During February, 2019, 14 million to 19 million new malware infiltrations were recorded on computers running major brand antivirus software. In global aggregate numbers, malicious state sponsored cyber-attacks generate 3000-8000 gigabits per second of malicious data on enterprise and government servers — with the nefarious goal to consume network bandwidth and disable the servers.

At the same time, hundreds of millions of network-attached security cameras, Wi-Fi printers, personal computers, digital assistants and Internet of Things (IoT) gadgets often exclude current security patches.  These IoT devices provide an entry bridge for criminal states and individuals wishing to access an organization’s confidential information. The result, all too often, results in a security breach of important confidential data.

Why Artificial Intelligence (AI) for Network Security?

Given the incredible scope of the increasingly complex threat – conventional security methods are no longer sufficient to protect against today’s sinister state sponsored security threats.

The Solution is to Employ a Combination of Artificial Intelligence (AI) and Machine Learning (a subset of AI) on Secure US and European Designed and Manufactured Hardware

Artificial intelligence duplicates the rational human intellect at machine speed – enabling computers to rapidly identify and accurately scope the threat magnitude.

Artificial Intelligence utilizes a machine learning model using Artificial Neural Networks (ANNs) which use computing elements to construct a system that is modeled after the operation of neurons in the human brain. For instance, a human’s insights evolve based on ‘learned material’. In machine training, models are fed huge quantities of information and the system then analyzes the information and optimizes processes based on new strategies and competencies utilized by an attack or malware attack.

Practically speaking, malware attacks are discovered by modeling the attacks behavior and features, and the characteristics of sterile versus malicious data can ideally be fine-tuned using a large volume of patterns. Thus, AI and ML are emerging as an essential part of an enterprise network’s security policy – as it is becoming practically impossible to manually detect and quarantine most advanced threats before they cause serious harm.

Why Use Interface Masters Secure Networking Hardware

But again the technology must be employed on secure hardware – and we are all aware of the state sponsored hardware security threat. Interface Masters hardware is designed and manufactured in the United States and effectively guards against premediated state sponsored intrusion into the actual hardware.

AI Benefits for Cybersecurity

Protecting enterprise networks through AI-based threat prediction and prevention allows cybersecurity to go from being an inhibitor to a business enabler. Specifically, deployment of AI can decrease security incidents and allow IT to prioritize their efforts. As a result, IT is transformed from reacting to events to proactively securing the enterprise environment, while getting rid of routine tasks that derail strategic projects such as automation, cloud deployment, and virtualization. AI also enables improved business continuity and customer service by better guarding against attacks that expose sensitive customer and corporate information.

AI-centered network security improves corporate compliance and better meets government guidelines for key industry segments such as financial, healthcare, and vital infrastructure, as well as in-house security policies.

AI Use Cases for Cybersecurity

AI-led advances will make cybersecurity smarter, more adaptive, and less human-intensive. Industry analysts believe that AI based cybersecurity will be the major enabler for next-generation IT across a number of use-cases.
Phishing is the top security threat based frequency faced by the enterprise today with over 90% of all cyberattacks begin with phishing. An effective phishing email attack can cause the collapse of the enterprise’s complete security scheme including supplying adversaries with credentials needed to penetrate the enterprise network, gain access to business-critical databases, and purloin sensitive financial or customer data.

Pattern recognition-based machine learning techniques alleviate most phishing attacks – and continually improve as the AI database expands and ‘gains experience’. This involves using the large body of existing enterprise emails to analyze relationships between internal and external domains, frequently contacted entities, and specific authentication patterns. Based on a sizeable number of analyzed emails, real-time alerts could be set by the system to highlight threats before they evolve into data breaches.

Advanced persistent threats (APTs) and malware detection typically utilizes signature matching, heuristics, and sandboxing

According to the AV-TEST Institute, 560,000 new forms of malware are produced every day.   Traditional approaches fail to counter the volume and sophistication of such attacks. Simply put, machine learning can be used to identify malicious code without requiring a specialist to clearly specify characteristics defining such code.

State sponsored malicious insiders have been the source of a range of high-profile security threats that have been difficult to identify, expensive in their impact and which can cause serious operational (including compliance) and competitive perils. For example, per the National Counterintelligence and Security Center, “Over the past century, the most damaging U.S. counterintelligence failures were perpetrated by a trusted insider with ulterior motives.”

Using a set of categorized samples, a machine learning-based system can identify patterns for every enterprise user and network device. Such data would then be compared to detect subtle inconsistencies that point to threats in progress. Behavior of users and applications could also be modeled, including employees’ demographic data and/or behavioral patterns, which could be extricated from network traffic and access logs traffic to indicate fraudulent activity.

Interface Masters Technologies’ embedded network appliances are scalable network security platforms capable of providing the hardware and software foundation for network security appliances for applications including AI/deep learning-based threat mitigation. Interface Masters appliances feature off-the-shelf server hardware technologies supporting high-performance threat protection including AI-based security applications.

Interface Masters Technologies has for over 20 years been providing off-the-shelf innovative networking solutions with customization services to OEMs, Fortune 100 and startup companies. Our headquarters is located in San Jose, California in the heart of Silicon Valley where we are proud to design and manufacture all of our products.  Based on MIPS, ARM, PowerPC and x86 processors, Interface Masters appliance models enable OEMs to significantly reduce time-to-market with reliable, pre-tested and pre-integrated appliance solutions that can meet the most challenging networking requirements.

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