Situational Awareness – Small robotic sensors could be used to collect information, and AI-enabled sensors and processing could help make better sense of that information. Deep neural networks already are being used for image classification for drone video feeds as part of the Defense Department’s Project Maven, in order to help humans process the large volumes of data being collected. While current AI methods lack the ability to translate this into an understanding of the broader context, AI systems could be used to fuse data from multiple intelligence sources and cue humans to items of interest. AI systems also could be used to generate tailored spoofing attacks to counter such sensors and processors.

Electromagnetic Spectrum Dominance –  AI systems built on top of Generative Adversarial Networks(GANs) could be used to generate new ways of jamming and communications by playing itself. For example, one AI system sends signals through a contested electromagnetic environment and another can try to jam the signal. This way both the systems learn and improve its performance. Currently, DARPA is using machine learning to aid in radio spectrum allocation; the same concept can be used to jam signals and create jam-resistant signals.

Drones For Surveillance – Drones are getting increasingly popular in the military sector. This technology has been developing in a huge way and now its finding application in unmanned aerial vehicles. Drones carry out all kinds of works right from inspecting a terrain to flying an unmanned aerial vehicle that can channel remote communication, both video, and audio, to ground troops and to military bases. It can also conduct reconnaissance in war zones and help with peacekeeping operations and border surveillance. 

As the current security systems are not able to withstand the modern attacking strategies, it is necessary to incorporate more AI based security system for a quicker and more effective response method. AI has the potential to significantly change the security and surveillance systems, more sophisticated security systems are being developed for the future.