Threat Awareness: A Critical First Step in Detecting Adversaries

One thing seems certain: Attackers are only getting more devious and lethal. Expect to see more advanced attacks.

We’re in an arms race with cybercriminals. Adversaries are becoming stealthier and more dangerous, constantly improving their attack techniques to evade the best security defenses. From malicious mobile apps to exploited cloud misconfigurations and devastating Remote Desktop Protocol (RDP) exploits, there’s no shortage of threats we need to protect against. 

As security practitioners, it’s our mission not only to build the new tools needed to arrest and detect threats effectively, but to help make sense of the wide-ranging nature of what constitutes security. That starts with threat awareness. After all, you can’t defend against what you don’t understand.

Here are some of the major threat landscape changes we’ve seen in the last year and will continue to see this year and beyond.

Evading Security Controls with Automated, Active Attacks
Automated, active attacks are on the rise. These types of attacks involve a mix of automation and human-directed hacking to evade security controls.

Attackers in the recent Snatch ransomware attacks, for example, gained access by abusing remote access services, like RDP, and then used hand-to-keyboard hacking to complete the attack. Recently, attackers have upped the ante by exfiltrating data before beginning encryption and rebooting machines into Safe Mode during the attack in order to circumvent endpoint protection. These changing attack methods are part of the growing trend of defense evasion and highlight the need for protection at every layer of the environment.

On the detection side, the problem is that it’s challenging to determine what’s a malicious versus legitimate use of those tools. This method has been used successfully by the criminals behind the SamSam and MegaCortex ransomware attacks.

Raising the Stakes with Ransomware
Ransomware creators know that if they can’t get past detection systems, their operation has little chance of success. Therefore, they’re putting a lot of effort into figuring out ways to evade detection systems altogether. One of the most effective methods is changing their appearance — often by obfuscating their code — to disguise their true intent.

For example, attackers may digitally code-sign ransomware with an Authenticode certificate. Anti-ransomware defenses give code with signatures a less thorough examination, and some endpoint security products may even choose to trust it.

At the same time, attackers exploit vulnerabilities to elevate their privileges to an administrative credential. This way, their privileges will meet or exceed the access permissions necessary to ensure that encrypting files will be successful.

Scamming Through Stealthy, Malicious Apps
Smartphones, tablets, and other mobile devices are rich environments for attacks. Not only can attackers steal user information and cash or cryptocurrency, but they can also use mobile devices to gain access to corporate resources.

Fraudulent banking apps, referred to as bankers, continue to plague users by stealing credentials for financial institutions. Downloaders — apps that appear to be finance-related but are really downloading banker payloads in the background — are increasingly common. Some bankers even steal credentials by abusing accessibility features to virtually monitor keystrokes when a user logs in to legitimate banking apps.

Unscrupulous developers are also finding success exploiting the legitimate in-app advertising model found on mobile devices, creating apps whose sole purpose is to maximize ad revenue. The most nefarious types of adware, known as Hiddad, hide themselves from the app tray and launcher, so they’re impossible to find and remove. Hiddad malware often takes the form of a legitimate app, like a QR code reader, but makes money through aggressive advertising.

Fleeceware is another example of how developers take advantage of legal models to scam unwitting consumers. These apps vastly overcharge users for app functionality that’s already available for free or at low cost, often relying on free trials that are nearly impossible to cancel to lure users into paying $275 for a simple calculator app. It’s an ongoing, widespread issue, with researchers recently uncovering 20 new fleeceware applications that may have nearly 600 million downloads.

Exploiting Misconfiguration in the Cloud
The strengths that make cloud such a valuable platform for computing and business operations — flexibility, speed and ease of use — are also what makes it challenging to secure. With changes happening at the rapid pace of cloud, operator error is a growing risk. All it takes is one misstep in configuration to expose the entire customer database to attack.

Attackers are taking note. Most cloud-based security incidents are a result of misconfiguration of some kind. Attackers know that companies struggle with a general lack of visibility into their cloud environments, so they can sneak in and carry out an attack before anyone notices. That’s why they’ve seen success with Magecart malware, which infected retailers’ “shopping cart” pages without their knowledge to steal customer information from businesses like Ticketmaster and Cathay Pacific.

As more and more companies turn to the cloud for backups, these attacks are becoming increasingly common. Businesses need visibility into the consequences of configuration changes, as well as the ability to monitor for malicious and suspicious activity in the cloud.

Abusing Machine Learning
Attacks against machine learning security systems are moving from an academic possibility into the toolkits of attackers. Machine learning systems have their own weaknesses, and it’s only a matter of time before attackers figure out how to evade them. Research shows how attackers could trick models, highlighting the need for multiple layers of protection.

With machine learning becoming as a regular part of defense, we’re also seeing the first signs of using machine learning models on offense. Imagine using text-to-speech machine learning models to evade security measures like voice authentication. Such technology, like the technology underlying “deepfakes,” has already allegedly been used in a CEO vishing (voice phishing) scam. In the future, attackers might also use machine learning to optimize attacks, like phishing email click-through rates.

The Road Ahead
As we look ahead, one thing seems certain: Attackers are only getting more devious and lethal. We expect to see more advanced attacks, like the weaponization of machine learning. In the meantime, awareness of existing threats gives companies the information they need to design effective protection.

Related Content:

Dan Schiappa is Executive Vice President and Chief Product Officer with Sophos. In this role, Dan is responsible for the overall strategy, product management, architecture, research and development, and product quality for the network security, enduser security, and Sophos … View Full Bio

More Insights

Read More HERE