510 research outputs found
MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles
Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applications in various domains, but one of the most important and challenging
is HAR in sports videos. Some of the major issues include high inter- and
intra-class variations, large class imbalance, the presence of both group
actions and single player actions, and recognizing simultaneous actions, i.e.,
the multi-label learning problem. Keeping in mind these challenges and the
recent success of CNNs in solving various computer vision problems, in this
work, we implement a 3D CNN based multi-label deep HAR system for multi-label
class-imbalanced action recognition in hockey videos. We test our system for
two different scenarios: an ensemble of binary networks vs. a single
-output network, on a publicly available dataset. We also compare our
results with the system that was originally designed for the chosen dataset.
Experimental results show that the proposed approach performs better than the
existing solution.Comment: Accepted to IEEE/ACIS SNPD 2018, 6 pages, 3 figure
Enterprise API Security and GDPR Compliance:Design and Implementation Perspective
With the advancements in the enterprise-level business development, the
demand for new applications and services is overwhelming. For the development
and delivery of such applications and services, enterprise businesses rely on
Application Programming Interfaces (APIs). In essence, API is a double-edged
sword. On one hand, API provides ease of expanding the business through sharing
value and utility, but on another hand it raises security and privacy issues.
Since the applications usually use APIs to retrieve important data, therefore
it is extremely important to make sure that an effective access control and
security mechanism are in place , and the data does not fall into wrong hands.
In this article, we discuss the current state of the enterprise API security
and the role of Machine Learning (ML) in API security. We also discuss the
General Data Protection Regulation (GDPR) compliance and its effect on the API
security.Comment: 7 page
On M2M Micropayments : A Case Study of Electric Autonomous Vehicles
The proliferation of electric vehicles has spurred the research interest in
technologies associated with it, for instance, batteries, and charging
mechanisms. Moreover, the recent advancements in autonomous cars also encourage
the enabling technologies to integrate and provide holistic applications. To
this end, one key requirement for electric vehicles is to have an efficient,
secure, and scalable infrastructure and framework for charging, billing, and
auditing. However, the current manual charging systems for EVs may not be
applicable to the autonomous cars that demand new, automatic, secure,
efficient, and scalable billing and auditing mechanism. Owing to the
distributed systems such as blockchain technology, in this paper, we propose a
new charging and billing mechanism for electric vehicles that charge their
batteries in a charging-on-the-move fashion. To meet the requirements of
billing in electric vehicles, we leverage distributed ledger technology (DLT),
a distributed peer-to-peer technology for micro-transactions. Our
proof-of-concept implementation of the billing framework demonstrates the
feasibility of such system in electric vehicles. It is also worth noting that
the solution can easily be extended to the electric autonomous cars (EACs)
Machine Learning in IoT Security:Current Solutions and Future Challenges
The future Internet of Things (IoT) will have a deep economical, commercial
and social impact on our lives. The participating nodes in IoT networks are
usually resource-constrained, which makes them luring targets for cyber
attacks. In this regard, extensive efforts have been made to address the
security and privacy issues in IoT networks primarily through traditional
cryptographic approaches. However, the unique characteristics of IoT nodes
render the existing solutions insufficient to encompass the entire security
spectrum of the IoT networks. This is, at least in part, because of the
resource constraints, heterogeneity, massive real-time data generated by the
IoT devices, and the extensively dynamic behavior of the networks. Therefore,
Machine Learning (ML) and Deep Learning (DL) techniques, which are able to
provide embedded intelligence in the IoT devices and networks, are leveraged to
cope with different security problems. In this paper, we systematically review
the security requirements, attack vectors, and the current security solutions
for the IoT networks. We then shed light on the gaps in these security
solutions that call for ML and DL approaches. We also discuss in detail the
existing ML and DL solutions for addressing different security problems in IoT
networks. At last, based on the detailed investigation of the existing
solutions in the literature, we discuss the future research directions for ML-
and DL-based IoT security
An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles
Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve
transportation issues such as accidents, emissions and congestion, and aim at
establishing the foundation of Machine-to-Machine (M2M) economy. For this to be
possible, the market should be able to offer appropriate charging services
without involving humans. The state-of-the-art mechanisms of charging and
billing do not meet this requirement, and often impose service fees for value
transactions that may also endanger users and their location privacy. This
paper aims at filling this gap and envisions a new charging architecture and a
billing framework for EAV which would enable M2M transactions via the use of
Distributed Ledger Technology (DLT)
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