Irad Ben-Gal - Academia.edu (original) (raw)

Papers by Irad Ben-Gal

Springer eBooks, Dec 31, 2022

IEEE Conference Proceedings, 2016

ACM Transactions on Knowledge Discovery From Data, Jul 30, 2022

Chapman and Hall/CRC eBooks, Jun 23, 2015

International Journal of Information Security, Mar 24, 2023

Quality Engineering, 2004

Iie Transactions, Nov 1, 2005

IEEE Transactions on Computational Social Systems, Apr 1, 2022

In this article, we evaluate, for the first time, the potential of a scheduled seeding strategy f... more In this article, we evaluate, for the first time, the potential of a scheduled seeding strategy for influence maximization in a real-world setting. We first propose methods for analyzing historical data to quantify the infection probability of a node with a given set of properties in a given time and assess the potential of a given seeding strategy to infect nodes. Then, we examine the potential of a scheduled seeding strategy by analyzing a real-world large-scale dataset containing both the network topology as well as the nodes’ infection times. Specifically, we use the proposed methods to demonstrate the existence of two important effects in our dataset: a complex contagion effect and a diminishing social influence effect. As shown in a recent study, the scheduled seeding approach is expected to benefit greatly from the existence of these two effects. Finally, we compare a number of benchmark seeding strategies to a scheduled seeding strategy that ranks nodes based on a combination of the number of infectious friends (NIF) they have, as well as the time that has passed since they became infectious. Results of our analyses show that for a seeding budget of 1%, the scheduled seeding strategy yields a convergence rate that is 14% better than a seeding strategy based solely on their degrees, and 215% better than a random seeding strategy, which is often used in practice.

Computers & Security, Aug 1, 2021

Applied Stochastic Models in Business and Industry, 2015

In recent years, with the emergence of big data and online Internet applications, the ability to ... more In recent years, with the emergence of big data and online Internet applications, the ability to classify huge amounts of objects in
a short time has become extremely important. Such a challenge can be achieved by constructing decision trees (DTs) with a low
expected number of tests (ENT).We address this challenge by proposing the ‘save favorable general optimal testing algorithm’ (SFGOTA)
that guarantees, unlike conventional look-ahead DT algorithms, the construction of DTs with monotonic non-increasing
ENT. The proposed algorithm has a lower complexity in comparison to conventional look-ahead algorithms. It can utilize parallel
processing to reduce the execution time when needed. Several numerical studies exemplify how the proposed SF-GOTA generates
efficient DTs faster than standard look-ahead algorithms, while converging to a DT with a minimum ENT.

Proceedings of the 12th International Conference on Agents and Artificial Intelligence, 2020

International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004), 2019

ACM Transactions on Intelligent Systems and Technology, 2017

It has been claimed that many security breaches are often caused by vulnerable (naïve) employees ... more It has been claimed that many security breaches are often caused by vulnerable (naïve) employees within the organization [Ponemon Institute LLC 2015a]. Thus, the weakest link in security is often not the technology itself but rather the people who use it [Schneier 2003]. In this article, we propose a machine learning scheme for detecting risky webpages and risky browsing behavior, performed by naïve users in the organization. The scheme analyzes the interaction between two modules: one represents naïve users, while the other represents risky webpages. It implements a feedback loop between these modules such that if a webpage is exposed to a lot of traffic from risky users, its “risk score” increases, while in a similar manner, as the user is exposed to risky webpages (with a high “risk score”), his own “risk score” increases. The proposed scheme is tested on a real-world dataset of HTTP logs provided by a large American toolbar company. The results suggest that a feedback learning p...

We consider the use of a wireless body area network (WBAN) for remote health monitoring applicati... more We consider the use of a wireless body area network (WBAN) for remote health monitoring applications. A partially observable Markov decision process is used to describe the information flow and behavior of the WBAN. We then discuss a sensor activation policy, used for optimizing the tradeoff between power consumption and probability of patient health state misclassification. In order to determine the underlying health state transition probabilities, by which a patient's health state evolves, we develop a learning algorithm which uses the data collected from a group of patients, each being monitored by a WBAN. Finally, a numerical examination demonstrates the applicability of such a system, which applies the learning process and sensor activation policy simultaneously.

medRxiv (Cold Spring Harbor Laboratory), May 21, 2021