Contact Us
×

Tag: machine learning training

How Machine Learning is Changing Identity Theft Detection?

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Debilitating data breaches and identity theft scenarios have left several high-profile firms across the globe scrambling to recover losses. In 2018 alone, in the US, over $1.48 billion worth of losses occurred, after 1.4 million fraud reports1. Of these reports, identity theft was a significant defining factor. Businesses and corporates alike are turning to machine learning and Artificial Intelligence (AI) in general for help. Current employees are also being upskilled for an artificial intelligence career through machine learning courses in order to prep for the future of machine learning.

Machine learning has already permeated everyday lives, from online recommendations on your favorite streaming site to self-drive cars that have awed the masses. When it comes to identity theft detection, machine learning has so much potential– especially since there are larger players and higher factors at stake.

Here are some ways in which AI and machine learning are being leveraged to detect, reduce and prevent identity theft:

Authentication Tests

With machine learning, identity documents including the likes of passports, drivers’ licenses, and PAN cards are scanned and cross-verified with an unseen database in real-time. An additional set of authentication tests can usurp theft to some extent– the use of biometrics and facial recognition being some of the more used ML-based tests. Other examples of authentication tests include microprint tests, OCR-barcode-magnetic strip cross-verification, and paper-and-ink validation2.

Real-Time Decision Making

Machine learning training has the power to operationalize and automate the process of data analytics, especially tasks that are mundane or prone to human error. Beyond speeding up the process of identity theft detection, machine learning enables real-time decision making to stop theft in its tracks or sound an alert in case of a potential threat. This is a boon for businesses both large and small who cannot afford to waste valuable human resources on mundane tasks. By detecting identity theft at speeds hitherto unmatched, machine learning allows analysts to make spot decisions before any damage is done.

Pattern Identification

An added benefit of using machine learning to revolutionize identity theft detection is pattern recognition. Since any machine learning algorithm is wired to a database with tonnes of data, these algorithms can scan through all the information available over the years to predict future threats and identify the source and patterns so that preventive measures can be taken in advance. This is beneficial in that it creates links between individual theft cases, allowing analysts to better assess what the best plan of action is in response.

Dataset Scaling

The more data that’s collected, the better machine learning algorithms are trained for a variety of situations. Unlike many other scenarios where lots of data mean more complexity, a wider database allows machine learning algorithms to be scaled and adapted as required. It also allows them to grow more accurate with every addition, make comparisons and identify genuine and fraud transactions in an instant– a true step up from the days of human involvement. However, a caveat– in training stages, it is crucial that analysts be monitoring the process because if the machine goes over an undetected fraud without flagging it, chances are it’ll learn to ignore that type of fraud in the future, opening up a big sinkhole in the system.

The final word

Machine learning is revolutionary in preventing billions of dollars being lost in fraud, theft and data recovery. Firms are increasingly allocating a huge chunk of their budget towards sound ML-based security systems– a testament to just how revolutionary the technology is in identity theft detection.

Career in Machine Learning – Check Job Profiles, Top Courses and Colleges, Fee Structure

Posted on by Imarticus Learning
Reading Time: 3 minutes

Professionals in the sphere that is Machine Learning are very important and sought after in the Information Technology Industries across the globe. Through Machine Learning, human work has reduced significantly and boosted efficiency.

Machine learning has also helped reduce errors and a large number of companies have begun automating their systems. Business systems are using machine learning training to reduce costs and improve productivity along with performance as a whole.

Data scientistThere are a variety of career parts to pursue in the sphere that is Machine Learning and the positions offered are very rewarding.

Career Paths for Machine Learning Aspirants

  1. Software Engineer: Software Engineering aspirants need to know the nitty-gritty of code writing fluently as candidates will be needed to create code that would support the creation of specific algorithms. Using principles from engineering with computer science within mathematics from an engineering degree, designing and developing software is what computer software engineers are responsible for.The candidate for these jobs is required to have skills in listening to and understanding clientele requirements on a more detailed level. Along with this, they are also required to create a system in accordance with clientele parameters.
  2. Software developer: The job of a person in software development entails creating flow charts to assist coders in their work. Software developers are known to be the true brains behind any computer program. They are responsible for creating models, illustrative representations, strategic groundwork, and plotting out the required working of a complete system. They are required to test machinery and look at the working of each component.
  3. HTML Designer: These designers are involved in the creation of software for various social media platforms, big online stores, and banks as well. In banks the designs they put into effect help in increasing the number along with the efficiency of bank transactions that are managed and done online including those done electronically.
  4. Data Scientists: Involved in the procedure of analysis, they are responsible for utilizing data to find out vital information using inspecting and modeling processes.
  5. Computational Linguistics: The job of a computer linguist holds candidates responsible for helping computers in understanding spoken languages and to constantly improve currently existing systems.
  6. National Language Processing Scientist: The people in this position are needed to do the designing and development of machines and also applications that can learn patterns and translate various words imputed by a speaker to various other languages.

The demand for professionals in the area that is machine learning is growing every day. Various other career paths in this area include Data Analyst, Cloud Architect, Intelligence Developer for a Business and also Data Architecture.

Best Courses and Facilities for Training within India

The top 3 machine language courses available in India are listed below. Students,

professionals in analytics and also data scientists pick the finest programs to increase their skills and improve themselves.

  1. PGP in Machine Learning & Artificial Intelligence offered by IIIT-B
  2. Offered by IIT in Hyderabad is Fundamentals of ML
  3. PGP in Artificial Intelligence along with Machine Learning

Colleges Offering Machine Learning Courses

The various colleges offering machine learning courses in India are listed as follows:

  1. Indian Institute of Technology, Hyderabad
  2. DY Patil International University
  3. University of Petroleum and Energy Studies
  4. Jain University, Bangalore
  5. Sharda University
  6. Indraprastha Institute of Information Technology
  7. Vellore Institute of Technology
  8. SRM Institute of Science and Technology
  9. Dehradun Institute of Technology University
  10. SVVV (Shri Vaishnav Vidyapeeth Vishwavidyalaya)

Job Opportunities in The Field of Artificial Intelligence in This Pandemic Time!

Posted on by Imarticus Learning
Reading Time: 3 minutes

To become an Artificial Intelligence (AI) professional, you need to have practical problem-solving skills, logic, communication, and analytical skills. AI is made to create computer programs that can achieve goals and solve a problem better than humans. With lesser mistakes and emotions to hinder the work, AI gives better and jan efficient output.

The scope in AI is vast. You can get into robotics, gameplay, language detection, machine learning, computer vision, speech recognition, and many more.

Some of the factors that characterize a great career in AI are as follows:

  • Robotics
  • Use of sophisticated computer software
  • Automation

Math, technology, engineering, and logic are some of the specific fields that individuals have to specialize in if they are considering a job in this field.

Along with this, learning science including physics and computer studies is beneficial.  Considering the computational approach to AI, knowing the technical, as well as physiological knowledge of the system, is immensely helpful. Knowledge of primary machine language is a must. There are many other courses that you can do to get into the world of AI like, Machine learning.

Data Science Online CourseMany institutes like IIT provide machine learning courses, there are other institutes that provide these courses online and then there are certification courses that you can take up in private institutions.

Some of the career opportunities in AI

  • Robotic Scientist

Robots are gradually taking over the industrial worlds. There is lesser workforce and more robots. To help create such robots that can solve problems as a human would, we need engineers or programmers. For a career in Artificial Intelligence field, a master’s in robotics engineering and having a license from the state can be of help.

  • Software Engineer

In every phone that is there in the market, there is an option for face recognition or finger print recognition. Many companies, including big businesses, security companies, casinos, etc. have face recognition and fingerprint recognition to understand the people who use their services. Hence being a software engineer is one of the opportunities here.

  • Game Programmer

To keep the players challenged and highly anticipated, every gaming company requires candidates that are well known with the basics of AI and can design games that can keep the players engaged and interested.

  • Search Engine Manager

Many big companies, like Google, pay a massive amount to candidates with an AI degree to manage their massive search engines. Many may search for various things on Google, but Google search is able to predict the search even when there are spelling mistakes or grammatical errors. This is done with the help of knowledge and the study of artificial intelligence.

  • Government Sector

There are jobs not just in the Private sector, but there is an intense need for candidates with a degree in AI in the government sector too. The pay is high, and along with that, the amenities provided are even better.

Conclusion

The scope of artificial intelligence is vast. Having a master’s degree or a doctorate is the best if you are looking for a long term job in the field of AI.

The demand for people with knowledge of AI is strong. Companies like Google, Apple, etc. are always on the lookout for candidates who can take the world of AI to another level. The choices are plenty, and the income from working in such a field is high.

‘Eve’, a robot created by the scientist at the University of Manchester, Cambridge, discovered that a common ingredient found in toothpaste is capable of curing malaria. This event, itself, can show how much this field has grown, and the job possibilities are endless.

The Role of AI in Minimising Physical Contact in Public Spaces!

Posted on by Imarticus Learning
Reading Time: 3 minutes

The novel coronavirus pandemic has forced a majority of countries around the world to enforce lockdowns. Although met with initial resistance, a large chunk of the global population has stuck to social distancing and shelter-in-place norms, allowing the curve to be flattened.

As countries now begin to emerge out of lockdowns in phases, the focus will turn to maintain high standards of sanitation and hygiene. This is to avoid undoing the work that has been done over the past few months as well as set new norms for effective mitigation and disease controls. Amongst these, processes to minimize the frequent touching of common surfaces in public spaces will certainly feature.

So far, however, all efforts have been wholly dependent on manual efforts and individual dedication to social distancing and mitigation. AI can be pivotal in the efforts to curb the touching of surfaces in public areas without banking on individuals entirely.

Here’s how:

  • Contactless Access Systems

Tech titans are currently exploring the use of technologies for facial recognition to monitor the social distance between staff members. These can also be taken one step further to be combined with thermal scanning; when paired, this system can regulate who enters and exits the front doors in just a few seconds.

Machine Learning

This system also negates the need for touch-and-go biometric scanners or ID scanners which often become a collection point for employee throughout the day. Artificial Intelligence can be used to virtually cordon off some parts of the office as well as maintain control over how many times a person touches their face in a day (which is one of the quickest methods of COVID19 transmission).

  • Leveraging Voice Commands

Voice functionality has penetrated many aspects of human lives– and it’s only set to increase. Voice commands can be used to operate systems in public spaces such as bathrooms, elevators, entryways and cubicles to minimize the risk of contact. It can also be implemented at the water cooler, in the printing room and in office pantries, which are often places that see the highest footfall in large-scale organisations. Voice functionality can be implemented by integrated voice assistants and or smartphone apps. Aside from voice commands, gestures can also be used to minimizing the frequency of touching high-risk surfaces such as flushes, taps, door handles and elevator buttons.

  • Smart Handles and Locks

Doorknobs and handles are high-priority areas for sanitation teams given that we subconsciously handle them every day. AI can be implemented to reduce the need to physically touch handles to open doors. Technology can be used to kick into motion self-locking or gesture-controlled mechanisms. In a case where physical touch is absolutely required, AI can also be used to trigger the dispensing of antibacterial coatings or single-use sanitary sleeves. Newer inventions that use these technologies are able to be retrofitted onto existing doorknobs and handles, making them a quick fix to the sanitation problem in this aspect.

  • Location and Distance Tracking

Although some industries are slowly opening up, others have seen an influx of workers considered essential. However, that doesn’t reduce the need for strict social distancing measures, which is where AI comes into the picture. Artificial Intelligence can be used to account for the location of every employee in the facility and alert them if they have crossed social distancing boundaries.

Additionally, AI can also be used to demarcate spaces in queues and cubicles to maintain distance between employees. This system can be implemented through smartphone apps or wearable devices such as smartwatches.

Conclusion

Even after the pandemic loosens its hold, social distancing is slated to become the new norm. Businesses looking to leverage AI to maintain these rules without manual labour can consider upskilling their IT team through an artificial intelligence course or Machine learning training to ensure they’re achieving their potential.

Use of Machine Learning in Social Cause!

Posted on by Imarticus Learning
Reading Time: < 1 minute

Watch Vinay Borhade, Founder and Director of AIQuest Solutions(LLP), Former Sr. Manager-Bank of America discusses how Machine Learning is used in Social Causes today. He goes into detail and shares some examples of its uses in water crisis, climatology, renewable energy, crisis management, and health nutrition.

Imarticus Learning is India’s leading professional education institute, offering certified industry-endorsed training in Financial Services, Investment Banking, Business Analysis, IT, Business Analytics & Wealth Management.

To know more about the course, please visit here – https://bit.ly/2N6yO0d

Website: https://imarticus.org/

Facebook: https://bit.ly/2y6UjKW

Twitter: https://bit.ly/2J11llx

LinkedIn: https://bit.ly/2xwSoPM

Pattern Recognition – How is It Different from Machine Learning?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Pattern Recognition and Machine Learning are closely related terms in the field of data analysis. The former is a part of Machine Learning and is used as a technique to detect patterns and irregularities in a pool of data.

There is a very thin line between them which will be covered in the following sections. And a simple way to distinguish between them is to understand their individual functions and qualities.

Pattern Recognition vs Machine Learning

Let’s first understand what Machine Learning is? It is basically a concept that allows systems to learn and adapt in a particular way by means of data.

Take the example of how a user behaves with an automatic food recipe machine. If the appliance uses Machine Learning to understand user behavior in a better way, it would ideally take insights from all the past user actions and adapt itself for better functioning.

The primary (and perhaps the only) goal of Machine Learning is to make good guesses. In consumer tech, this is used to automate actions in an application as suggested in the example above. However, Machine Learning has applications across industries (as noted below). This is why there is a growing demand for professionals with relevant skills, which in turn, has resulted in a boom in Machine Learning courses.

What is Pattern Recognition?

It can be seen as an application or subset of Machine Learning (ML). It is basically an element that detects patterns in an ML algorithm. Unlike ML, it uses previous information to refine its findings.

Let’s go back to the appliance example given above. How would the process change if the appliance was already fed with some patterns that the user is assumed to take? This can have a considerable impact on how the appliance is built in the first place. When used, it only has to match the user actions with those already available in its memory. This can improve user experience considerably.

The prediction made on the basis of this pattern recognition on an ML algorithm is essentially called predictive analytics. It is a growing field and one that can be further studied as part of Machine Learning training programs.

Moreover, there are some features that make Pattern recognition a great addition to the world of ML. Some of them are listed below.

  • It can detect familiar patterns and known issues accurately. (This function is extremely helpful in hi-tech to detect online fraud)
  • Classification of patterns
  • Continuous learning as more streams of data is analyzed and processed.

Overall, Pattern recognition acts as an improvement in ML algorithms as it aids in making certain tasks easier. This is why it is heavily utilized across applications in the fields of image processing, biometrics, seismic analysis, and speed recognition.

A very fine example of the use of Pattern recognition is in the field of DNA testing. It can aid the scientific community in detecting DNA sequences with more accuracy and a low error rate. This is advantageous in forensics as well where accuracy is extremely critical.

To conclude, the thin line between Pattern Recognition and Machine Learning is in their functions within an algorithm. While ML is the main method used to process data and influence outcomes, Pattern recognition acts as a helping hand.

One of the best ways to learn more about the differences between the two is to undergo Machine Learning training. Students and professionals can take advantage of online courses available in this field and make good use of the ample free time available during this lockdown period.

What Are Some Tips And Tricks For Training Deep Neural Networks?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Deep Neural Networks aid AI applications such as image and voice recognition to function at unprecedented accuracy. A Deep Neural network is basically an array of several layers, where each layer sieves raw data into a structured mathematical model. 

The process of making the data flow through the various layers is called Deep Neural Network Training. In humans, we also start recognizing an object once we have seen it several times. If you saw just one “car” in your entire life, you might not be able to recognize a car again if you saw a different model this time. 

In Data Science, this is easier said than done. Therefore, we have some tips and tricks that you can use when you sit down to teach your DNN to distinguish cars from trucks.

Normalization is Effective

Normalization layers help group logical data points into a higher consolidated structure. An apparent increase in performance has been recorded when using Normalization.

You can use it three ways;

  • Instance Normalization – If you’re training the DNN with small batch sizes. 
  • Batch Normalization – If you have a large batch size, supposedly more than 10, a batch normalization layer helps. 
  • Group Normalization – Independent of batch size, it divides the computation into groups to increase accuracy. 

Zero Centering 

Zero Centering is considered as an important process for preparing your data for training. Just like normalization, it helps in providing accurate results later. 

In order to zero center your data, you should move the mean of the data to 0. You can do this by subtracting the non-zeroed mean of the data from all the data inputs. This way, the origin of the data set on a scalar plane will lie on 0, making it Zero Centered.

Choose the Training Model Wisely 

One thing that you’ll come across when you learn Deep Learning, is that the choice of model can have a significant impact on training.   

Commonly, there are pre-trained models and there are models you train from scratch. Finalizing the right one that corresponds to your needs is crucial. 

Today, most DNN developers are using pre-trained models for their projects as they are resourceful in terms of the time and effort required to train a model. It’s also called Transfer Learning. VGG net and ResNet are common examples.  

The key here is the concurrency of your project with the pre-trained model. In case you can’t get a satisfactory model design, you can train a model from scratch too. 

Deal with Overfitting
Overfitting is one of the most popular problems in DNN training. It occurs when the live run of the training model yields exceptionally good results but the same wasn’t observed during the test runs. 

The problem is basically caused when the DNN starts accepting the attenuations as the perfect fit. This can be dealt with, using the technique of Regularization, which adjusts the problem of overfitting using an objective function. 

Conclusion

Wish you’d know more? Take up a deep neural network training course on Imarticus and start your progress today. DNNs are becoming increasingly popular in data science-related careers. Just like everything else, you can use the first-mover advantage with pro-active learning. 

The Perks of Using Machine Learning for Small Businesses!

Posted on by Imarticus Learning
Reading Time: 2 minutes

Machine Learning and Artificial Intelligence have often been associated with top-of-the-rank brands such as Google and Apple. That has led to the perpetuation of an idea that AI just isn’t for everyone… and that’s incorrect.

Artificial Intelligence, specifically Machine Learning, is just as accessible and usable to small businesses as they are to tech and finance titans. When it comes to staying ahead of competitors, the situation is make-or-break– emerging technology is the portal through which smaller companies can gain headway in an already airtight industry, or quickly adapt processes that take months to approve in larger corporations.

As with anything new, the future of artificial intelligence and Machine Learning also presents its own sets of stumbling blocks, some of which may prove to be a detriment for smaller companies with limited budgets and skilled personnel. R&D accounts for a large chunk of the expenditure; training and analysing models takes topline human resources.

However, if firms are willing to take the risk and take the plunge, there are a whole host of perks that will have small businesses emerging victorious:

Making Marketing Campaigns Stronger

Marketing is the be-all and end-all of many brands, especially those that heavily rely on brand image and word of mouth to sell products or services. Machine learning can be put to use in marketing in the following manners:

  • Personalising product recommendations
  • Automating cataloguing of products
  • Optimising content from email subject lines to Facebook ads
  • Researching trends and search terms
  • Revamping keywords and SEO strategies

To achieve the following goals:

  • Innovative products and services
  • Happy customers and lesser returns
  • Intuitive and interactive user experiences
  • Diversified revenue streams
  • Reduced marketing costs and subsequent waste

Driving Sales Numbers

When it comes to sales, insights and analyses of data can be a veritable goldmine– this is where machine learning comes in. A solid ML tool can analyse:

  • customer-product interactions
  • past purchases
  • digital behaviour
  • trending search terms
  • popular products
  • transaction types

Using this, firms can identify what leads are likely to convert and equally pay attention to converting hesitant users into loyal customers.

Upselling and Cross-selling

Upselling means getting the customer to purchase a higher or more upgraded product, while cross-selling means pitching products in the same segment or complementary to the product in their cart. Machine learning can be leveraged to produce personalised recommendations of products and services based on analyses of the existing database. By identifying past purchases or inter-linking products, machine learning tools can upsell or cross-sell appropriately, thereby driving revenue and increasing the number of items sold.

Automating Repetitive Tasks

Small businesses are often faced with having to delegate the most menial tasks to precious employees, leaving the latter overburdened and unable to innovate. Using machine learning to automate repetitive tasks can ensure that routine measures are taken care of at scheduled times and employees are left with time to think strategically and fulfil intended roles. Some tasks that are automatable include:

  • Generating and sending email responses
  • Setting up a sales pipeline
  • Collecting and logging payments
  • Gathering and evaluating client satisfaction

Conclusion

Regardless of the industry, machine learning offers several perks for small businesses to help them grow, expand and generate revenue through different streams. From bookkeeping and manual data entry to voice assistants and exclusive data insights, a machine learning course can put you at the. Forefront of the industrial revolution taking the world by storm today.

How Statistics Relate to Machine Learning?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction

Machine learning and statistics have always been closely related to each other. This led to an argument about whether it was different from machine learning or formed a part of machine learning. Several Machine learning courses specify statistics as one of the perquisites for machine learning.

Hence, we need to develop an understanding of the fact if statistics relate to machine learning and if it does, how?

Individuals working in the field of machine learning concentrate on the task of model building and the result interpretation from the model that was constructed while the statisticians perform the same task but under the cover of a mathematician concentrating more on the mathematical theory involved in the machine learning task concentrating more on the explanation of the predictions made by the machine learning model. So, we can say that in spite of the differences between statistics and machine learning, we need to learn statistics in machine learning.

Statistics and machine learning

Both statistics and machine learning are related to data. Although they work with the data in their way, some requirements are needed by both and hence they form a close relationship with each other. Given below is a step by step analysis as to how statistics relate to machine learning.

Data preprocessing requires statistics

To proceed with the machine learning task, cleaning of data is a mandatory step. This process involves tasks such as identifying missing values, normalization of the values, identifying the outliers, etc. These operations call for statistical concepts such as distributions, mean, median, mode etc.

Model construction and statistics

After the data has been cleaned, the next step is to build a model with that data. A hypothesis test might be needed for model construction which calls for good statistical concepts.

Statistics in evaluation

Model evaluation requires tasks such as validation techniques to be performed so that the accuracy and model performance increases. These validation techniques are easily understood by the statisticians but a bit difficult for the machine learners to interpret as it involves mathematical concepts.

Presenting the model

After the successful construction and evaluation of the model, the model is presented to the general public. The interpretation of results requires a good understanding of concepts such as confidence interval, quantification, an average of the predicted results based on outputs produced and so on.

Other than the above-mentioned steps some additional concepts must be adhered to while working with machine learning. Some of these concepts are listed below:

  • Gaussian distribution – It is often represented by a bell-shaped curve. The bell-shaped curve plays a very important role while normalising the data as a normalised data is supposed to lie at the point where the bell-shaped curve is divided into two equal parts.
  • Correlation– It can be either positive, negative or neutral. A positive correlation indicates that the values change in the same manner(positive causes positive and negative leads to negative). A negative correlation indicates values change oppositely while neural suggests no relationship. This concept is of great importance to the analysts while identifying the tendencies in the data.
  • Hypothesis- An assumption might be done for the elementary predictive analysis in machine learning that requires a good understanding of the hypothesis.
  • Probability – Probability plays an important role in predicting the possible class values in classification tasks and hence forms an important part in machine learning.

Conclusion

Statistics is of huge importance to machine learning, especially in the analysis field. It is one of the key concepts for data visualization and pattern recognition. It is widely used in regression and classification and helps in establishing a relationship between data points. Hence, statistics and machine learning go hand in hand.

How AI and ML Affects Cybersecurity?

Posted on by Imarticus Learning
Reading Time: 2 minutes

The digital world has been shaken up many a time by cyber-attacks which only continue to get sophisticated and more complex. True to the fact that this era is being referred to as the ‘digital dark age’, data fraud and cyber attacks are two of the top 5 global risks in the world today, not far behind natural disasters and abject weather situations

However, AI and ML are being leveraged to take the battle against cyber attacks up a notch. The future of artificial intelligence will see cybersecurity being taken off the hands of human resources and automated to achieve more efficient results in real-time.

How AI and Machine Learning Affects Cybersecurity

Detection of Anomalies

Before prevention comes detection– and that was one of the many failings of a human-based security force that couldn’t keep up with increasingly complex digital threats. Deep learning and access to databases spanning decades have made AI and ML capable of detecting anomalies in existing systems and tracing sources, whether internal or external.

Pre-emption of Strikes

AI and ML are crucial in the continuous battle against cyberattacks. That said, they’re also instruments used by hackers to conduct strikes. In a case of fighting fire with fire, AI and ML can be leveraged to pre-empt such strikes to identify vulnerabilities and identify threats in services from as basic as emails to as confidential as financial transactions.

Prediction of Threats

As any Machine Learning course would teach, pattern prediction is a perk of AI and ML that can be used in achieving cybersecurity targets and maintaining defenses against breaches. Emerging technologies such as these can successfully predict the likelihood and type of future threats as well as identifying the source to take preventative measures. The same logic can be implemented internally, to analyze internal systems and close up loopholes and weak links.

Improvement of Biometric Authentication

Gone are the days when passwords and swipe patterns were the most innovative authentication technology could get. Biometric authentication is the new norm– think face ID, fingerprint technology– but inaccuracies and failings were always a concern. Today, developers are leveraging AI and machine learning to rid biometric authentication of its imperfections to make it more stable, reliable and more difficult to hack. This is crucial because biometric authentication affects so much more than cellphones and email addresses– it’s used for ID verification, financial authentication and more. Therefore, the stakes are much higher.

Management of Vulnerabilities

In the days when security was largely relegated to antivirus software and human resources, vulnerabilities would be manipulated and turned into a threat or an outright attack before measures were taken. In contrast, AI and ML allow firms to identify and manage their vulnerabilities well in advance so that the approach is preventative rather than scrambling for a cure. AI and ML use a plethora of combinations and tactics to identify these vulnerabilities, such as:

  • Dark web leads
  • Hacker discussions or threats
  • Threat patterns
  • Frequently targeted systems or divisions
  • Risks and losses at hand

By effectively leveraging emerging technologies, firms can meet cyber-threats head-on, even strike pre-emptively, instead of dealing with thousands of dollars’ worth of losses and bills in the cleanup.