activity
20162023
most citedSupervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review

183 citations · 463 across the 13 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cs.SI2020★ 11 cited

Correlations among Game of Thieves and other centrality measures in complex networks

Annamaria Ficara, Giacomo Fiumara, Pasquale De Meo +1

Social Network Analysis (SNA) is used to study the exchange of resources among individuals, groups, or organizations. The role of individuals or connections in a network is describ…

cs.LG2020

Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review

L. Erhan, M. Ndubuaku, M. Di Mauro +5

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of ap…

cs.NI2020★ 26 cited

Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach

Mario Di Mauro, Antonio Liotta

The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provisioning p…

cs.NI2020★ 20 cited

An experimental evaluation and characterization of VoIP over an LTE-A network

Mario Di Mauro, Antonio Liotta

Mobile telecommunications are converging towards all-IP solutions. This is the case of the Long Term Evolution (LTE) technology that, having no circuit-switched bearer to support v…

cs.NI2020★ 92 cited

Experimental Review of Neural-based approaches for Network Intrusion Management

Mario Di Mauro, Giovanni Galatro, Antonio Liotta

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number…

cs.SI2020

Network connectivity under a probabilistic node failure model

Lucia Cavallaro, Stefania Costantini, Pasquale De Meo +2

Centrality metrics have been widely applied to identify the nodes in a graph whose removal is effective in decomposing the graph into smaller sub-components. The node--removal proc…