With great power comes great responsibilities- it is the most iconic phrase written by Stan Lee and popularized by Spider-Man. Undoubtedly the line holds to disruptive technologies, including AI and ML.
You might have heard only praise and good things about AI and ML, but that does not mean that they cannot become a bitter enemy to humanity. AI & ML are advanced technologies bound to function under the administrator’s command.
Artificial Intelligence (AI) and Machine Learning (ML) technologies have many positive applications, from helping researchers better understand neural pathways in the brain to assisting law enforcement with identifying suspects in criminal investigations.
They are renowned for the greater good of cybersecurity. However, these technologies also hold the potential to ruin our perfectly running digital world and become a source of power to the dark web users/administrators.
If you’re not familiar with how AI and ML might impact cybersecurity, this blog will discuss both sides of the coin and help you better understand how this technology might affect you one day soon.
Here are some famous AI-based cybersecurity use cases:
# As per Forbes, 69% of enterprises trust AI for handling cyber attacks.
# As much as 80% of telecommunication industries believe that without AI, cyber security is not feasible.
# Network security uses AI up to 75% in developed nations, as per Statista.
# Cloud security accounts for nearly 59% of use cases of Artificial Intelligence.
# According to Oracle, 62% of U.S based enterprises have already implemented AI for cyber security applications.
# IBM says AI’s most outstanding contribution in cyber security is the speed of analyzing threats with an acceleration up to 64%.
Look at the infographic below depicting a section of the above stats:
A lot has been explored in recent years about how little we know about security. If you ask ten different cybersecurity experts, you will likely get ten unique answers. However, many are unfamiliar with the exact aspects of cyber security.
It isn’t necessarily a bad thing because there is no one-size-fits-all approach to cybersecurity.
What works for one firm might fail to work for another in a completely different industry or with entirely different kinds of data; Similarly, what works in one country might not work in another country. However, everyone agrees to the definition below.
What is Cybersecurity?
Cybersecurity is a proactive defense against hackers, cybercriminals, data theft, and other cybersecurity threats. Security experts work with IT professionals to identify weaknesses in systems before intruders can exploit them.
Businesses need to incorporate cybersecurity into their overall business plan to prevent losses due to cybersecurity issues. It means implementing tools like encryption software (including secure mobile device management), building alliances with local law enforcement agencies, or hiring external cybersecurity teams.
In addition to helping keep your customers’ data safe, these strategies will also help strengthen your company’s bottom line by protecting its assets from theft and damage in an increasingly digital world.
Companies must be flexible enough to customize their security strategies as needed so they can adapt to new threats without slowing down their business processes too much.
Major Cybersecurity Threats in 2021
#1. Malware: These are software programs designed to disrupt computer operation, gather sensitive information, or access private computer systems. Malware often arrives as spam messages containing attachments or links that download viruses and worms onto their computers when clicked on by users.
#2. Ransomware: It hijacks your computer, encrypts all of your files, and then demands a ransom payment for you to get them back. These sorts of attacks are becoming increasingly popular with criminals due to their success rate.
#3. Social Engineering Attacks: It is a deception that exploits human nature to trick people into giving up sensitive information or performing actions that harm their organizations.
#4. Phishing: It is social engineering typically carried out through email spoofing or instant messaging that aims to steal personal information such as usernames, passwords, and credit card details by masquerading as trustworthy entity in an electronic communication.
#5. PDF Scams: Using a PDF file to scam is a common exploit used by malicious hackers. In general, these types of attacks are tough to detect.
#6. Database Exposure Threat: Occurs when sensitive data stored on a server gets exposed to an unauthorized third party.
#7. Credential Stuffing: These are automated, web-based attacks in which attackers gather a list of usernames and passwords stolen from one website or application and use them to try logging into other sites and applications.
#8. Accidental Sharing: It is a scenario when users share confidential information without realizing they are doing so. It occurs when users intend to share a document only with their colleagues or manager, but it accidentally gets shared with all other users on an email distribution list.
That was all about the common cybersecurity threats; let’s explore how AI & ML influences them.
Positive Impacts of AI & ML Over Cybersecurity Threats
1. Combating Fraud & Anomalies
Anti Fraud use cases are more effective with machine learning as they can monitor transactions, behavioral patterns of users, or click patterns more effectively. Using Machine Learning for fraud detection is very efficient compared to traditional methods that require a lot of human labor.
In a recent study, Forbes reported that account takeovers (ATO) decreased from 33% to 17%, and revenue per client increased by 65%. With machine learning, financial institutions can block millions of fraudulent transactions before reaching consumers.
The industry is ripe for applying new techniques like deep neural networks, which allow analyzing large volumes of information at rapid speeds without compromising accuracy. AI algorithms also help reduce Internet traffic snarls caused by malicious bots trying to access web applications from various browsers on devices. This can be achieved by hiring top AI developers.
2. Handling Spams
Researchers found that the average word length in an email has increased, suggesting that scammers are putting more effort into creating authentic-sounding messages. Because of these complications, a straightforward solution will not be effective at entirely thwarting spam accounts – but using artificial intelligence does it.
Using advanced AI models called generative adversarial networks (GANs), researchers at MIT were able to develop algorithms capable of distinguishing between bots and humans with incredible accuracy.
3. Smart Malware Detection
Intelligent malware detection software was developed by computer scientists to detect malware using a set of predetermined rules based on known malware automatically. Intelligent anti-malware uses machine learning, or artificial intelligence, to learn what is safe code and what is malicious code.
Over time, smart anti-malware can quickly recognize new varieties of malicious code and create rules that help it determine if specific actions are suspicious or hostile. Not only do intelligent anti-malware keep data protected, but its ability increases as hackers develop new methods for attacking computers.
4. Data Leak Prevention
With massive amounts of data collected on people’s online habits, companies must use their immense stores of data responsibly. With artificial intelligence (AI) and Machine Learning (ML), companies can now mine their big data sets to understand better what kinds of activities might pose data leak risks.
The most vulnerable data types include customer credit card numbers, social security numbers, or health records, all pieces of information worth protecting from external threats. However, current cybersecurity methods aren’t capable of preventing all potential threats effectively.
Negative Impact of AI & ML Over Cybersecurity Threats
AI-based malware is more efficient in hiding from the reach of the anti-malware software. Thus, when the software finds and eliminates malware, the hackers would have already gained potential information from the user’s device.
Bots are pieces of software that run automated tasks on a compromised system. Malware authors often use them for malicious purposes like attacking websites or other computers and stealing information from infected systems.
One such malware has been Cryptolocker, which aggressively spreads itself via spam messages posing as speeding tickets. It encrypted users’ files until they paid a ransom in BitCoins to get their data back.
A botnet is a matrix of computers controlled by an attacker through malware installed on each computer. Combining these two has made it easy for criminals to wreak havoc online, earning them profit at the cost of innocent users’ privacy and security.
ML-based spoof emails are more effective and successful in stealing credentials. It is mainly because ML algorithms are reasonably successful in creating instant messages and emails that look like they are from trustworthy resources.
4. ML-based Password Generators
Common passcode guesses include-123456789, date of birth, name of your close family members, etc. With most websites encouraging solid passwords, it has become a hard nut to crack the users’ passwords.
However, ML-based algorithms have already been developed that guess passwords accurately. PassGan is a famous example of password guessing software using the ML algorithm for the job. So, we can expect more revolutionary password generators from hackers.
That was all about how Artificial Intelligence and ML are influencing the security of the digital world. Under the right hands, they are a boon to humanity, but they can quickly turn into a bane on the corrupt hands.
As for now, upgrade your security with these technologies to stay in the competition. Connect with a Machine Learning company in India to maximize your cybersecurity.