Prof. David Hay

Manufacturer Usage Description (MUD) is a new, whitelist-based cybersecurity framework that was recently proposed by the IETF to cope with the huge attack surface and a constantly increasing number of IoT devices connected to the Internet.
MUD allows the IoT manufacturers themselves to publish the legitimate communication patterns of their devices, making it easier for security devices to enforce this policy, filter out non-complying traffic, and block a device in case it has been compromised.
Typically, MUD includes a set of legitimate endpoints, specified either by domain names or by IP addresses, along with the legitimate port numbers and protocols. While these descriptions are adequate when IoT devices connect (as clients) to servers (e.g., services in the cloud), they cannot adequately describe the cases where IoT devices act as servers to which endpoints connect [1]. These endpoints (e.g., users’ mobile devices) typically do not have fixed IP addresses, nor do they associate with a domain name. In this case, accounting for 78% of IoT devices we have surveyed, MUD degrades nowadays to allow all possible endpoints and cannot mitigate any attack. In this work, we evaluate this phenomenon and show it has a high prevalence today, thus harming dramatically the MUD framework security efficiency. We then present a solution, MUDirect, which enhances the MUD framework to deal with these cases while preserving the current MUD specification. Finally, we have implemented our solution (extending the existing osMUD implementation [2]) and showed that it enables P2P IoT devices protection while having minimal changes to the osMUD code.

A new scalable ISP level system architecture to secure and protect all IoT devices in a large number of homes is presented. The system is based on whitelisting, as in the Manufacturer Usage Description (MUD) framework, implemented as a VNF. Unlike common MUD suggestions that place the whitelist application at the home/enterprise network, our approach is to place the enforcement upstream at the provider network, combining an NFV (Network Function Virtualization) with router/switching filtering capabilities, e.g., ACLs. The VNF monitors many home networks simultaneously, and therefore, is a highly-scalable managed service solution that provides both the end customers and the ISP with excellent visibility and security of the IoT devices at the customer premises.
The system includes a mechanism to distinguish between flows of different devices at the ISP level despite the fact that most home networks (and their IoT devices) are behind a NAT and all the flows from the same home come out with the same source IP address. Moreover, the NFV system needs to receive only the first packet of each connection at the VNF, and rules space is proportional to the number of unique types of IoT devices rather than the number of IoT devices. The monitoring part of the solution is off the critical path and can also uniquely protect from incoming DDoS attacks.
To cope with internal traffic, that is not visible outside the customer premise and often consists of P2P communication, we suggest a hybrid approach, where we deploy a lightweight component at the CPE, whose sole purpose is to monitor P2P communication. As current MUD solution does not provide a secure solution to P2P communication, we also extend the MUD protocol to deal also with peer-to-peer communicating devices. A PoC with a large national level ISP proves that our technology works as expected.

This demo focuses on demonstrating features of a new system to protect IoT devices in customer premises at the ISP level. The core of the system is deployed as a Virtual Network Function (VNF) within the ISP network, and is based on the Manufacturer Usage Description (MUD) framework, a white-list IoT protection scheme that has been proposed in recent years.
As MUD is designed for on-premise deployment, the system makes the necessary adaptations to enable its deployment outside the customer premise. Moreover, the system includes a mechanism to distinguish between flows of different devices at the ISP level despite the fact that most home networks (and their IoT devices) are behind a NAT and all the flows from the same home come out with the same source IP address.
Our demo follows closely a proof-of-concept that we have done with a large national level ISP, showing how our system can identify the various IoT devices that are connected to the network
and detecting any unauthorized communications.

Memcached is an in-memory key-value distributed
caching solution, commonly used by web servers for fast content delivery. Keys with their values are distributed between Memcached servers using a consistent hashing technique, resulting in an even distribution (of keys) among the servers. However, as small number of keys are significantly more popular than others (a.k.a., hot keys), even distribution of keys may cause a significantly different request load on the Memcached servers, which, in turn, causes substantial performance degradation.
Previous solutions to this problem require complex application level solutions and extra servers. In this paper, we propose MBalancer–a simple L7 load balancing scheme for Memcached that can be seamlessly integrated into Memcached architectures running over Software-Defined Networks (SDN). In a nutshell, MBalancer runs as an SDN application and duplicates the hot keys to many (or all) Memcached servers. The SDN controller updates the SDN switches forwarding tables and uses SDN readymade load balancing capabilities. Thus, no change is required to 1Memcached clients or servers.
Our analysis shows that with minimal overhead for storing
a few extra keys, the number of requests per server is close to balanced (assuming requests for keys follows a Zipf distribution).
Moreover, we have implemented MBalancer on a hardware based OpenFlow switch. As MBalancer offloads requests from bottleneck Memcached servers, our experiments show that it achieves significant throughput boost and latency reduction.

URL matching lies at the core of many networking applications and Information Centric Networking architectures. For example, URL matching is extensively used by Layer 7 switches, ICN/NDN routers, load balancers, and security devices. Modern URL matching is done by maintaining a rich database that consists of tens of millions of URL which are classified to dozens of categories (or egress ports). In real-time, any input URL has to be searched in this database to find the corresponding category.
In this paper, we introduce a generic framework for accurate URL matching (namely, no false positives or miscategorization) that aims to reduce the overall memory footprint, while still having low matching latency. We introduce a dictionary-based compression method that compresses the database by 60%, while having only a slight overhead in time. Our framework is very flexible and it allows hot-updates,
cloud-based deployments, and can deal with strings that are not URLs.