AI-powered botnets are a growing risk to the security of IoT devices. The evolution of AI is ramping up the sophistication of botnets as they increasingly leverage machine learning (ML) algorithms to expand the reach and impact of their attacks. This is creating new challenges for security teams as they’re forced to navigate a more complex threat landscape.
Traditionally, botnet attacks involve threat actors connecting multiple IoT devices to overwhelm a site with large amounts of internet traffic. This prevents users from accessing products or services. Threat actors can even use the disruption as a smokescreen to conduct malicious activity, including phishing and ransomware attacks.
In recent years, the proliferation of IoT has ramped up botnet activity and made it easier for threat actors to launch widespread attacks. According to Statista, the number of IoT devices is set to reach more than 29 billion in 2030. Coupled with the rise of AI, this has expanded and evolved the threat landscape taking the risk posed to IoT devices to the next level.
The emergence of AI-powered botnets is amplifying threat actors’ abilities to orchestrate complex cyberthreats with greater ease. To stay ahead of this evolving challenge, organisations need to arm themselves with a comprehensive defence strategy that incorporates both security best practice and powerful analytics.
AI and advancing botnet attacks
While botnet attacks are already a critical challenge for organisations, AI-powered botnets can attack for a longer amount of time and easily change their tactics from one type of denial-of-service (DoS) attack to another in real-time. They can adapt and refine their attacks and execute on this without the need for human intervention. The speed that they can evolve makes them harder to detect and mitigate than traditional botnets and increases pressure on security teams.
As well as introducing new risks, AI-powered botnets are also intensifying existing security challenges around IoT devices. They are capable of manipulating vulnerabilities, such as the lack of standardised security protocols, outdated software, and the overall complexity associated with managing multiple connected devices.
The consequences of a successful AI botnet attack can cause widespread damage to organisations. This includes disrupting critical services, compromising high-value sensitive data and causing long-term reputational and financial damage. It also brings the security and trust of connected devices under scrutiny. Getting prepared now is essential to avoid falling victim to complex AI-powered attacks.
Edging the advantage against botnets
The risks posed by AI-powered botnets will only increase as algorithms and technologies become more sophisticated. Protecting critical data and systems requires organisations to take proactive action underpinned by following best practice advice and deploying a comprehensive security information and event management (SIEM) solution.
For organisations facing resource restraints caused by the ever-changing nature of AI attacks, a cloud-native SIEM solution could be the perfect fit. They gain a powerful platform that enables them to:
- Identify and monitor anomalous activity – AI botnets won’t necessarily look like traditional botnets, and this could make them harder to detect. In light of this, security teams need a cloud SIEM that utilises automated rules to spot any suspicious activity and then identify its source. Security teams can optimise their threat detection processes with analytic rules that use scenario modelling, behavioural modelling, and ML to quickly alert to the signs of an AI botnet attack.
- Close visibility gaps – With a cloud-native SIEM platform, security teams can ensure that they are monitoring for malicious activity across their entire digital ecosystem, so no threats slip through the cracks. This is especially important for mitigating botnets that can lay dormant for long periods of time. Advanced analytics allow security analysts to easily identify activity in networks, system servers, applications and across users to find threats faster.
- Rapidly mitigate attacks – Leveraging an intelligent solution with guided workflows empowers security teams with Risk-Based Prioritisation (RBP) to drill down on the most urgent threats in their IT or OT environment. Prioritising risks based on factors such as the origin of location of a login boosts analyst productivity when mitigating AI-powered attacks. It allows analysts to streamline tasks and enable quicker decision-making.
On top of deploying a cloud-native SIEM solution, organisations need to ensure they’re following best practice advice to establish well-rounded protection for their IoT devices. Regularly patching software and implementing encryption, authentication and access controls are all essential to establish a strong security posture against AI-powered botnets.
Maintaining good password hygiene and building awareness of the signs of a distributed denial-of-service (DDoS) attacks also contributes towards building resilience. These can include the inability to access a particular network service and/or website, abnormal patterns in traffic and Slow or unresponsive servers.
Future-proofing your security strategy
AI-powered botnet attacks will continue to escalate and become more sophisticated as technologies continue to evolve. Due to this, organisations across the board will likely see cybersecurity challenges around DDoS attacks get worse before they get better.
Staying ahead of evolving threat tactics requires organisations to build awareness around the IoT devices that are present within their network. They must ensure that best security practice is always at the forefront and support this with a reliable SIEM solution to gain the edge over AI botnets.
Author: Kev Eley is responsible for Sales in UK and Europe at LogRhythm. With 12 years’ experience in leading sales teams in the cybersecurity sector, he has plenty of knowledge and expertise across start-up and enterprise environments.
