25,960 research outputs found
Recommended from our members
The THREAT-ARREST Cyber-Security Training Platform
Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed
Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data Networking
Content-Centric Networking (CCN) is an emerging networking paradigm being
considered as a possible replacement for the current IP-based host-centric
Internet infrastructure. In CCN, named content becomes a first-class entity.
CCN focuses on content distribution, which dominates current Internet traffic
and is arguably not well served by IP. Named-Data Networking (NDN) is an
example of CCN. NDN is also an active research project under the NSF Future
Internet Architectures (FIA) program. FIA emphasizes security and privacy from
the outset and by design. To be a viable Internet architecture, NDN must be
resilient against current and emerging threats. This paper focuses on
distributed denial-of-service (DDoS) attacks; in particular we address interest
flooding, an attack that exploits key architectural features of NDN. We show
that an adversary with limited resources can implement such attack, having a
significant impact on network performance. We then introduce Poseidon: a
framework for detecting and mitigating interest flooding attacks. Finally, we
report on results of extensive simulations assessing proposed countermeasure.Comment: The IEEE Conference on Local Computer Networks (LCN 2013
Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks
Vehicular adhoc networks allow vehicles to share their information for safety
and traffic efficiency. However, sharing information may threaten the driver
privacy because it includes spatiotemporal information and is broadcast
publicly and periodically. In this paper, we propose a context-adaptive
pseudonym changing scheme which lets a vehicle decide autonomously when to
change its pseudonym and how long it should remain silent to ensure
unlinkability. This scheme adapts dynamically based on the density of the
surrounding traffic and the user privacy preferences. We employ a multi-target
tracking algorithm to measure privacy in terms of traceability in realistic
vehicle traces. We use Monte Carlo analysis to estimate the quality of service
(QoS) of a forward collision warning application when vehicles apply this
scheme. According to the experimental results, the proposed scheme provides a
better compromise between traceability and QoS than a random silent period
scheme.Comment: Extended version of a previous paper "K. Emara, W. Woerndl, and J.
Schlichter, "Poster: Context-Adaptive User-Centric Privacy Scheme for VANET,"
in Proceedings of the 11th EAI International Conference on Security and
Privacy in Communication Networks, SecureComm'15. Dallas, TX, USA: Springer,
June 2015.
Intrusion Detection System for Platooning Connected Autonomous Vehicles
The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks
Acoustic Integrity Codes: Secure Device Pairing Using Short-Range Acoustic Communication
Secure Device Pairing (SDP) relies on an out-of-band channel to authenticate
devices. This requires a common hardware interface, which limits the use of
existing SDP systems. We propose to use short-range acoustic communication for
the initial pairing. Audio hardware is commonly available on existing
off-the-shelf devices and can be accessed from user space without requiring
firmware or hardware modifications. We improve upon previous approaches by
designing Acoustic Integrity Codes (AICs): a modulation scheme that provides
message authentication on the acoustic physical layer. We analyze their
security and demonstrate that we can defend against signal cancellation attacks
by designing signals with low autocorrelation. Our system can detect
overshadowing attacks using a ternary decision function with a threshold. In
our evaluation of this SDP scheme's security and robustness, we achieve a bit
error ratio below 0.1% for a net bit rate of 100 bps with a signal-to-noise
ratio (SNR) of 14 dB. Using our open-source proof-of-concept implementation on
Android smartphones, we demonstrate pairing between different smartphone
models.Comment: 11 pages, 11 figures. Published at ACM WiSec 2020 (13th ACM
Conference on Security and Privacy in Wireless and Mobile Networks). Updated
reference
- …