As the universe of technology continues to expand, the forms and numbers of cyber threats are also increasing. With the increase in attack surface and threat types, cyber-attacks have also become sophisticated.
According to the Cybercrime Magazine, cybercrimes are expected to cost the global economy USD 10.5 trillion annually from 2025 onwards. That is 10,500,000,000,000 US Dollars! The report notes that “this represent as the greatest transfer of economic wealth in history, risks the incentives for innovation and investment, is exponentially larger than the damage inflicted from natural disasters in a year, and will be more profitable than the global trade of all major illegal drugs combined.”
Modern cybercriminals leverage Artificial Intelligence (AI), automated bots, cloud technology, Distributed Denial of Service (DDoS) and Machine Learning (ML) technologies to harvest sensitive information, mount phishing campaigns, steal confidential business data, attack multiple systems, disrupt sensitive services, and so on.
To deal with the ever-expanding threat landscape and growing complexity, enterprises are also responding with AI-assisted security postures. Security experts are employing sophisticated Data Analytics and AI-based threat detection systems in conjunction with Zero-trust regulations and the Principle of Least Privilege. According to a Markets and Markets Research report, the global market for Artificial Intelligence in Cybersecurity is rolling at an estimated 22.4 billion USD in 2023. It is predicted to surpass USD 60 billion by 2028.
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To develop complex data-driven, AI-assisted security systems, enterprises will need specialized processors called Graphics Processing Units (GPUs). This article will highlight the crux of employing GPU servers in advanced cyber threat detection systems.
Table of Contents
Advanced Cyber Threat Detection – A Primer
The concept of cyber threat detection is not new. It refers to the ability of an enterprise/ individual to accurately identify and manage threats/ malicious actors in their network, apps, and other services and digital assets.
Traditional threat detection systems (used in firewalls) leverage rule-based mechanisms and heuristic approaches for discerning threats. These traditional systems were minimally automated, resulting in glacial detection of malicious actions and cyberattacks. Such lethargic approach to dealing with tech-savvy cybercriminals endangered not only the interests of the organization, but also its employees and customers.
Today, cyberattack vectors have soared to new, near-unimaginable horizons. Hackers and other malicious actors are now using sophisticated techniques and strategies to breach IT systems and compromise sensitive business/ personal data. And as the Covid-19 pandemic forced millions of employees worldwide to work from home, the cyberattack surface area increased a million-fold in the blink of an eye.
Thankfully, cyber threat detection has also evolved with each succession in technological advancement. As the adage goes – data is the new oil – and so cybersecurity professionals have begun leveraging data-driven approaches for actionable insights. Advanced cyber threat detection systems use Big Data analytics, AI-based real-time analytics, process automation, and pattern recognition/ behavioral recognition using Machine Learning.
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What are GPU Servers?
GPU servers are large computational units that run GPUs to deliver lightning-fast data analytics, image/ pattern recognition, biometric identification, simulation/ graphics rendering, etc., among other applications.
Enterprises across sectors such as healthcare, architecture, financial services, data analytics, game development, cryptocurrency/blockchain development, etc. have long been relying on GPU servers to accelerate massive-scale data processing and complex computations. This has applications in ML/ DL model training, Artificial Neural Network/ Graph Neural Network projects (ANN/ GNN), real-time data analytics, scientific/ mechanical/ medical imaging and simulations, behavioral analysis, personalized recommendations, and so on.
Read: What Are GPUs and How Can You Afford One?
Role of GPU Servers in Cyber Threat Detection
Data centers, Cloud services, and IoT systems are witnessing staggering growth in userbase and user demands. This unparalleled, relentless growth in the usage of emerging technologies has lured cybercriminals to attempt massive and shrewd attacks without being perceived by traditional cyber threat monitoring systems. Forget phishing and credit card fraud, today malicious code and ransomware can be effortlessly injected into unsuspecting networks. After all, not every organization invests in Vulnerability Assessment and Penetration Testing (VAPT).
Warren Buffet considers cyberattacks a “bigger threat to humanity than nuclear weapons” and terms them “the number one problem with mankind”.
Defending IT systems against such unprecedented challenges requires data-driven approaches, behavioral analytics, pattern detection technologies, IP-monitoring systems, anomalous/ spoofed log-in flagging, and so on. Using each of these technologies individually in a silo entails unbearable cost and manpower overheads. Incorporating these together necessitates the employment of AI-enabled security solutions.
Deploying such sophisticated AI-enabled systems requires intensive processing capabilities not only to develop and train the underlying ML/DL algorithms, but also to accelerate the analytics and threat detection framework. GPU servers elegantly tie up these cybersecurity requirements and deliver the requisite processing power for all kinds of data analytics, pattern recognition, behavior monitoring, security incident flagging, log management, and other advanced cybersecurity use cases.
Benefits of GPU Servers in Cyber Threat Detection Systems
With technological advancements, Moore’s Law has become a thing the past and GPUs have emerged as the panacea for all data-intensive operations, information-rich visualization, Augmented/ Virtual Reality solutions (AR/ VR), multi-variable calculations and even financial risk management. No wonder the global GPU market (integrated + discrete) is projected to grow from nearly 20 billion USD in 2019 to over 200 billion USD in 2027.
On the cybersecurity front, GPU servers offer numerous enviable benefits such as –
Faster Threat Analysis:
Cybersecurity is a critical domain where the slightest delay or tardiness in detecting threats can inflict crippling damage. Dealing with real-time threat analysis with traditional CPU systems or even arrays of CPUs will be excruciatingly slow. Cybersecurity companies have already begun deploying enterprise-grade GPU servers. These machines GPUs deliver massive parallel processing power requisite to run multi-factor real-time threat analysis.
Furthermore, GPU servers can easily clean the input data, develop supervised/ unsupervised ML models and train ML/ DL systems for automated cyber threat detection and response. GPU servers can accelerate threat detection and analysis many times compared to traditional computational systems. Cloud GPU servers offered by Ace Cloud Hosting can support AI development, guard against massive DDoS attacks, execute multi-variable ML algorithms to detect malware and ransomware, and facilitate high-end database-level encryption.
Reliable Pattern Recognition:
Data for training AI systems through attack patterns can often be semi-structured or unstructured. For example, model training on physical breaches often uses old video footage. Training AI systems on how an employee behaves before committing any malicious/ violent action against to the organization requires lengthy training across different parameters. Again, analyzing endless chains of data packets (in continuous monitoring systems) in corporate networks is also unavoidable when developing AI-assisted cybersecurity systems.
Cyber threat management AI systems perform real-time pattern/ behavior analysis, flag aberrant/ spoofed log-ins, and monitor security logs and endpoint breaches, each of which requires immense computational power on its own. Given that GPUs can draw on their highly-efficient SIMD architecture to run the same mathematical/ analytics operations on numerous datasets in parallel, they are eminently suitable for AI-powered cybersecurity solutions dependent on humongous amounts of unstructured/ semi-structured data for training. Not only that, but the massive parallel processing capabilities of dedicated GPUs also brings speed and accuracy in pattern analysis undertakings.
High Scalability:
Cybersecurity solutions constantly require dynamic and unpredictable processing resources for real-time monitoring as well as for testing out new tools and services. Modern GPUs, though expensive, can easily provide scalability to even the most complex cybersecurity applications that harness adaptive pipelines. Adaptive AI can effortlessly handle a broader range of model complexity.
The exorbitant procurement and maintenance costs of on-premise GPUs can be overcome by subscribing to a Cloud GPU service. Cloud-based GPU servers can dynamically scale in terms of memory availability and compute resources. It can manage diverse resource-intensive workloads. Additionally, enterprises are also protected from downtimes and recurrent outages when harnessing Cloud GPU servers for cybersecurity projects, and can also subscribe to geographic redundancy and cyberattack/ disaster recovery plans for just a little more expenditure.
Real-time Threat Monitoring and Visualization:
Real-time data visualization constitutes a core requirement of cybersecurity monitoring operations. Be it highlighting anomalous behavior against predictable log data, or generating graphical threat analytics for easy comprehension, or analyzing millions of causal interactions and correlations within a GNN database, interactive data visualization is the bread-and-butter of threat analytics.
Any delay in identifying a cybersecurity threat can cause massive financial damage to an organization, not to speak of the reputational damage. Hence, data visualization plays a substantial role in deciphering threat vectors, predicting potential risks and generating behavior insights.
Deeper and more dynamically interactive the data visualization demands, higher the processing power requirement. GPU servers are excellent for real-time verification and validation against access privileges, digital identity management policies, and other security parameters. They can then follow these up with detail-oriented, data-rich visualization with zero lags.
Battling Bots:
Internet bots or web bots are web-based programs that can perform numerous automated tasks over the internet to imitate human activities. There are good bots as well, such as search engine spiders that crawl around the world wide web to extract relevant information from different websites. But cybercriminals also leverage such programs to send spam, scan for sensitive personal information, steal credit card details, infect computer networks with malware, among other nefarious uses.
Botnets created with malicious intentions are a bitter example of these. Bots constitute a colossal segment of the internet traffic today. Cybercriminals aren’t the only ones though running bots for malicious purposes. Marketing agencies use them to falsely demonstrate hype or to drown out public criticism. Political parties and foreign espionage agencies are also known to use them to influence electoral results, as happened in US Presidential elections.
DDoS-as-a-Service is one example that harvests sophisticated botnet to demolish a website or a server or a network. To battle such sophisticated and large-scale cyberattacks, GPU-powered AI-based cybersecurity solutions is the magic bullet. With powerful GPU servers, AI-based threat detection systems can instantaneously uncover irregularities, flag similar ping patterns, and prohibit bot-automated malicious traffic.
Conclusion
Not every company invests in cybersecurity measures. When they are afflicted by ransomware or DDoS attacks, they are often compelled to pay exorbitant sums to hackers.
However, infinitely more concerning is the case of companies that comprehend their susceptibility and deploy sophisticated cybersecurity measures but without adequate planning and foresight.
Today every enterprise with any form of digital systems in place is prone to cyber threats. To remedy this as their earnings and vulnerabilities grow bigger, they must eventually proceed to using AI-based multi-threat management systems with real-time data monitoring, analytics and response. Supporting such AI systems with top-notch Cloud GPUs is the natural progression in the cybersecurity arms race.
Ace Cloud Hosting with its advanced Nvidia GPUs and highly secure Tier-4 Data Storage facilities can help you bring your A-game against wannabe hackers and other malicious actors. We also offer disaster recovery-equipped data storage services. Book a call with our Consultant to learn more and rev up your cybersecurity measures.
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