Dr. Eli Brosh

AI and networks research


AI and networks research


Poster and brief announcement
Anat Bremler-Barr and Michael Czeizler

Auto-scaling is a fundamental capability of cloud computing which allows consuming resources dynamically according to changing traffic needed to be served.
By the micro-services architecture paradigm, software systems are built as a set of loosely-coupled applications and services that can be individually scaled.
In this paper, we present a new attack the \emph{Tandem Attack} that exploits the Tandem behavior of micro-services with different scaling properties. Such issues can result in Denial of Service (DoS) and Economic Denial of Sustainability (EDoS) created by malicious attackers or self-inflicted due to wrong configurations set up by administrators. We demonstrate the Tandem attack using a popular AWS serverless infrastructure modeling two services and show that removing servers’ management responsibility from the cloud users does not mitigate the different scaling properties challenge and can even make the problem harder to solve.

Conferences & Workshops
Yehuda Afek, Anat Bremler-Barr, Dor Israeli and Alon Noy
The International Symposium on Cyber Security, Cryptology and Machine Learning (CSCML),

This paper presents a new localhost browser based vulnerability and corresponding attack that opens the door to new attacks on private networks and local devices. We show that this new vulnerability may put hundreds of millions of internet users and their IoT devices at risk. Following the attack presentation, we suggest three new protection mechanisms to mitigate this vulnerability.
This new attack bypasses recently suggested protection mechanisms designed to stop browser-based attacks on private devices and local applications.

Conferences & Workshops
Anat Bremler-Barr, Matan Sabag
IFIP Networking,

Distributed denial of service (DDoS) attacks, especially distributed reflection denial of service attacks (DRDoS), have increased dramatically in frequency and volume in recent years. Such attacks are possible due to the attacker’s ability to spoof the source address of IP packets. Since the early days of the internet, authenticating the IP source address has remained unresolved in the real world. Although there are many methods available to eliminate source spoofing, they are not widely used, primarily due to a lack of economic incentives.
We propose a collaborative on-demand route-based defense technique (CORB) to offer efficient DDoS mitigation as a paid-for-service, and efficiently assuage reflector attacks before they reach the reflectors and flood the victim. The technique uses scrubbing facilities located across the internet at internet service providers (ISPs) and internet exchange points (IXPs).
By transmitting a small amount of data based on border gateway protocol (BGP) information from the victim to the scrubbing facilities, we can filter out the attack without any false-positive cases. For example, the data can be sent using DOTS, a new signaling DDoS protocol that was standardized by the IETF. CORB filters the attack before it is amplified by the reflector, thereby reducing the overall cost of the attack. This provides a win-win financial situation for the victim and the scrubbing facilities that provide the service.
We demonstrate the value of CORB by simulating a Memcached DRDoS attack using real-life data. Our evaluation found that deploying CORB on scrubbing facilities at approximately 40 autonomous systems blocks 90% of the attack and can reduce the mitigation cost by 85%.

Poster and brief announcement
Anat Bremler-Barr, Bar Meyuhas, Ran Shister

The Manufacturer Usage Description (MUD) is an IETF white-list protection scheme that formalizes the authorized network behavior in a MUD file; this MUD file can then be used as a type of firewall mechanism.

This demo introduces MUDIS, a MUD Inspection System that inspects the network behavior of devices, based on their formal description in the MUD file. We present several use-cases in which MUDIS is useful, including examining the impact of device location, the impact of a firmware update, the correlation of network behavior between different devices of the same manufacture, and more.

MUDIS inspects two MUD files, clusters together and graph- ically visualizes identical, similar, and dissimilar rules. It then calculates a similarity score that measures the similarity between them both. It also generalizes the two MUD files where possible, such that the resulting generalized MUD covers all the permitted (white-list) network behavior for both MUDs.

Our open-source MUDIS tool and proof-of-concept dataset are available for researchers and IoT manufacturers, allowing anyone to gain meaningful insights over the network behavior of IoT devices.

Conferences & Workshops
Eli Brosh, Elad Wasserstein, Anat Bremler-Barr
IEEE/IFIP NOMS Manage-IoT workshop ,

Monitoring medical data, e.g., Electrocardiogram (ECG) signals, is a common application of Internet of Things (IoT) devices. Compression methods are often applied on the massive amounts of sensor data generated prior to sending it to the Cloud to reduce the storage and delivery costs. A lossy compression provides high compression gain (CG), but may reduce the performance of an ECG application (downstream task) due to information loss. Previous works on ECG monitoring focus either on optimizing the signal reconstruction or the task’s performance. Instead, we advocate a self-adapting lossy compression solution that allows configuring a desired performance level on the downstream tasks while maintaining an optimized CG that reduces Cloud costs.
We propose Dynamic-Deep, a task-aware compression geared for IoT-Cloud architectures. Our compressor is trained to optimize the CG while maintaining the performance requirement of the downstream tasks chosen out of a wide range. In deployment, the IoT edge device adapts the compression and sends an optimized representation for each data segment, accounting for the downstream task’s desired performance without relying on feedback from the Cloud. We conduct an extensive evaluation of our approach on common ECG datasets using two popular ECG applications, which includes heart rate (HR) arrhythmia classification. We demonstrate that Dynamic-Deep can be configured to improve HR classification F1-score in a wide range of requirements. One of which is tuned to improve the F1-score by 3 and increases CG by up to 83% compared to the previous state of-the-art (autoencoder-based) compressor. Analyzing DynamicDeep on the Google Cloud Platform, we observe a 97% reduction in cloud costs compared to a no compression solution. To the best of our knowledge, Dynamic-Deep is the first end-to end system architecture proposal to focus on balancing the need for high performance of cloud-based downstream tasks and the desire to achieve optimized compression in IoT ECG monitoring settings.