Machine Learning has been becoming a huge part of our lives. No matter what field we are in, AI goes along. Machine Learning allows computers to learn without being explicitly programmed. Let us see it another way: Machine learning teaches computers to do what people do: learn by experience. Machine learning is a domain within the broader field of AI.
Do you know how police track down the criminal by watching the footage of CCTV? And, what happens when governments can track huge numbers of people using CCTV? How does police tail you around the city just by digital means? A mischievous group of teenagers that are disturbing people around them, how do they get recognized? It's all Artificial Intelligence.
How do you get to know when somebody is peeping through the whole of your doors? Those security cameras on your doorstep work on AI. These cameras are digital eyes that watch over us, watch out for us, and it doesn't matter where you are and what you do. This ensures public safety, helps police and first responders more easily spot crimes and accidents, and has a range of scientific and industrial applications. So what does AI change? Basically, AI gives surveillance cameras digital brains to match their eyes, letting them analyze live video with no humans necessary. Do you want to know how? Let's get started!
Whenever something new comes up, we first look at its pros, and slowly-slowly its cons start appearing. "The Worldwide Web" had sparked the technological part of our lives, but soon it felt like a threat once the Dark Web began to eat the society. AI has the required potential to support several national and international security initiatives, from cybersecurity to logistics and counter-terrorism. You do not even know how much of your personal data is available to the public. This lack of knowledge leads to cybercrimes. The threats that emerge due to the large volume of our security data exposed online must get controlled. It is not feasible to manage this volume of information by a team of humans. Here, we feel the urgent need for machine learning and artificial intelligence. Machine Learning can recognize patterns and predict threats in massive data sets, all at machine speed. Cyber teams can rapidly detect threats and isolate situations that need deeper human analysis.
1.Using various mathematical techniques across huge datasets, the algorithms essentially build models of behaviors and use those models as a basis for making future predictions based on new input data.
2. Machine Learning helps businesses analyze threats and respond to attacks and security incidents. It can automate more menial tasks previously carried out by stretched and sometimes under-skilled security teams.
3. AI in security is a fast-growing trend. Google uses machine learning to analyze threats against mobile endpoints running on Android. It identifies and removes malware from infected handsets. Did you know that Amazon has launched Macie, a service that uses machine learning to uncover, sort, and classify data stored on the S3 cloud storage service?
4. Most companies use AI to form a purely “Signature-based” system. This system detects malware and tries to interpret actions and events, and learns from a variety of sources what is safe and what is not
If we talk about the applications of Machine Learning, then there are mainly five of them-
1. To detect malicious activities and stop attacks- Machine Learning algorithms help various business houses put a full stop to the web threats. AI can recognize these activities and their origin faster than any team of people. The machine learning algorithms can spot the attack within seconds. David Palmer, the director of technology at UK-based start-up Dark trace, mentioned the data exfiltration attack on a casino. Their algorithms detected a bulk of such attacks and helped them to get rid of them. Dark trace also helped the Wannacry security crisis last summer. Thankfully, the threat was mitigated without causing any damage to that organization.
2. To analyse mobile endpoints- Machine Learning always goes mainstream on mobile devices. As I told you, Google has been using machine learning to analyse threats against mobile endpoints, while enterprise is seeing an opportunity to protect the growing number of bring-your-own and choose-your-own mobile devices. Recently, MobileIron and Zimperium thought to collaborate. The motive is to help enterprises adopt mobile anti-malware solutions incorporating machine learning. The integration consists of Zimperium's machine learning-based threat detection with MobileIron’s security and compliance engine. The collaboration was made to sell the combined solution, which would address challenges like detecting device, network, and application threats and immediately take automated actions to protect data.
3. To speed up Human Analysis- Machine Learning has proven to be teaching humans almost all the aspects of the job. These aspects include detecting malicious attacks, analysing the network, endpoint protection, and vulnerability assessment.
4. To automate repetitive security tasks- The best benefit of machine learning is that it can automate repetitive security tasks. It enables staff to focus on more important work. Machine Learning aims to remove the need for humans to do repetitive, low-value decision-making activities, like triaging threat intelligence. Machines can handle repetitive work. It does tactical firefighting like interrupting ransomware so that humans can free up time to deal with strategic issues.
5. To close zero-day vulnerabilities- AI helps close vulnerabilities, particularly zero-day threats and others that target largely unsecured IoT devices.
If we talk about the pros of machine learning in security, then there are only pros as it-
• Finds the threat- AI detects the threats by constantly monitoring the behaviour of the network for anomalies. All the engines process massive amounts of data in near real-time to discover critical incidents. All these modern techniques also detect insider threats, unknown malware, and policy violations.
• Allows safe browsing- The world wide web possesses many threats to business houses and individuals. AI techniques can predict “bad neighbourhoods” online to help prevent people from connecting to malicious websites. Machine learning analyses Internet activity and automatically identifies attack infrastructures staged for current and emergent threats.
• Protects Data in the cloud- It can safeguard productivity by analysing suspicious cloud app login activity, detecting location-based anomalies, and conducting IP reputation analysis to identify threats and risks in cloud apps and platforms.
• Detects malware in encrypted traffic- These algorithms detect malware in encrypted traffic by analysing data elements in common network telemetry. Rather than decrypting, machine learning algorithms pinpoint malicious patterns to find threats hidden with encryption.
GTS works on six elements in generating Training Data for Security Cameras.
1. Annotation for crowd detection
2. Night Vision Thermal Image Annotation
3. 3D Cuboid Annotation for Traffic Motion
4. Point of Interest for Human Monitoring
5. Person Annotation for Theft Detection
6. Landmark Annotation for Face Detection
GTS offers you a wide variety of services in order to make your life more secure than ever. All our AI Training Datasets are required to create such high-tech video equipment and integrate them into the surveillance system. Our team provides you Computer Vision-based visual perception model with an advanced level of accuracy for precise object detection and faces recognition.
Let us make a safe world for you. Allow our professionals to provide you with a better place of living with no threats. Security Cameras require multiple AI Training Datasets to function properly. Let Global Technology Solutions help you to live a better life and make this society a better place for you and your family.