Fighting against bots: protecting our networks and data

They are one of the darker sides of Artificial Intelligence. Bots, used to automate attacks and bypass traditional security measures, can carry out a wide range of harmful activities such as data theft, financial fraud, and malware distribution, jeopardizing the credibility of banks or tech companies. 

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urrently, the term “bot” encompasses a wide range of computer programs that automate tasks on the Internet. While some have a useful purpose and are even beneficial to users, others are used for malicious purposes. 

However, the term “bot” can be misleading if we reduce it to too simple a definition. It’s not a single entity, but an umbrella under which many automated computer programs shelter. Understanding this diversity is crucial to recognize when a bot represents an opportunity and when it poses a threat. 

Bot categories 

Broadly speaking, we can classify bots into two main groups: 

BENEVOLENT BOTS 

• Chatbots: Designed to simulate conversations with human users, mainly on instant messaging platforms or websites. They are often used for customer service, technical support, or even entertainment. 
• Search engines: “Crawlers” or “spiders” are bots that crawl the web to index information and content, enabling the functioning of search engines like Google or Bing. 
• Monitoring bots: They monitor the status of websites, networks, or computer systems to detect failures or performance issues. 
• Shopping bots: They automate the online purchasing process by searching for products, comparing prices, and making transactions. 
• Social media bots: They automatically post content, manage interactions with followers, and even analyze network data, making them a valuable tool for marketing departments in any sector. 

MALICIOUS BOTS 

• Spambots: Send unsolicited emails for advertising or fraudulent purposes. 
• Web scraping bots: Extract information from websites without authorization, often for malicious purposes such as data theft or creating fake content. 
• Fraud bots: Manipulate online systems to obtain illicit gains, such as in ticket purchases or sports betting. 
• Attack bots: Launch denial-of-service attacks (DDoS, Distributed Denial of Service) to overload and collapse websites or networks. 
• Malware bots: Distribute malicious software, such as viruses, trojans, or ransomware, to infect devices and steal information. 

It is those encompassed in the second group that concern large companies, as they undermine user trust. Some figures are truly shocking, especially regarding banks, fintechs, and social networks. 

A study by the University of Boston revealed that approximately 5.6% of accounts on Twitter are bots, while on Instagram, this percentage would be between 10% and 15%. Although Facebook has not released official data, it is believed that the presence of bots on this platform could be even higher than on the other two. 

In the financial realm, bots represent a significant threat. According to reports from Javelin Strategy & Research, online banking fraud reached $30 billion in 2022, with a significant portion attributed to bot activity. 

How advanced detection solutions work 

Detection solutions have evolved towards machine learning and artificial intelligence techniques to identify and block bots in real-time. The techniques used are varied and focus on locating aberrations in user behavior or analyzing web traffic data on a massive scale. 

• Behavior analysis: To identify patterns that are characteristic of bots, such as requests to a website at abnormally high speeds or clicks on links in a way that is not typical of human users. 
• Network traffic analysis: This technique analyzes network traffic to identify patterns that are characteristic of these programmes. For example, bots may come from IP addresses known to be used by bots or may send traffic to a website from an unusual geographic location. 
• Web content analysis: This technique analyzes the content of a website to identify scripts or malicious code that could be used to control bots. 

User identity verification is a fundamental aspect in the fight against the proliferation of bots, especially in sensitive fields such as banking. Old CAPTCHAs do not cover current needs. Other options, such as 2FA (two-factor authentication), are emerging to consolidate a robust online security landscape. At this point, we must highlight the penetration of biometrics in the identification strategies of all types of companies (banks, insurers, and gaming platforms, among others), which use unique physical or behavioral characteristics of an individual to verify their identity. Some examples of biometric technologies include facial recognition, fingerprinting, voice recognition, or iris recognition. TrustCloud VideoID‘s assisted or unassisted biometric analysis completes identification in less than 3 minutes, with all security guarantees, including advanced deepfake detection functionality. Request a free demo now. 

What are the benefits of using advanced bot detection solutions? 

Firstly, they provide better protection against cyber-attacks by identifying and blocking bots more accurately than traditional solutions such as firewalls, antivirus, and intrusion detection programs, thus reducing the risk of successful attacks. Additionally, they are less likely to generate false positives, preventing the accidental blocking of legitimate users. On the other hand, they offer greater visibility of bot activity in business networks, helping to identify and address threats more effectively. 

Choosing the right bot detection solution 

When choosing a detection solution, companies must consider a number of factors, including: 

• The size and complexity of the network: Companies with large and complex networks will need a more powerful solution than companies with smaller networks. 
• The types of attacks the company is exposed to: Companies should choose a solution that can detect and block the types of attacks they are most vulnerable to. 

What is the future of bot detection? 

The threat is constantly evolving, so detection solutions must also evolve to keep up. Future solutions will use even more sophisticated machine learning and artificial intelligence techniques to identify and block bots, and they will increasingly integrate with other security, digital identification, and KYC solutions. 

Contact our security and fraud experts