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Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
Moshe Kravchik, Battista Biggio, Asaf Shabtai
Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detecto…
BENN: Bias Estimation Using Deep Neural Network
Amit Giloni, Edita Grolman, Tanja Hagemann +4
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) explo…
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Alon Zolfi, Moshe Kravchik, Yuval Elovici +1
Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order…
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
Tzvika Shapira, David Berend, Ishai Rosenberg +3
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training s…
Dynamic Adversarial Patch for Evading Object Detection Models
Shahar Hoory, Tzvika Shapira, Asaf Shabtai +1
Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real-worl…
Approximating Aggregated SQL Queries With LSTM Networks
Nir Regev, Lior Rokach, Asaf Shabtai
Despite continuous investments in data technologies, the latency of querying data still poses a significant challenge. Modern analytic solutions require near real-time responsivene…
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
Yael Mathov, Tal Ben Senior, Asaf Shabtai +1
Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy. These automated systems use learning models to analyze conversations, and calls that…
Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
Elior Nehemya, Yael Mathov, Asaf Shabtai +1
In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market'…
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
Yael Mathov, Eden Levy, Ziv Katzir +2
Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these do…
Adversarial robustness via stochastic regularization of neural activation sensitivity
Gil Fidel, Ron Bitton, Ziv Katzir +1
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples. Thus we can no longer hope to immune classifiers against…
Fair and accurate age prediction using distribution aware data curation and augmentation
Yushi Cao, David Berend, Palina Tolmach +5
Deep learning-based facial recognition systems have experienced increased media attention due to exhibiting unfair behavior. Large enterprises, such as IBM, shut down their facial…
An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
Hodaya Binyamini, Ron Bitton, Masaki Inokuchi +3
Attack graphs are one of the main techniques used to automate the risk assessment process. In order to derive a relevant attack graph, up-to-date information on known attack techni…
FOOD: Fast Out-Of-Distribution Detector
Guy Amit, Moshe Levy, Ishai Rosenberg +2
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. However, out-of-dis…
MORTON: Detection of Malicious Routines in Large-Scale DNS Traffic
Yael Daihes, Hen Tzaban, Asaf Nadler +1
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 disrep…
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes
Yoni Birman, Ziv Ido, Gilad Katz +1
Multi-objective task scheduling (MOTS) is the task scheduling while optimizing multiple and possibly contradicting constraints. A challenging extension of this problem occurs when…
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Ihai Rosenberg, Asaf Shabtai, Yuval Elovici +1
In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learni…
Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities
Noam Moscovich, Ron Bitton, Yakov Mallah +5
The existence of a security vulnerability in a system does not necessarily mean that it can be exploited. In this research, we introduce Autosploit -- an automated framework for ev…
GIM: Gaussian Isolation Machines
Guy Amit, Ishai Rosenberg, Moshe Levy +3
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be c…
DANTE: A framework for mining and monitoring darknet traffic
Dvir Cohen, Yisroel Mirsky, Yuval Elovici +4
Trillions of network packets are sent over the Internet to destinations which do not exist. This 'darknet' traffic captures the activity of botnets and other malicious campaigns ai…
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
Moshe Kravchik, Asaf Shabtai
In recent years, a variety of effective neural network-based methods for anomaly and cyber attack detection in industrial control systems (ICSs) have been demonstrated in the liter…