The Roles of Artificial Intelligence in CybersecurityDonna Marie Padua
One of the most practical uses of AI is cybersecurity. According to Norton, the worldwide cost to recover from an average data breach is 3.86 million US dollars. Research also shows that it takes 196 days to recover from a security breach. Business owners are taking extra precautions to avoid this financial and time sinkhole by using AI. We are going to take a look at how artificial intelligence can help developers improve the security of their website against malicious attacks.
According to research from Markets and Markets, the artificial intelligence industry is expected to grow to $190 billion by 2025. By the year 2021, it is estimated that three-quarters of commercial enterprise apps will use AI.
One area where artificial intelligence is making headway is cybersecurity. As in all industries, AI can improve data processes and free up humans from time-consuming responsibilities, allowing them to focus on more important issues.
AI is becoming such an important issue in cybersecurity, let’s take a look at some of the key ways the technology can help:
Updating Databases and Identifying Large Scale Movements
AI can be used to update security databases. By analyzing logs from various sources, artificial intelligence can detect when new threats are imminent.
In other words, AI can collect comprehensive data from different logs and records and “connect the dots” to identify new threats that are being spread by hackers.
AI can also identify malware and spyware trends by analyzing data across multiple channels.
By using AI, new malware systems can be detected much quicker and before they can do damage on a large scale. There will be more time to come up with prevention methods to fix any bugs or security flaws that may be exploited by the malware or virus.
Identify Unusual Activity
Besides detecting large scale malware movements, AI can also be used on an individual level to scan a system for abnormal activity. By constantly scanning, enough data can be collected to determine when a particular activity is abnormal.
Users can be constantly monitored to detect when unauthorized access occurs. If abnormal activity is detected, AI can use certain parameters to help determine whether or not it may indicate a threat or whether it is a false alarm.
Machine learning can be used to help AI determine what “normal” activity is and what should be considered “abnormal.” As machine learning becomes more advanced, AI will become better at detecting slight abnormalities which may indicate something wrong going on.
As above, “connecting the dots” is the key here. Certain slight abnormalities may not seem significant on their own, but together they can paint a bigger picture of what may be causing them.
An example would be how attackers entered the systems of US companies Home Depot and Target by gaining access to third-party supplier credentials. Unfortunately, this was perceived as normal traffic.
AI can constantly scan the system, analyze different activities, compare them with each other, and create warning alerts.
This is slightly different from how AI detects abnormal activity. Here, the focus is AI pinpointing potential weaknesses, bugs, and security flaws. For example, machine learning can be used to detect when untrusted data has been sent from an application.
Injection SQL vulnerabilities are one of the most commonly exploited weaknesses by malware and viruses to steal data and enter systems. Another weakness AI can help detect is a buffer overflow, or when an application puts more data than usual in a buffer. Yet another area where AI can help is human error. Employee mistakes are some of the major causes of data breaches, and AI can detect them in time to prevent damage.
On a larger scale, AI can stay up-to-date on current malware threats (as mentioned) and scan the current system to see how it would be vulnerable to any potential threats.
As AI gets more advanced, it cannot only detect when a certain system or update has a flaw, but automatically prevent those flaws from being exploited.
Whether it’s adding additional firewalls or fixing coding errors causing vulnerabilities, this can be a great way to prevent problems from occurring.
This is similar to prevention, but happens at a later stage – when malware has already entered the system. As mentioned, AI can be used to detect abnormal behaviors and connect the dots to create a profile of malware or viruses in the system.
The next step is an appropriate response to the malware or virus. This includes damage control, removing the virus from the system, patching any security flaws, and making sure additional protections are put in place to prevent the virus from infecting the system again.
Other Ways To Use AI for Cybersecurity
Beyond these five main benefits, there are other ways AI can be used to improve online security – such as e-commerce. This is significant considering e-tail revenues are projected to surpass $4.88 trillion by
- And while it shows business promise, e-tail also brings added pressure for retailers to make transaction systems more robust and secure.
AI has already simplified inventory management, customer support & other operations. Now it looks set to strengthen security processes.
For example, AI can be used to prevent credit card fraud. This is somewhat similar to the second point mentioned. Abnormal activity, such as a purchase from an unusual device, location or of an unusual amount, can be used to help prevent unauthorized purchases. It can also be used in biometric security systems to accurately detect users via face, eye, and fingerprint recognition systems.
Machine learning can also be used to help users choose passwords. These systems can warn users when passwords are not safe enough – or even when passwords may have been compromised.
Artificial Intelligence in Mobile Security
Most of us do our online transactions using our mobile phones, hence the biggest risk of hacking and fraud can happen from our mobile transactions.
Artificial Intelligence is considered more than a smart technology that is transforming industries into existing and emerging technologies. When it comes to industry AI development is creating a benchmark footprint and turning things to another new level. Moreover, this technology has enhanced the user experience with advanced features. AI is best for decision-making faster in the business as well as helping many firms in developing innovative solutions. By introducing such mobile app solutions in the market the competition in the mobile industry is increasing.
The best thing about AI is that without taking much time AI’s conquered the industry by pushing mobile technology. With its advanced feature of assimilates, an enormous volume of information at a point of time AI is introducing the latest trends in the market. This is the main reason AI is having so much buzz in the mobile technology industry.
Due to the sudden increase of data, it is striving every size of the business to use mobile technology. Data is increasing with the time and yes it needs to be protected. Here comes the key role of AI as a shield for the ever-increasing information that is connected to mobile technology. As it is a mobile technology all of the information is all about the apps, cloud services, website data, and IoT more. Therefore, AI with advanced technology is helping to improve security.
Tailored Mobile App Security
AI into mobile apps helps to provide location-based searches to users and makes things in better ways. This is the reason many mobile app development firms are integrating AI better-personalized App security. The advance feature of Artificial Intelligence redefined the definition of what it means to develop innovative AI mobile apps solutions.
In today’s time, security is the biggest concern for a mobile app developer while developing mobile applications. Therefore, AI helps provide a high rate of processing data that need an advanced level of data security. The usage of the mobile application is growing with time and AI is efficient technology in today’s time.
AI technology is rectifying security issues by alerting the user about probable threats and vulnerabilities. Artificial Intelligence has created a high level of the user-friendly environment by analyzing user behavior. Thus, when we talk about data security Artificial Intelligence with such advance features embraces a high liability.
Many AI-empowered applications provide personalized results that reduce the searching time. Therefore, AI can help users to leverage the benefits of brands and explore its more services. AI in Mobile Apps acts smarter by collecting data-oriented related to the user’s purchase history. This helps to deliver unique results to compile information with secure data that is obtained from online traffic, mobile devices, etc.
Boosting Mobile App Market
AI helps to maintain customer information which is otherwise a tough task to do. It is a multifaceted part of the process for marketers to maintain huge chunks of data. Without Artificial Intelligance this causes the reckless amount of time when to deal with a million or more customers. Therefore, AI-powered applications help the research market thereby guide user purchase while using a mobile App. This helps to gather all the information and eradicates maximum errors at the time of the purchasing journey.
It is witnessed that many e-commerce companies are coming up with AI technology to enhance their sales and marketing. AI assists the brands in various verticals to make better marketing planning to increase their sales using mobile Apps. From the results, it is witnessed that users are enjoying the role of AI in every manner by increasing the user-engagement.
Artificial Intelligence and Antivirus Programs
AI techniques have played increasingly important role in antivirus detection. At present, some principal artificial intelligence techniques applied in antivirus detection are proposed, including heuristic technique, data mining, agent technique, artificial immune, and artificial neural network. It believes that it will improve the performance of antivirus detection systems, and promote the production of new artificial intelligence algorithm and the application in antivirus detection to integrate antivirus detection with artificial intelligence.
Now, not all antivirus software has AI technology integrated within it. But here are 2 common ones you can encounter:
Avast Free Antivirus
For most people, Avast antivirus is a familiar name in security. Avast Free Antivirus is the most popular antivirus on the market, and its history of protections goes back decades. Avast Free Antivirus has been “using AI and machine learning for years” to protect users from evolving threats. In 2012, the Avast Research Lab announced three powerful backend tools for their products.
- The “Malware Similarity Search” allows almost instantaneous categorization of huge samples of incoming malware. Avast Free Antivirus quickly analyzes similarities between existing malware files using both static and dynamic analysis.
- “Evo-Gen” is similar “but a bit subtler in nature.” Evo-Gen is a genetic algorithm that works to find short and generic descriptions of malware in massive datasets.
- “MDE” is a database that works on top of the indexed data, allowing heavy parallel access.
These three machine learning technologies collectively evolved as the foundation for Avast’s CyberCapture.
CyberCapture is a core feature of the Avast security suite, specifically targeting unknown malware and zero-days. When an unknown suspicious file enters a system, CyberCapture activates and immediately isolates the host system. The suspect file automatically uploads to an Avast cloud server for data analysis. Afterwards, the user receives a positive or negative notification regarding the status of the file. All the while, your data is feeding back into the algorithms to define further and enhance yours and others’ system security.
Windows Defender Security Center
The Windows Defender Security Center for enterprise and business solutions, in the past years, received a phenomenal boost as Microsoft turns to artificial intelligence to bulk out its security. The 2017 WannaCry ransomworm ripped through Windows systems after hackers released a CIA trove of zero-day vulnerabilities into the wild.
Microsoft is creating a 400 million computer-strong machine learning network to build its next generation of security tools. The new AI-backed security features will start with its enterprise customers, but eventually filter down to Windows 10 systems for regular consumers. Windows Defender is constantly improving in other ways, too, and is now one of the top enterprise and consumer security solutions.
AI Still Has a Long Way to Go
While the benefits of AI in cybersecurity are numerous, there’s still a lot of room for improvement.
For example, while detecting abnormalities can help prevent unauthorized access to an account or detect malware in the early stages of an attack, it can also lead to false positives. AI can become a lot better at learning when an activity is truly abnormal (for example, a login from a new location may simply mean that the user is currently traveling).
It can be seen that these days there is a clear emphasis on using AI as a ladder in the mobile App. Keeping user’s benefits AI gives an advanced personalized experience. While being a part of a process this advance technology, AI App development Companies are helping businesses to grow their dimensions and thus increase their ROI.
Mobile technology is taking advantage of this technology to grow tremendously with time. As the future is all about mobile technologies AI is surely going to be more business-oriented.