Mark O'Neill

TLS Proxies: Friend or Foe?

We measure the prevalence and uses of TLS proxies using a Flash tool deployed with a Google AdWords campaign. We generate 2.9 million certificate tests and find that 1 in 250 TLS connections are TLS-proxied. The majority of these proxies appear to be benevolent, however we identify over 1,000 cases where three malware products are using this technology nefariously. We also find numerous instances of negligent, duplicitous, and suspicious behavior, some of which degrade security for users without their knowledge. Distinguishing these types of practices is challenging in practice, indicating a need for transparency and user awareness.

TLS Inspection: How Often and Who Cares?

TLS inspection -- inline decryption, inspection, and re-encryption of TLS traffic -- is a controversial practice used for both benevolent and malicious purposes. This article describes measurements of how often TLS inspection occurs and reports on a survey of the general public regarding the practice of TLS inspection. This helps inform security researchers and policymakers regarding current practices and user preferences.

POSTER: TLS Proxies: Friend or Foe?

The use of TLS proxies to intercept encrypted traffic is controversial since the same mechanism can be used for both benevolent purposes, such as protecting against malware, and for malicious purposes, such as identity theft or warrantless government surveillance. To understand the prevalence and uses of these proxies, we build a TLS proxy measurement tool and deploy it via a Google AdWords campaign. We generate 2.9 million certificate tests and find that 1 in 250 TLS connections are proxied. The majority of these proxies appear to be benevolent, however we identify over 1,000 cases where three malware products are using this technology nefariously. We also find numerous instances of negligent and duplicitous behavior, some of which degrade security for users without their knowledge.

Intrusion Detection with Unsupervised Heterogeneous Ensembles Using Cluster-Based Normalization

Outlier detection has been shown to be a promising machine learning technique for a diverse array of felds and problem areas. However, traditional, supervised outlier detection is not well suited for problems such as network intrusion detection, where proper labelled data is scarce. This has created a focus on extending these approaches to be unsupervised, removing the need for explicit labels, but at a cost of poorer performance compared to their supervised counterparts. Recent work has explored ways of making up for this, such as creating ensembles of diverse models, or even diverse learning algorithms, to jointly classify data. While using unsupervised, heterogeneous ensembles of learning algorithms has been proposed as a viable next step for research, the implications of how these ensembles are built and used has not been explored.