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The consequences of living in an interconnected world driven by technology were brought into sharp focus by a series of shocking data leaks and security breaches in 2017. From the data of over 150 million people being compromised in the Equifax breach to the shutdown of the U.K.’s National Health Services by the WannaCry ransomware attack, cybersecurity problems resulted in an estimated $5 billion in damages in the last year alone. However, this total pales in comparison to the projected $6 trillion of damages per year expected to be the norm by 2021.
In 2016, Trend Micro, makers of XGen™ endpoint security, declared,
“The challenge today is not in creating new systems. It’s in securing them.”
This statement rings true today as traditional systems requiring consistent interactions between humans and machines are replaced by new technologies utilizing machine learning, the latest trend in artificial intelligence (AI). Some IT experts hope to harness the power of such systems to address problems with cybersecurity and minimize the occurrence of widespread and damaging breaches in the future.
What is Machine Learning?
Coined in 1959, the term machine learning refers to a form of AI technology with the ability to “learn and adapt to new data without human interference.” Unlike traditional systems, machine learning doesn’t require fresh code to be introduced every time a new pattern, behavior or scenario arises. Instead, the original algorithms established during setup automatically adjust to the change.
Systems powered by machine learning tap into the massive streams of data modern industries collect every day, process it with algorithms and use what they “learn” to make predictions about future actions. Every prediction is based on data, meaning the quality of the data drives the quality of the results. Data containing errors or missing key bits of information leads to inaccurate predictions and can cripple a system, but the more the technology is refined, the smaller the risk of errors being introduced.
As part of the movement toward the automation of every possible system, machine learning has already begun to transform the interactions between consumers and brands, the delivery of health care, the basic operations of financial institutions and the way businesses understand their customers.
Everyday Interactions with Machine Learning
It’s possible to have multiple daily encounters with machine learning without realizing what’s happening. With all the data industries now have to handle, it can be difficult to make any use of the information without machine learning algorithms. Some of the most basic processes people take for granted rely on machine learning to deliver expected results.
Today, the average person usually encounters machine learning when dealing with large institutions and corporations. However, as big data gets bigger and interactions between humans and technology grow more complex, machine learning is likely to become ubiquitous across industries.
Some of the most recognizable examples of machine learning in current use occur in the financial, commercial and health care sectors, although educational institutions and manufacturers are also beginning to incorporate more AI into their operations.
Fraud is one of the biggest concerns in the financial sector, but banks, credit unions and other financial institutions handle so many transactions every day, no human team can catch every potential sign of malicious activity. Fraudulent transactions require time and money to correct and can leave permanent scars on the reputations of institutions failing to protect the assets of their clients.
Machine learning algorithms can be coded and trained to monitor for signs of fraudulent activity or suspicious data. Daily transactions are assessed for potential red flags, such as:
- Unusual purchase volume or frequency
- Purchases from uncommon or unfamiliar retailers
- Very large purchases
- Very small purchases
The process is much faster with machine learning than could ever be possible with humans and is prone to fewer errors, leading to improved security and a considerably lower risk of fraud.
Banks also use machine learning to assess the histories of people applying for loans and other financial assistance. Patterns showing an individual may be high-risk can help loan officers and mortgage lenders make smarter decisions and avoid potential losses.
Each time consumers interact with large e-commerce outlets, machine learning is at work in the background. Product recommendations, retargeting ads and remarketing emails rely on machine learning to deliver the personalized shopping environments required to retain consumers in an industry where experience is everything and competitors can easily steal traffic with more appealing deals or better customer service.
Every action on an e-commerce website provides data for machine learning algorithms to analyze. Even searches on sites such as Amazon make use of “relevance features” to deliver suggestions to individual customers based on recent searches or prior purchases. Thanks to the increasing use of mobile apps and smart devices, buying behaviors in brick-and-mortar stores are beginning to be incorporated into this highly customized shopping environment.
Just like financial institutions, retailers are concerned about preventing fraudulent transactions. Accidentally rejecting legitimate credit cards or allowing false charges to go through reflects poorly on brands and racks up chargeback costs. Machine learning looks for patterns suggestive of suspicious activity to ensure only authentic purchases are approved.
Modern health care relies on patient information stored in electronic health record (EHR) format instead of on paper. EHR data can be accessed from anywhere to deliver personalized care, making it much easier for physicians to share information and emergency workers to avoid mistakes when administering medications or conducting urgent procedures.
Algorithms to compare and interpret EHR data can detect red flags for specific conditions or warn health care workers about potential interactions between medications. Machine learning can also compare data from patients’ medical devices to their histories to help doctors make decisions about treatment. In an environment where time is of the essence, but speed can also lead to dangerous errors, machine learning can offer life-saving solutions for patients and providers.
How Does Machine Learning Help Prevent Cybersecurity Attacks?
The extensive use of technology across industries, especially those handling private or proprietary information, requires strong security measures to be in place at every level. To thwart persistent hackers, these measures must be adaptable without the need for constant monitoring or manual adjustment.
Since machine learning trains computers and other devices to “understand” patterns and adjust to changes, some IT experts see it at a viable solution to the growing problem of cybercrime. Others are concerned about the potential for excessive false positives resulting in fake alerts. To resolve these fears and create a smarter machine learning environment, a team at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) is combining algorithms with input from “expert analysts.” Data flagged as suspicious is passed on to these analysts. Legitimate threats are tagged and fed back into the machine learning platform to fine-tune the algorithms and minimize future errors. Platforms like Trend Micro’s XGen™ use other methods, including census checking and whitelisting, to create “smarter” algorithms and ensure real threats are detected before a breach occurs or data is lost.
Any company, public system or government agency dealing with personal information and other sensitive data is at risk for cybersecurity attacks, and the importance of developing reliable security measures can’t be emphasized enough. With machine learning becoming more mainstream, advanced security against breaches should become more accessible, providing better defenses capable of beating hackers at their own games.
Cybersecurity will always be a prominent issue, but the growth of machine learning technology has the potential to counteract the most crippling attacks and provide peace of mind for millions of people. By training computers to recognize patterns indicating malicious activity and continuing to refine essential algorithms, IT experts and cybersecurity engineers could discover a whole new system to protect the volumes of sensitive data flowing between machines every day.