Search Results for author: Asaf Shabtai

Found 45 papers, 6 papers with code

A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection

no code implementations19 Sep 2022 Orel Lavie, Asaf Shabtai, Gilad Katz

Many challenging real-world problems require the deployment of ensembles multiple complementary learning models to reach acceptable performance levels.

reinforcement-learning

Adversarial Machine Learning Threat Analysis in Open Radio Access Networks

no code implementations16 Jan 2022 Ron Bitton, Dan Avraham, Eitan Klevansky, Dudu Mimran, Oleg Brodt, Heiko Lehmann, Yuval Elovici, Asaf Shabtai

Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efficiently manage network resources in diverse use cases such as traffic steering, quality of experience prediction, and anomaly detection.

Anomaly Detection BIG-bench Machine Learning

Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model

1 code implementation21 Nov 2021 Alon Zolfi, Shai Avidan, Yuval Elovici, Asaf Shabtai

In our experiments, we examined the transferability of our adversarial mask to a wide range of FR model architectures and datasets.

Face Recognition Real-World Adversarial Attack

Dodging Attack Using Carefully Crafted Natural Makeup

no code implementations14 Sep 2021 Nitzan Guetta, Asaf Shabtai, Inderjeet Singh, Satoru Momiyama, Yuval Elovici

Deep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e. g., airports).

Face Recognition

Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems

no code implementations5 Jul 2021 Ron Bitton, Nadav Maman, Inderjeet Singh, Satoru Momiyama, Yuval Elovici, Asaf Shabtai

Using the extension, security practitioners can apply attack graph analysis methods in environments that include ML components; thus, providing security practitioners with a methodological and practical tool for evaluating the impact and quantifying the risk of a cyberattack targeting an ML production system.

BIG-bench Machine Learning Graph Generation

RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks

no code implementations13 Jun 2021 Shai Cohen, Efrat Levy, Avi Shaked, Tair Cohen, Yuval Elovici, Asaf Shabtai

The proposed technique, which allows the detection of malicious manipulation of critical fields in the data stream, is complemented by a timing-interval anomaly detection mechanism proposed for the detection of message dropping attempts.

Anomaly Detection

TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack

no code implementations10 Mar 2021 Yam Sharon, David Berend, Yang Liu, Asaf Shabtai, Yuval Elovici

Prior research on bypassing NIDSs has mainly focused on perturbing the features extracted from the attack traffic to fool the detection system, however, this may jeopardize the attack's functionality.

Network Intrusion Detection

Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems

no code implementations23 Dec 2020 Moshe Kravchik, Battista Biggio, Asaf Shabtai

With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors.

The Translucent Patch: A Physical and Universal Attack on Object Detectors

no code implementations CVPR 2021 Alon Zolfi, Moshe Kravchik, Yuval Elovici, Asaf Shabtai

Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes.

Autonomous Driving object-detection +1

BENN: Bias Estimation Using Deep Neural Network

no code implementations23 Dec 2020 Amit Giloni, Edita Grolman, Tanja Hagemann, Ronald Fromm, Sebastian Fischer, Yuval Elovici, Asaf Shabtai

The need to detect bias in machine learning (ML) models has led to the development of multiple bias detection methods, yet utilizing them is challenging since each method: i) explores a different ethical aspect of bias, which may result in contradictory output among the different methods, ii) provides an output of a different range/scale and therefore, can't be compared with other methods, and iii) requires different input, and therefore a human expert needs to be involved to adjust each method according to the examined model.

Bias Detection

Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers

no code implementations30 Oct 2020 Tzvika Shapira, David Berend, Ishai Rosenberg, Yang Liu, Asaf Shabtai, Yuval Elovici

The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model.

Malware Detection

Dynamic Adversarial Patch for Evading Object Detection Models

no code implementations25 Oct 2020 Shahar Hoory, Tzvika Shapira, Asaf Shabtai, Yuval Elovici

In order to demonstrate our attack in a real-world setup, we implemented the patches by attaching flat screens to the target object; the screens are used to present the patches and switch between them, depending on the current camera location.

object-detection Object Detection

Approximating Aggregated SQL Queries With LSTM Networks

no code implementations25 Oct 2020 Nir Regev, Lior Rokach, Asaf Shabtai

We use LSTM network to learn the relationship between queries and their results, and to provide a rapid inference layer for predicting query results.

Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations

no code implementations24 Oct 2020 Yael Mathov, Tal Ben Senior, Asaf Shabtai, Yuval Elovici

Our results in the real world suggest that our approach is a feasible solution for privacy protection.

Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders

1 code implementation19 Oct 2020 Elior Nehemya, Yael Mathov, Asaf Shabtai, Yuval Elovici

In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time.

Algorithmic Trading BIG-bench Machine Learning +2

Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples

no code implementations7 Oct 2020 Yael Mathov, Eden Levy, Ziv Katzir, Asaf Shabtai, Yuval Elovici

We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images.

BIG-bench Machine Learning

Adversarial robustness via stochastic regularization of neural activation sensitivity

no code implementations23 Sep 2020 Gil Fidel, Ron Bitton, Ziv Katzir, Asaf Shabtai

Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples.

Adversarial Robustness

FOOD: Fast Out-Of-Distribution Detector

1 code implementation16 Aug 2020 Guy Amit, Moshe Levy, Ishai Rosenberg, Asaf Shabtai, Yuval Elovici

Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs.

OOD Detection Out-of-Distribution Detection

An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions

no code implementations10 Aug 2020 Hodaya Binyamini, Ron Bitton, Masaki Inokuchi, Tomohiko Yagyu, Yuval Elovici, Asaf Shabtai

Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is integrated within MulVAL attack graph tool.

MORTON: Detection of Malicious Routines in Large-Scale DNS Traffic

no code implementations5 Aug 2020 Yael Daihes, Hen Tzaban, Asaf Nadler, Asaf Shabtai

In this paper, we present MORTON, a method that identifies compromised devices in enterprise networks based on the existence of routine DNS communication between devices and disreputable host names.

Cryptography and Security

Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes

no code implementations17 Jul 2020 Yoni Birman, Ziv Ido, Gilad Katz, Asaf Shabtai

In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling.

reinforcement-learning

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

no code implementations5 Jul 2020 Ihai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach

In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security.

Adversarial Attack BIG-bench Machine Learning

Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities

no code implementations30 Jun 2020 Noam Moscovich, Ron Bitton, Yakov Mallah, Masaki Inokuchi, Tomohiko Yagyu, Meir Kalech, Yuval Elovici, Asaf Shabtai

The results show that Autosploit is able to automatically identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.

Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks

no code implementations7 Feb 2020 Moshe Kravchik, Asaf Shabtai

This finding suggests that neural network-based attack detectors used in the cyber-physical domain are more robust to poisoning than in other problem domains, such as malware detection and image processing.

Cyber Attack Detection Data Poisoning +1

GIM: Gaussian Isolation Machines

no code implementations6 Feb 2020 Guy Amit, Ishai Rosenberg, Moshe Levy, Ron Bitton, Asaf Shabtai, Yuval Elovici

In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data.

General Classification Sentiment Analysis

When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures

no code implementations8 Sep 2019 Gil Fidel, Ron Bitton, Asaf Shabtai

We evaluate our method by building an extensive dataset of adversarial examples over the popular CIFAR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs.

Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

no code implementations2 Jul 2019 Moshe Kravchik, Asaf Shabtai

Finally, we study the proposed method's robustness against adversarial attacks, that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect.

Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup

no code implementations31 May 2019 Yair Meidan, Vinay Sachidananda, Yuval Elovici, Asaf Shabtai

Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks.

Privacy Preserving

Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning

no code implementations25 May 2019 Yoni Birman, Shaked Hindi, Gilad Katz, Asaf Shabtai

This security policy is then implemented, and for each inspected file, a different set of detectors is assigned and a different detection threshold is set.

Malware Detection reinforcement-learning

MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses

no code implementations24 Feb 2019 Lior Sidi, Asaf Nadler, Asaf Shabtai

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names through which bots can establish a resilient communication channel with their command and control servers.

Cryptography and Security

Defense Methods Against Adversarial Examples for Recurrent Neural Networks

no code implementations28 Jan 2019 Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach

Using our methods we were able to decrease the effectiveness of such attack from 99. 9% to 15%.

Cryptography and Security

MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks

no code implementations11 Oct 2018 Yotam Intrator, Gilad Katz, Asaf Shabtai

Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data.

Anomaly Detection

Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

no code implementations21 Jun 2018 Moshe Kravchik, Asaf Shabtai

This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks.

Anomaly Detection

N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

1 code implementation9 May 2018 Yair Meidan, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Dominik Breitenbacher, Asaf Shabtai, Yuval Elovici

The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks.

Anomaly Detection

Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers

no code implementations23 Apr 2018 Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach

In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers.

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

3 code implementations25 Feb 2018 Yisroel Mirsky, Tomer Doitshman, Yuval Elovici, Asaf Shabtai

In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.

Network Intrusion Detection

Detection of Unauthorized IoT Devices Using Machine Learning Techniques

no code implementations14 Sep 2017 Yair Meidan, Michael Bohadana, Asaf Shabtai, Martin Ochoa, Nils Ole Tippenhauer, Juan Davis Guarnizo, Yuval Elovici

Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases.

BIG-bench Machine Learning General Classification

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

no code implementations19 Jul 2017 Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e. g., printable strings) that will be misclassified by the classifier without affecting the malware functionality.

BIG-bench Machine Learning

SIPHON: Towards Scalable High-Interaction Physical Honeypots

no code implementations10 Jan 2017 Juan Guarnizo, Amit Tambe, Suman Sankar Bhunia, Martín Ochoa, Nils Tippenhauer, Asaf Shabtai, Yuval Elovici

Based on this setup, six physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months.

Cryptography and Security

Classification of Smartphone Users Using Internet Traffic

no code implementations1 Jan 2017 Andrey Finkelstein, Ron Biton, Rami Puzis, Asaf Shabtai

Today, smartphone devices are owned by a large portion of the population and have become a very popular platform for accessing the Internet.

Classification General Classification

Anomaly Detection Using the Knowledge-based Temporal Abstraction Method

no code implementations14 Dec 2016 Asaf Shabtai

According to the proposed method a temporal pattern mining process is applied on a dataset of basic temporal abstraction database in order to extract patterns representing normal behavior.

Anomaly Detection

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