How to Use Machine Learning to Defeat DDoS Attacks

Machine learning (ML) and artificial intelligence (AI) are critical players in the most recent cybercrime revolution for defenders and hackers alike. How can business professionals stop DDoS disruptions with ML as the foundation?

Why Use ML and AI to detect and prevent DDoS attacks?

Automation like AI and machine learning should replace triage tasks that computers could do more accurately than humans in cybersecurity. More capable ML systems outfit analysts with mature tools to supplement risk management plans.

Instead of management teams reteaching their analysts ad nauseam ways to reduce human error, their efforts are better placed in ML learning databases and algorithms. Employees could spend time teaching ML to be more accurate and efficient at detecting DDoS attacks by reinforcement learning, supervised oversight and data training. They can write policies and rule-based detection to have ML pay attention to organization-specific concerns while considering historical data and current DDoS trends.

These time investments reduce alert fatigue, false positives and wasted downtime during every facet of digital protection, including business continuity plan testing and system updates. Data-driven decision-making also prevents exhausted and stressed analysts from making emotionally charged or spontaneous decisions when careful tactics are necessary. This lets the ML algorithm suggest mitigation tactics or reveal entry points.

Developing a more competent system to stop DDoS with ML also means protecting service availability and uptime while performing neutralization. The resources it takes to stop an incoming DDoS attack could cause disruptions, even if it doesn’t fully pan out. More efficient systems keep machines powered and connected while stopping potential threats.

How algorithms search for DDoS intrusions

After analysts take the time to familiarize themselves with the ML algorithms, how can they stop DDoS attacks and with what techniques? Stakeholders and management boards will not need to know the jargon of these strategies, but they must understand there are numerous options to test.

Plus, it’s helpful to know how they differ, in nontechnical terms, to inform future cybersecurity funding and implementation decisions. These are some of the algorithms and methods that could target these catastrophic attacks:

• Logistic regression: Uses probability to determine the chance of a DDoS attack
• Random forest classifier: Constructs every possible decision tree to predict attacks. It works best along with supervised learning
• Support vector machines: Organizes data into classifications, separated by a line, and judges DDoS threat with categorization
• Gaussian Naive Bayes: Assumes each data point in the distribution can impact ML outputs
• K-Nearest neighbors: Considers how close data points are to each other to classify instead of other variables

Most machine learning categorizes each threat on a scale, redirecting it to appropriate places based on its malicious nature. It performs these actions much faster than it would take humans to execute.

Analysts can review the results, demonstrating the need for human intervention. Regardless of the team’s technique, each strategy leads to unique conclusions about the scale and scope of future DDoS attacks.

How companies can improve DDoS ML algorithms

Training AI is most of the battle in creating an intelligent, swift and accurate wall against DDoS threats. However, teams can maximize the usefulness of their ML efforts by incorporating other strategies. The first step is acknowledging the vulnerabilities of side attack surface areas because of diverse data silos. Everyone, from IT to the CEO, must know where their internal data goes and how it’s stored.

More companies are leaning toward dynamic software-defined network (SDN) structures prioritizing mobility over various workspaces. Consider how a business can simultaneously use cloud storage, data centers and on-site hardware to handle operations by going through routers and network protocols. It can add a few Internet of Things (IoT) devices and mobile company phones for a reasonably vulnerable network.

Companies may want to simplify these structures using process discovery and digital transformation to help ML algorithms better spot DDoS threats from the most relevant avenues. DDoS attacks are diverse, with botnets becoming more complicated and active and HTTP floods more aggressive.

Other cybersecurity techniques can help reduce DDoS attacks alongside streamlining operations:

• Data minimization
• Penetration testing
• Virtual private network (VPN) usage
• Ongoing nontechnical DDoS responses like training and cyber hygiene awareness
• Honeypots or decoy systems to redirect suspicious activity
• Other ML uses, like improving incident response or managing system updates

Use ML to detect DDoS attacks

Service interruptions impact businesses and everyday life more than ever, primarily as every device and human responsibility lies within a technological space. These innovations bring convenience and more DDoS attacks as criminals realize the value of these interruptions for their profits.

ML is one of the best ways to counteract these intense cyberattacks, especially when it has so many other applications outside of DDoS prevention that cybersecurity analysts can employ.