Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems

Autor: Jovana Mijalkovic, Angelo Spognardi

Publikováno v: Algorithms, Vol 15, Iss 8, p 258 (2022)

Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of net

Externí odkaz: https://doaj.org/article/0122bfab5a294deb86599a67cbd60b22

'Who Counterfeited My Viagra?' Probabilistic Item Removal Detection via RFID Tag Cooperation

Autor: Mauro Conti, Roberto Di Pietro, Angelo Spognardi

Publikováno v: EURASIP Journal on Wireless Communications and Networking, Vol 2011 (2011)

Externí odkaz: https://doaj.org/article/19c7bfe38fda4ba6afefba54e90f5d0a

Adversarial machine learning for protecting against online manipulation

Autor: Marinella Petrocchi, Angelo Spognardi, Stefano Cresci, Stefano Tognazzi

Publikováno v: IEEE internet computing (2021).
info:cnr-pdr/source/autori:S. Cresci (1); M. Petrocchi (1); A. Spognardi (2); S. Tognazzi (3)/titolo:Adversarial machine learning for protecting against online manipulation/doi:/rivista:IEEE internet computing/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume

Adversarial examples are inputs to a machine learning system that result in an incorrect output from that system. Attacks launched through this type of input can cause severe consequences: for example, in the field of image recognition, a stop signal

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::181b31a6fcc1321e8320cdfeeffbcd90
http://arxiv.org/abs/2111.12034

DNA-inspired characterization and detection of novel social Twitter spambots

Autor: Marinella Petrocchi, Stefano Cresci, Angelo Spognardi, Roberto Di Pietro, Maurizio Tesconi

Publikováno v: Nature-Inspired Cyber Security and Resiliency: Fundamentals, Techniques and Applications ISBN: 9781785616389

Spambot detection is a must for the protection of cyberspace, in terms of both threats to sensitive information of users and trolls that may want to cheat and influence the public opinion. Unfortunately, new waves of malicious accounts are characteri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b608f1d75a0779784f712d56e6cfa17d
http://hdl.handle.net/11573/1594243

Emergent properties, models, and laws of behavioral similarities within groups of twitter users

Autor: Angelo Spognardi, Marinella Petrocchi, Roberto Di Pietro, Maurizio Tesconi, Stefano Cresci

Publikováno v: Computer communications 150 (2019): 47–61. doi:10.1016/j.comcom.2019.10.019
info:cnr-pdr/source/autori:Cresci S.; Di Pietro R.; Petrocchi M.; Spognardi A.; Tesconi M./titolo:Emergent properties, models, and laws of behavioral similarities within groups of twitter users/doi:10.1016%2Fj.comcom.2019.10.019/rivista:Computer communications/anno:2019/pagina_da:47/pagina_a:61/intervallo_pagine:47–61/volume:150

DNA-inspired online behavioral modeling techniques have been proposed and successfully applied to a broad range of tasks. In this paper, we investigate the fundamental laws that drive the occurrence of behavioral similarities among Twitter users, emp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43b8734ed181b4fb7cf9106d65517a9e
https://publications.cnr.it/doc/424959