CADeSH: Collaborative Anomaly Detection for Smart Homes

no code implementations • 2 Mar 2023 • Yair Meidan, Dan Avraham, Hanan Libhaber, Asaf Shabtai

To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (`benign') and infrequent (possibly `malicious') traffic flows.

Anomaly Detection Intrusion Detection +1

Attacking Object Detector Using A Universal Targeted Label-Switch Patch

no code implementations • 16 Nov 2022 • Avishag Shapira, Ron Bitton, Dan Avraham, Alon Zolfi, Yuval Elovici, Asaf Shabtai

However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object.

Object

Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation

no code implementations • 20 Jan 2022 • Hilla Shinitzky, Yuval Shahar, Dan Avraham, Yizhak Vaisman, Yakir Tsizer, Yaniv Leedon

To evaluate our methodology, we collected 2490 responses for different tasks, which we used for feature engineering and for the training of ML models.

Decision Making Feature Engineering

Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)

no code implementations • 16 Jan 2022 • Edan Habler, Ron Bitton, Dan Avraham, Dudu Mimran, Eitan Klevansky, Oleg Brodt, Heiko Lehmann, Yuval Elovici, Asaf Shabtai

Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model.

Anomaly Detection BIG-bench Machine Learning