Cybersecurity best practices and DDoS defence strategies
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The rise of artificial intelligence attacks keeps CISOs, CIOs, and CEOs up at night. If AI combined DDoS attacks against IoT devices and cloud platforms, chaos could ensue. AI can change a network or IoT-driven attack in mere minutes. With proper security preparation, organizations can reduce the impact of an AI-powered DDoS attack.
This article discusses the double-edged sword between the positive organizational benefits of artificial intelligence and the negative repercussions of hackers and cybercriminals accessing similar tools to launch their DDoS attacks.
Using AI to battle AI is not just in a sci-fi movie; this true-to-life reality has become the modern cybersecurity battleground. Nexusguard, a global provider of DDoS mitigation and protection capabilities, understands this reality all too well. Their research teams discover the implications of this new warfare and its impact on all organizations regardless of size or geo-location. Nexusguard incorporates several mitigation and protection strategies by leveraging artificial intelligence and machine learning to help stop global DDoS attacks.
Understanding DDoS and how AI has changed the playfield is critical for all organizations.
Anatomy of an AI-based DDoS Attack
Sophisticated attacks and cyber threats involving DDoS attackers have moved beyond human intervention. Al-Powered attacks are fully automated, agile, and effective. Leveraging data mining to feed complex and creative algorithms is one part of the attack strategy.
An attack algorithm involves information collection and a result. It needs as much information as possible for machine learning techniques to work. Attackers try to collect information about failed attempts, scans, or restarts in the beginning stages. They may use data mining to learn supervised or unsupervised ML and build an AI model that mimics decision-making.
Hackers will alter their various decision-making stages of the attack, including:
The hacker's expertise in developing complex yet effective algorithms is critical to their success in bypassing their victim's DDoS defensive counter-measures.
Stopping the UnStoppable: Automated DDoS attack
DDoS has the power to destroy established sites and services on the Internet. Robotic automation and scripting have made denial-of-service attacks more popular. Hackers similar to security operations or SecOps teams will process their vast amounts of security telemetry data into an AI engine to develop datasets to feed into machine learning models. The hacker's data is an accumulation of the various reconnaissance efforts to determine which organizations are less protected to stop their DDoS attacks. That data is processed through AI and then to machine learning algorithms to execute to decide on their next episode.
The organization's security teams process attack threats into the various defensive tools, including XDR platforms, to process the data and then feed the results into machine learning to help determine the best "defensive" strategy.
While both sides of the DDoS deliver their virtual and counter-attack virtual punches, the amount of data processed benefits both parties. Using AI gives an advantage to hackers by granting them the ability to adjust their distribution based on targets that protect the highest success. SecOps teams also benefit from learning about these hacker attacks and their AI and ML, and they can create several automated counter-attacks to stop the propagation.
What Role does Automation Play in Stopping AI-Powered DDoS?
Speed through security automation is a critical weapon against AI-based DDoS attacks. Hackers will use automation to adjust their threat vectors based on real-time analysis of their success against various attack services; SecOps will use automation to block attacks and establish pre-prevention rules and controls based on analysis of AI-processed datasets and machine learning trends.
Which is faster? Who will come out on top?
Both Parties Swapping Roles in the Battlefield of AI-DDoS Attacks
Do hackers get attacked? Of course! Rival cyber criminals often target their competition with DDoS attacks. Do global organizations use offensive attack tools to counter-attack the hackers launch a DDoS against their assets?
How quickly do the tables turn between the one "hunter" and the "hunted." Hackers learn from their targets how to defend their cyber attack platforms. Using AI and automation, hackers can spin up their attack platforms, tear them up, and spin them again into another cloud provider. Global organizations can also spin up and spin down their infrastructures to avoid being shut down entirely by a DDoS Attack.
Each side of the battlefield learns from the other's tactics. This new game is no longer executed by human intervention. Many of these offensive and defensive tactics become driven by AI and automation. Deep learning is another critical capability.
The Promise of Deep Learning to Prevent AI-DDoS Attacks
Deep learning has a critical current and future role in battling AI-based DDoS attacks. Recently, there has been a growing interest in using different deep-learning models to detect DDoS attacks. This model is complex and requires extensive data and computing resources. DDoS mitigation experts like Nexusguard recognized early in their solution development strategy the need to embrace deep learning. Their adoption of deep understanding led to the innovation of their smart-detection and smart-mitigation capabilities.
Processing deep learning and using the outputs required high expertise and resources. Hackers, similar to the firms like Nexusguard, compete for global engineering talent. With a minimal global pool of experts in AI and ML with working knowledge of deep learning, hackers and international organizations continue to develop talent.
Optimizing DDoS Mitigation and Prevention - The Nexusguard Way
Nexusguard offers a comprehensive DDoS protection platform that combines People, Processes, and Technology. Their platform includes Application Protection (AP), Origin Protection (OP), and DNS Protection (DP), which can effectively safeguard public-facing websites, applications, APIs, infrastructure, backend, and DNS servers from all types and complexities of DDoS attacks.
Founded in 2008, Nexusguard has over a decade of experience in DDoS attack detection, mitigation, and analysis. We build our strategy for success from the ground up on a solid foundation of people, processes, and technology. Organic growth and staying focused on countering the threat of DDoS attacks are critical to our success. Our comprehensive product portfolio now combines the features and benefits of on-premise, private cloud, and global cloud to protect against various attacks.
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Deep Learning plays a critical role in battling AI-powered DDoS attacks, and Nexusguard offers a comprehensive array of DDoS protection solutions that combine people, processes, and technology to safeguard organizations from all types of DDoS attacks.