The pandemic was the perfect time for everybody to actualize their remote working dreams. But with that, many problems have arisen in the meantime. In 2021, cybercrime increased by 600%, and there’s no stopping it. Funnily enough, most of them were phishing attacks in which the scammers pose as health representatives from the Center for Disease Control (CDC) and the World Health Organization (WHO).
Phishing attacks are not uncommon and have been in the news for decades. But what is worrying about it is that hackers find new ways to exploit your network’s vulnerabilities with every new year. With 1.5 million phishing sites being created every month, how do we keep up?
42% of the US labor force was working from home in 2020, including managerial and technical roles. Now that you look at it, email is the best way to impersonate somebody and carry out such attacks. High-level professionals have been the easy target for such attacks. With more specialized attacks created with each passing day, it’s becoming humanly impossible to identify and thwart them.
So, how do we track it? Using artificial intelligence. It is seemingly the only option out there that can act and adapt as fast as we need it to. But, can we trust artificial intelligence? Let’s understand why it could be a paradoxical solution.
Phishing scams are more commonly known as ‘Nigerian Prince’ scams, where impersonators steal your confidential information for their benefit, albeit illegal. They’re focused on stealing any kind of sensitive information and don’t have a particular target in mind. With time, this has evolved into a host of attacks that go much beyond the usual impersonation and are highly targeted.
As of 2021, more than 12 types of phishing scams are popular in the market. They are email phishing, HTTPS phishing, spear phishing, whaling, vishing, smishing, angler phishing, pharming, pop-up phishing, clone phishing, evil twin, watering hole phishing. Out of these, the most common ones are detailed below.
Email phishing: It’s done using email and downloads malware using links, PDFs, or any other files that can be downloaded.
HTTPS Phishing: Phishing attacks are made using websites that seem encrypted with HTTPS security but are riddled with malware.
Spear phishing: These are targeted email phishing attacks directed towards either an individual or an organization.
Whaling: Also known as CEO fraud, the scammers impersonate senior-level executives and ask the user to process a payment or review a document.
Vishing: It’s done over the phone, and the scammers tend to alert you over in such a way that you’re forced to give out your personal information.
Most of these attacks are currently being identified using our own wits. It’s also one of the reasons why artificial intelligence is being purported as the new cybersecurity warrior.
Daily, at least 25% of phishing attacks get past the current security defences that we have. Some of these defences include phishing filters, spam filters, and the like. But, these filters cannot cope with the never-ending adaptive capabilities of phishing networks. That’s where artificial intelligence or AI, in short, comes in.
Artificial intelligence networks can adapt by learning constantly. It’s one of the reasons why it’s able to identify phishing attacks much quicker than the usual methods out there. It can handle a lot of data at one go, and it stores it simultaneously for future use. It pretty much learns on its own while constantly monitoring your systems.
AI-powered devices also have better endpoint protection — one of the most vulnerable parts of any network. Cybersecurity solutions such as antivirus software or VPNs tend to learn the phishing behaviour over time. This results in the creation of signature patterns over time. If these signatures change, they can’t identify it, but artificial intelligence-based tools can. In this case, you have increased awareness and faster-alerting systems.
There are many ways in which artificial intelligence and cybersecurity go hand in hand. A few of the most common methods are that the tools look for anomalies; they understand the context of a message or how users communicate.
When it comes to anomalies, it tends to look for specific phrases that indicate a sense of urgency or are asking you to fill out sensitive information. In the same way, once these tools are installed, they tend to read the messages and understand their context. It’ll account for points such as whether or not the sender has contacted you previously or whether they’re asking you for untoward information and the like. Using this information, they set up a filter based on your response and eventual communication with them.
Many machine learning models prevent such attacks with a low false-positive rate. They are classified into three main categories: single classifier, hybrid models, and deep learning. Single classifiers are those which make use of one algorithm only. Hybrid models make users of two or more algorithms get the job done. But deep learning models mimic the complex networks of our brain and are more thorough in their approach.
Some artificial intelligence and machine learning algorithms that are used to identify phishing attacks are given below:
Support Vector Machine: It makes use of a two-way classification system in which each section is labelled as either safe or dangerous. Depending on the type of attack it identifies, it classifies it and takes the necessary action.
Decision Tree: This model makes use of a tree structure in which a feature and a possibility are included. When certain choices are made, either of the two results already input into the algorithm can be either.
Random Forest: A random forest is a much more complex model that uses multiple decision trees. It averages the output of all the decision trees together and produces one result. It gives a much more accurate result.
Bagging: It’s a bootstrap aggregating procedure where the original dataset is divided into multiple sets. It uses resampling techniques to generate a dataset of the original size and then runs its analysis. Once it’s done, the output is given out as the average of all the divided datasets combined.
Convoluted Neural Network: It’s a deep learning algorithm used for image classification. The network consists of multiple layers – the outer layer, hidden layers, and the input layer. It stores the data so that the information in each layer contributes to the next (forming a feature map) and so forth.
Artificial intelligence and cybersecurity can be a killer combination, but not in the way that you think. On the one hand, artificial intelligence can help you get ahead of phishing attacks by implementing algorithms that detect them in milliseconds. On the other hand, you also have many phishing attacks that are particularly orchestrated using artificial intelligence.
By using AI, they can create malware that precisely consists of the harmful application within the files that are being sent. It also prevents the reverse engineering of such a scam, making it hard to figure out the culprit. All of these come under the ‘Smart Phishing’ domain and use several whitehat techniques to carry out numerous illegal activities.
From the protection perspective, it’s hard to disregard the importance of implementing Artificial Intelligence and subsequent machine learning models. These algorithms work based on the purpose that they’re created for. In that case, it makes more sense to use AI-assisted software to simplify and accelerate the data security process.
With remote work becoming the go-to option, work is limited to a laptop/ desktop device. Mobile phone usage is becoming increasingly common, and AI-assisted software works perfectly with smaller devices too. It also uses predictive modeling algorithms that can warn you much beforehand, thereby increasing the user’s protection. The most significant advantage is that even if the phishing attacks are made using an AI-based system, the user’s software can identify them.
All in all, while it may seem like artificial intelligence and cybersecurity are a paradox of their own, we err on the side of its benefits rather than the negatives. You stand a much better chance at protecting yourself and your enterprise when you use artificial intelligence-based software to detect and prevent phishing attacks.