Cybercrime has grown into a global threat, with revenue generated by malicious activities now rivalling the economies of leading nations. In fact, if cybercrime were a country, it would rank as the third-largest global economy, following the United States and China. With an estimated worth of $8.4 trillion annually, it’s a sophisticated industry, driven by ever-evolving technology. With such value attached to cybercrime, it should come as little surprise that cybercriminals are now turning their attentions to AI to help with their attacks. Read on to see how how hackers are using AI in cybercrime.
No business or individual is safe
While it might be tempting to think hackers only target large businesses, recently they have increasingly targeted small and medium-sized enterprises (SMEs), with 43% of cyberattacks now directed at this sector. SMEs are particularly vulnerable due to limited cybersecurity budgets and less robust systems. Their vital role in global commerce, contributing 40% of GDP in most economies, makes them lucrative targets. Likewise, individuals shouldn’t consider themselves below the level of interest for hackers. Ransomware attacks targeted at individuals rather than firms are becoming more and more common.
A rapidly evolving environment
The cybercriminal landscape is evolving. Hackers are using artificial intelligence (AI) to automate attacks, bypass security systems and create more advanced methods of deception. This race between hackers leveraging AI and security teams deploying it defensively highlights how critical this technology has become in modern cyber warfare.
AI and hackers – an increasing risk
AI has transformed the way hackers approach their craft, allowing for automation, efficiency and sophistication in their attacks. This has led to a meteoric rise in cybercrime attacks. Here are some common hacking techniques, enhanced by AI:
1. Phishing and spear-phishing attacks
AI has elevated phishing to a dangerous new level.
- Traditional phishing involves sending bulk fraudulent emails to unsuspecting victims.
- AI-enhanced spear-phishing uses machine learning (ML) to analyse data, tailoring messages that are highly convincing and personalised. AI scans social media profiles, job titles and email interactions to craft messages that appear authentic.
- This leads to a higher success rate, tricking even vigilant employees into divulging sensitive information.
2. Malware creation and obfuscation
Hackers are using AI to develop malware that adapts in real-time:
- Polymorphic malware can change its code each time it runs, making it harder to detect with traditional antivirus systems.
- AI also helps malware navigate security systems, learning how firewalls and endpoint security solutions operate.
- By creating undetectable code, hackers increase the chances of successful infiltration.
3. Automated brute-force attacks
AI enhances brute-force attacks by automating and refining them:
- Hackers use AI to guess passwords quickly, employing algorithms that learn common patterns in human password creation.
- AI-powered tools also identify vulnerabilities in systems faster than manual methods, significantly reducing the time required to execute an attack.
4. Social engineering at scale
Social engineering attacks, which exploit human psychology, have become more sophisticated with AI:
- Chatbots powered by natural language processing (NLP) can impersonate humans convincingly, tricking users into sharing sensitive information.
- AI analyses patterns in human behaviour to predict and exploit weaknesses, such as recognising when an employee might bypass security protocols under stress.
5. Deepfake technology
Deepfake technology, driven by generative adversarial networks (GANs), is a growing threat:
- Hackers create realistic fake videos or audio clips, impersonating executives or employees (also known as deepfakes)..
- These can be used in business email compromise (BEC) scams, where fraudsters impersonate company leaders to authorise fraudulent transactions.
6. Network vulnerability identification
AI allows hackers to quickly identify vulnerabilities in networks:
- Scanning tools powered by ML analyse vast amounts of data to uncover weaknesses in software or hardware.
- Once a vulnerability is identified, AI can deploy targeted attacks, such as exploiting unpatched software or outdated security protocols.
7. Ransomware-as-a-Service (RaaS)
The ransomware market has been revolutionised by AI:
- Hackers use AI to develop ransomware strains that are harder to decrypt.
- AI also helps automate the delivery of ransomware attacks, targeting specific industries or regions.
- The efficiency and scalability provided by AI have contributed to the exponential rise in ransomware incidents globally.
8. IoT exploitation
The Internet of Things (IoT) is a prime target for AI-driven attacks:
- Hackers use AI to locate unsecured IoT devices, such as smart cameras or connected appliances.
- Once inside, they can exploit these devices to access broader networks or carry out distributed denial-of-service (DDoS) attacks.
9. AI-generated exploit kits
Hackers now use AI to create exploit kits that automate attack sequences:
- These kits can identify and exploit vulnerabilities in real-time, delivering malware or executing harmful scripts.
- AI’s ability to adapt and refine these kits makes them particularly dangerous.
the good and bad of ai
While hackers are harnessing AI to bolster their attacks, cybersecurity experts are also deploying AI to counter these threats. Machine Learning (ML) and Deep Learning (DL) systems are being used to detect anomalies, analyse vast amounts of data and predict future attack vectors.
AI’s role in cybersecurity
- Real-time threat detection: AI-powered tools can monitor networks and identify unusual patterns indicative of a cyberattack.
- Predictive analytics: ML algorithms forecast potential vulnerabilities, enabling organisations to address them proactively.
- Automated response systems: AI systems can isolate infected devices or block malicious traffic without human intervention.
An escalating battle
As hackers refine their techniques using AI, cybersecurity teams must stay one step ahead. However, the sophistication of deep learning systems poses challenges for defenders:
- DL models can mimic legitimate users or applications, bypassing traditional security measures.
- The sheer scale and adaptability of AI-driven attacks make it difficult to anticipate every potential vector.
A continuing arms race
The battle between hackers and cybersecurity experts shows no sign of slowing. As AI, ML and DL systems grow more complex, both sides will continue to innovate. This escalating arms race underscores the importance of vigilance, proactive measures and investment in cutting-edge security solutions.
The thin end of the wedge
The integration of AI into cybercrime marks a pivotal moment in the digital age. Hackers are leveraging AI to automate attacks, outsmart security systems and exploit human vulnerabilities on an unprecedented scale. From advanced phishing schemes to deepfake scams, the risks posed by AI-enhanced cybercrime are vast and growing.
However, the same technology offers powerful tools for cybersecurity professionals. AI, ML, and DL are being deployed to predict, detect and counter threats in real-time. The ongoing battle between attackers and defenders will shape the future of digital security, making innovation on both sides inevitable.
As businesses and individuals, staying informed and investing in robust security measures is vital. As outlined above, cybercrime is not just a corporate issue – it affects everyone. By understanding the evolving tactics of hackers and the power of AI, we can better safeguard our data, devices and digital lives.
