Search Results for author: Danny Hendler

Found 9 papers, 6 papers with code

Machine-Learning Based Objective Function Selection for Community Detection

1 code implementation25 Mar 2022 Asa Bornstein, Amir Rubin, Danny Hendler

In this work, we present NECTAR-ML, an extension of the NECTAR algorithm that uses a machine-learning based model for automating the selection of the objective function, trained and evaluated on a dataset of 15, 755 synthetic and 7 real-world networks.

BIG-bench Machine Learning Community Detection

Early Detection of In-Memory Malicious Activity based on Run-time Environmental Features

no code implementations30 Mar 2021 Dorel Yaffe, Danny Hendler

We present a novel end-to-end solution for in-memory malicious activity detection done prior to exploitation by leveraging machine learning capabilities based on data from unique run-time logs, which are carefully curated in order to detect malicious activity in the memory of protected processes.

Action Detection Activity Detection +2

Flat-Combining-Based Persistent Data Structures for Non-Volatile Memory

no code implementations23 Dec 2020 Matan Rusanovsky, Ohad Ben-Baruch, Danny Hendler, Pedro Ramalhete

Flat combining (FC) is a synchronization paradigm in which a single thread, holding a global lock, collects requests by multiple threads for accessing a concurrent data structure and applies their combined requests to it.

Distributed, Parallel, and Cluster Computing Operating Systems

DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining

1 code implementation4 Aug 2020 Ron Korine, Danny Hendler

Moreover, malware classification facilitates determining which of the newly discovered variants should undergo manual analysis by a security expert, in order to determine whether they belong to a new family (e. g., one whose members exploit a zero-day vulnerability) or are simply the result of a concept drift within a known malicious family.

Classification General Classification +1

Towards Federated Learning With Byzantine-Robust Client Weighting

1 code implementation10 Apr 2020 Amit Portnoy, Yoav Tirosh, Danny Hendler

Federated Learning(FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by a central server.

Federated Learning

AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings

no code implementations23 May 2019 Amir Rubin, Shay Kels, Danny Hendler

In this work, we conduct the first study of malicious PowerShell code detection using the information made available by AMSI.


Detecting Malicious PowerShell Commands using Deep Neural Networks

1 code implementation11 Apr 2018 Danny Hendler, Shay Kels, Amir Rubin

Microsoft's PowerShell is a command-line shell and scripting language that is installed by default on Windows machines.

Node-Centric Detection of Overlapping Communities in Social Networks

1 code implementation6 Jul 2016 Yehonatan Cohen, Danny Hendler, Amir Rubin

We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures.

Social and Information Networks Physics and Society

A Dynamic Elimination-Combining Stack Algorithm

1 code implementation30 Jun 2011 Gal Bar-Nissan, Danny Hendler, Adi Suissa

Software combining, on the other hand, is effective when colliding operations have identical semantics: when a pair of threads performing operations with identical semantics collide, the task of performing the combined set of operations is delegated to one of the threads and the other thread waits for its operation(s) to be performed.

Distributed, Parallel, and Cluster Computing

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