Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Perimeter

9/15/2020
12:00 PM
Eric Parizo
Eric Parizo
Commentary
Connect Directly
Twitter
LinkedIn
RSS
E-Mail vvv
50%
50%

Encrypted Traffic Inference: An Alternative to Enterprise Network Traffic Decryption

Finding threats in encrypted inbound network traffic is complex and expensive for enterprises, but a fascinating new approach could eliminate the need for decryption.

Enterprises cannot afford to ignore the threat posed by encrypted inbound network traffic. Adversaries now commonly use encrypted traffic flows to cloak cyberattacks, slipping malware, ransomware, and other malicious content past perimeter detection systems.

But as many network security practitioners understand, finding and stopping encrypted threats requires gaining visibility into encrypted flows; that, in turn, requires decrypting and inspecting encrypted traffic, which is no easy task.

Decryption equipment is expensive to purchase and deploy, plus complex to configure and manage. Even in an ideal deployment scenario, backhauling traffic between remote users and a data center network security stack is increasingly untenable.

There may be a better way. What if it were possible to determine with near certainty whether an inbound encrypted network flow contained malcode without actually using decryption?

In newly published research from Omdia, Fundamentals of Network Traffic Decryption and Risk Management (Omdia subscription required, learn more here), we examine the state of both established and developing technologies for addressing the risk associated with inbound encrypted network traffic.

Some of our key findings include:

  • Omdia estimates that, at a minimum, between 70% and 80% of enterprise inbound network traffic flows are now encrypted.
  • The majority of enterprises do not decrypt their inbound network traffic, creating a sizable opportunity for adversaries.
  • For those that do, methods of decrypting this traffic might soon no longer be viable, due to costs associated with scaling traditional, proxy-based decryption, but also due to specific changes in the new TLS 1.3 encryption standard that effectively break existing decryption approaches.

Fortunately, new techniques are emerging for addressing encrypted traffic risk. Some involved decryption in the cloud with an identity-aware proxy (IAP), while another called session key forwarding obtains encryption key pairs from host memory without the processor-intensive processes of proxying sessions, obtaining keys, and re-encrypting sessions. Both offer notable promise as alternatives to traditional decryption methods.

However, encrypted traffic inference (ETI) is perhaps the most fascinating of all emerging alternative approaches. ETI solutions analyze aspects of encrypted traffic flows to discern whether they are likely to be malicious, without using decryption.

Based on concepts first published by Cisco Systems researchers in 2016, ETI works by capturing encrypted network flow data attributes -- including DNS metadata, TLS handshake metadata, and HTTP packet headers – and analyzing them for specific, intricate patterns that indicate malicious activity.

A number of vendors – including Cisco, Juniper, NTA vendor Corelight, NDR provider IronNet, and specialist vendor Barac – all offer some degree of ETI capability today.

While ETI is promising, it remains nascent. Enterprises are only beginning to adopt the technology, so its ability to identify malicious traffic in encrypted flows consistently over a long period of time, while delivering consistent ROI, has yet to be determined. 

Still, enterprises should not ignore the significant potential ETI presents. Omdia believes that the most appropriate long-term encrypted traffic risk management solution for enterprises will likely combine ETI with decryption.

For example, an organization may use an ETI solution to provide a "first pass" on inbound encrypted flows. Most traffic would likely be deemed safe, but the presumably small percentage of flows found to be suspicious or inconclusive would then be decrypted for deep-packet inspection. This combination approach would decrease the need for decryption, lowering decryption-related costs, and allow for more flexible deployments across increasingly hybrid infrastructures.

Omdia recommends enterprises prioritize a review of their business strategy and technical approach to network traffic decryption and risk management, with an eye toward considering how ETI and other emerging capabilities have the potential to reduce cost and operational complexity while ensuring inbound encrypted flows are not a cybersecurity blind spot.

Related Content:

Eric Parizo supports Omdia's Cybersecurity Accelerator, its research practice supporting vendor, service provider, and enterprise clients in the area of enterprise cybersecurity. Eric covers global cybersecurity trends and top-tier vendors in North America. He has been ... View Full Bio
 

Recommended Reading:

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Manchester United Suffers Cyberattack
Dark Reading Staff 11/23/2020
As 'Anywhere Work' Evolves, Security Will Be Key Challenge
Robert Lemos, Contributing Writer,  11/23/2020
Cloud Security Startup Lightspin Emerges From Stealth
Kelly Sheridan, Staff Editor, Dark Reading,  11/24/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Write a Caption, Win an Amazon Gift Card! Click Here
Latest Comment: This comment is waiting for review by our moderators.
Current Issue
2021 Top Enterprise IT Trends
We've identified the key trends that are poised to impact the IT landscape in 2021. Find out why they're important and how they will affect you today!
Flash Poll
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-20934
PUBLISHED: 2020-11-28
An issue was discovered in the Linux kernel before 5.2.6. On NUMA systems, the Linux fair scheduler has a use-after-free in show_numa_stats() because NUMA fault statistics are inappropriately freed, aka CID-16d51a590a8c.
CVE-2020-29368
PUBLISHED: 2020-11-28
An issue was discovered in __split_huge_pmd in mm/huge_memory.c in the Linux kernel before 5.7.5. The copy-on-write implementation can grant unintended write access because of a race condition in a THP mapcount check, aka CID-c444eb564fb1.
CVE-2020-29369
PUBLISHED: 2020-11-28
An issue was discovered in mm/mmap.c in the Linux kernel before 5.7.11. There is a race condition between certain expand functions (expand_downwards and expand_upwards) and page-table free operations from an munmap call, aka CID-246c320a8cfe.
CVE-2020-29370
PUBLISHED: 2020-11-28
An issue was discovered in kmem_cache_alloc_bulk in mm/slub.c in the Linux kernel before 5.5.11. The slowpath lacks the required TID increment, aka CID-fd4d9c7d0c71.
CVE-2020-29371
PUBLISHED: 2020-11-28
An issue was discovered in romfs_dev_read in fs/romfs/storage.c in the Linux kernel before 5.8.4. Uninitialized memory leaks to userspace, aka CID-bcf85fcedfdd.