A Methodological Approach for Assessing Amplified Reflection Distributed Denial of Service on the Internet of Things.
Concerns about security on Internet of Things (IoT) cover data privacy and integrity, access control, and availability. IoT abuse in distributed denial of service attacks is a major issue, as typical IoT devices' limited computing, communications, and power resources are prioritized in implementing functionality rather than security features. Incidents involving attacks have been reported, but without clear characterization and evaluation of threats and impacts. The main purpose of this work is to methodically assess the possible impacts of a specific class-amplified reflection distributed denial of service attacks (AR-DDoS)-against IoT. The novel approach used to empirically examine the threat represented by running the attack over a controlled environment, with IoT devices, considered the perspective of an attacker. The methodology used in tests includes that perspective, and actively prospects vulnerabilities in computer systems. This methodology defines standardized procedures for tool-independent vulnerability assessment based on strategy, and the decision flows during execution of penetration tests (pentests). After validation in different scenarios, the methodology was applied in amplified reflection distributed denial of service (AR-DDoS) attack threat assessment. Results show that, according to attack intensity, AR-DDoS saturates reflector infrastructure. Therefore, concerns about AR-DDoS are founded, but expected impact on abused IoT infrastructure and devices will be possibly as hard as on final victims.
Electrical Engineering Department, University of Brasília, Campus Universitário Darci Ribeiro, 70919-970 Brasília DF, Brazil. gondim@unb.br.
João José
JJ
Costa Gondim
Electrical Engineering Department, University of Brasília, Campus Universitário Darci Ribeiro, 70919-970 Brasília DF, Brazil. robson@redes.unb.br.
Robson
R
de Oliveira Albuquerque
Electrical Engineering Department, University of Brasília, Campus Universitário Darci Ribeiro, 70919-970 Brasília DF, Brazil. andclay@ene.unb.br.|Center for Data Science, Institute of Technology, University of Washington, Tacoma, WA 98402-3100, USA. andclay@ene.unb.br.
Anderson
A
Clayton Alves Nascimento
Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain. javiergv@fdi.ucm.es.
Luis Javier
LJ
García Villalba
Department of Convergence Security, Sungshin Women's University, 249-1 Dongseon-Dong 3-ga, Seoul 136-742, Korea. taihoonn@daum.net.
Tai-Hoon
TH
Kim
Amplified Reflection; Distributed Denial of Service; Pentest; Risk Management; Vulnerability Assessment