How Artificial Intelligence Has Changed The Way We Secure The Data?
Though the concepts have been around for ages the past two decades have seen a phenomenal in ML/AI applications. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence without the use of any human intervention or explicit programming. Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison.
The availability of very large databases of Big Data itself and the proliferation of cloud technology and cloud computing have directly contributed hugely to allowing ML/AI to sift through these very large volumes of very big data and mimic the human brain’s logic in inferential and logical predictions, gaining foresight or producing predictive insights into such data.
The figures and data volumes are mind-boggling and cannot be humanly attainable without ML/AI applications. It is estimated that by 2030 almost all businesses will use ML/AI techniques and the market value of training data sets using a Machine Learning course will see a market of 13trn USD!
AI in cybersecurity:
Cybersecurity is the most demanding and promising area for ML/AI. In theory, if the machine is given complete data both good and bad then it should throw up any pattern that is related to unusual behaviour or malware in the database. This implies that
- Your model needs to effectively harness a huge volume of available data including malware, and good and benign data.
- The data pipeline needs the data scientists and engineers to build and maintain a continuous process for the sampling of data sets and training effective data-based models.
- The goal of providing insights needs to be categorized by specialists in the domain to sift the bad from the good and the process and results need to be justifiable, logical and explained.
Sad but true, is the fact that many ML/AI security solutions lack in meeting these criteria.
The process used:
A basic tenet of cybersecurity is a multi-security-layered defence in depth rather than just the use of ML/AI technologies while scanning the system periodically for user-accessed content. The area of file downloading should stop SSL-encrypted communications between the user-client and destination servers and allow the scanning of every file involved in order to ensure scanner perceptibility. This is time-consuming and affects UX. However, such scanning is a compromise of providing a secure user experience with the white-listed files while effectively blocking threats and malware.
Once threat intelligence has been deployed there is still the zero-day or unknown threats which loom large. Such threats are sandboxed in a virtual environment mimicking the user environment and studied before labeling them as bad or good. ML/AI techniques with the deployment of artificial intelligence course trained algos can effectively do this process almost instantly and avoid the user having to wait for long periods of time.
Hackers use exploitative kits which borrow delivery techniques and exfiltration of previous known threats and attacks which are easily identified by ML/AI models trained to identify variants that are polymorphic. Importantly, answering queries on why particular data sets are classified ‘bad’ should use the expertise of domain specialists who are capable of explaining the triggers and test results in order to ensure better and more accurate predictive models.
Training the models:
There are two kinds of learning and making prediction models. One is unsupervised learning, which is based on data structure and free from any human bias in the selection of data sets or malware features. Supervised learning, on the other hand, uses human intervention in sampling and labelling the database while using labelled data for the extracted prediction model. Which method is better depends on the suitable parameters prior to training the artificial intelligence course of algorithms that result in the predictions?
The best security areas where AI/ML can help:
The cybersecurity challenges are confrontable by smart ML/AI algorithms. The detection of phishing attacks is dependent on the algorithm being able to easily compare the original and fake sites for logos, visual images, and site components. They can also detect unusual behaviour once they are trained in recognizing normal patterns on your profile or account. A red flag is immediately raised and you are asked to verify the transaction. This makes the hacker's job harder and your account safer and more secure.
An artificial intelligence course that can train the AI/ML model under expert guidance from cybersecurity and data science experts is a valuable tool in mitigating the effects of cyberfrauds. Do your course on AI and ML at the Imarticus Learning Academy to emerge career –ready in these fields.
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