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We continuously harden machine learning protections against evasion and adversarial attacks. Historically, detection evasion has followed a common pattern: attackers would build new versions of their malware and test them offline against antivirus solutions. In the cybercriminal underground, antivirus evasion services are available to make this process easier for attackers. A sizeable portion of the protection we deliver are powered by machine learning models hosted in the cloud. Most machine learning models are trained on a mix of malicious and clean features.

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We continuously harden machine learning protections against evasion and adversarial attacks. Historically, detection evasion has followed a common pattern: attackers would build new versions of their malware and test them offline against antivirus solutions.

In the cybercriminal underground, antivirus evasion services are available to make this process easier for attackers. A sizeable portion of the protection we deliver are powered by machine learning models hosted in the cloud. Most machine learning models are trained on a mix of malicious and clean features. Attackers routinely try to throw these models off balance by stuffing clean features into malware. Monotonic models are resistant against adversarial attacks because they are trained differently: they only look for malicious features.

To evade a monotonic model, an attacker would have to remove malicious features. Figure 1. Features used by a baseline versus a monotonic constrained logistic regression classifier. Inspired by the academic research, we deployed our first monotonic logistic regression models to Microsoft Defender ATP cloud protection service in late Figure 2 below illustrates the production performance of the monotonic classifiers versus the baseline unconstrained model.

Monotonic-constrained models expectedly have lower outcome in detecting malware overall compared to classic models. However, they can detect malware attacks that otherwise would have been missed because of clean features.

Figure 2. Malware detection machine learning classifiers comparing the unconstrained baseline classifier versus the monotonic constrained classifier in customer protection.

We combine all our classifiers using stacked classifier ensembles —monotonic classifiers add significant value because of the unique classification they provide.

One common way for attackers to add clean features to malware is to digitally code-sign malware with trusted certificates. Malware families like ShadowHammer, Kovter, and Balamid are known to abuse certificates to evade detection. In many of these cases, the attackers impersonate legitimate registered businesses to defraud certificate authorities into issuing them trusted code-signing certificates. LockerGoga emerged in early and has been used by attackers in high-profile campaigns that targeted organizations in the industrial sector.

Once attackers are able breach a target network, they use LockerGoga to encrypt enterprise data en masse and demand ransom. Figure 3. LockerGoga variant digitally code-signed with a trusted CA. When Microsoft Defender ATP encounters a new threat like LockerGoga, the client sends a featurized description of the file to the cloud protection service for real-time classification. An array of machine learning classifiers processes the features describing the content, including whether attackers had digitally code-signed the malware with a trusted code-signing certificate that chains to a trusted CA.

By ignoring certificates and other clean features, monotonic models in Microsoft Defender ATP can correctly identify attacks that otherwise would have slipped through defenses. Very recently, researchers demonstrated an adversarial attack that appends a large volume of clean strings from a computer game executable to several well-known malware and credential dumping tools — essentially adding clean features to the malicious files — to evade detection.

The researchers showed how this technique can successfully impact machine learning prediction scores so that the malware files are not classified as malware. One of our monotonic models uniquely blocks malware on an average of , distinct devices every month. We now have three different monotonic classifiers deployed, protecting against different attack scenarios.

We continue to evolve machine learning-based protections to be more resilient to adversarial attacks. More effective protections against malware and other threats on endpoints increases defense across the entire Microsoft Threat Protection. Questions, concerns, or insights on this story? Follow us on Twitter MsftSecIntel. Skip to main content This site uses cookies for analytics, personalized content and ads.

By continuing to browse this site, you agree to this use. Learn more. How Microsoft Defender ATP uses monotonic models to stop adversarial attacks One common way for attackers to add clean features to malware is to digitally code-sign malware with trusted certificates. LockerGoga variant digitally code-signed with a trusted CA When Microsoft Defender ATP encounters a new threat like LockerGoga, the client sends a featurized description of the file to the cloud protection service for real-time classification.

You may also like these articles Featured image for Inside out: Get to know the advanced technologies at the core of Microsoft Defender ATP next generation protection. While Windows Defender Antivirus makes catching 5 billion threats on devices every month look easy, multiple advanced detection and prevention technologies work under the hood to make this happen. Multiple next-generation protection engines to detect and stop a wide range of threats and attacker techniques at multiple points, providing industry-best detection and blocking capabilities.

Featured image for Tackling phishing with signal-sharing and machine learning. Across services in Microsoft Threat Protection, the correlation of security signals enhances the comprehensive and integrated security for identities, endpoints, user data, cloud apps, and infrastructure.

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For customers, they offer a seamless experience with added convenience and at discounted prices. For businesses, they provide recurring guaranteed revenue and build deeper customer relationships. From magazines and media-streaming, to the majority of the CPG industry, subscription schemes are proving increasingly popular, but are they always good? Here are 9 examples of these products-as-services in action, with some valuable lessons — what to do, how to do it and what to avoid! Not only did they find a target market with a clear user problem to solve, they also created an effective experience.

They offer a variety of packages and clearly lay out the steps for each, managing expectations with users. Ohne are an organic tampon subscription who offer a highly personalised experience. Users can choose the frequency, volume and variation of their order, not just at the point of subscription but at each interval. It may be a logistical nightmare in the back-end but this user-centric approach is a great innovation to suit every lady.

Flexible and tailored with a beautiful, intuitive UI. Well done Ohne. Barkbox are a great example of the mixed box model with their doggy treat subscription. This is where users select some of the products in their box, but the rest are a surprise.

As opposed to traditional email and SMS, this gives a non-intrusive way to promote products and build your brand.

Project B are a pregnancy-based service whereby they adapt the products in the box at each installment. This pro-active, thoughtful approach builds trust and keeps their offering highly relevant. Graze are good at getting customers over the initial hurdle of committing to a subscription scheme.

Especially in the fast-moving consumer goods industry, as customers pass supermarkets daily so their experience is already super convenient. Brands need to highlight the added benefits of their service and Graze do a good a job of this advertising their freebies and discount codes. It was great and everyone in my office used it… for the first month! Offering a freemium is great, only if you have a clear path to then convert users. Focusing too much on acquisition of new subscribers can lead to neglecting existing subscribers and, in turn, high attrition.

London Sock Company is an oddball in my opinion. A monthly sock subscription… um what? How many socks do you guys get through? Are your feet burning holes through the cotton? Is there data supporting this high-frequency demand for socks? A subscription service must be solving a real life user need and delivering clear value against that need.

Netflix hit headlines when they fell victim of a rookie mistake of setting the subscription price too low. Amazon may be a surprise for the ugliest spot on my list but I wanted to show how even the best of the best can get it slightly wrong sometimes. It was a confusing experience that left a sour taste in peoples mouths. Amazon has since learned that a subscription scheme can be designed in a way that is clearly beneficial for people, and not so that they have to deceive them into signing up.

Transparency is key. Overall, subscription schemes drive value to both businesses and customers alike, and have the power to change the way we buy and sell. Stephen Louzi even describes the impact as the end of ownership and the start of usership. Creating a suitable service will not only elevate your customer experience, but it can ultimately drive revenue through smart innovation. What do you reckon? What subscription schemes have caught your eye for the right or wrong reasons?

Let me know in the comments below. Sign in. Get started. UX Collective. Subscription models: 9 lessons from the good, the bad and the ugly. Rhiana Matthew Follow. UX Collective We believe designers are thinkers as much as they are makers. Bit of a mixed bag really. UX Collective Follow. We believe designers are thinkers as much as they are makers.

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