Publications VS: Bibliography 2020 BibTeX
@inproceedings {INPROC-2020-36,
author = {Milan Tepic\&\#769; and Mohamed Abdelaal and Marc Weber and Kurt Rothermel},
title = {{AutoSec: Multidimensional Timing-Based Anomaly Detection for Automotive Cybersecurity}},
booktitle = {Proceedings of the 26th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’20), August 2020},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--10},
type = {Conference Paper},
month = {August},
year = {2020},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-36/INPROC-2020-36.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Nowadays, autonomous driving and driver assistance applications are being
developed at an accelerated pace. This rapid growth is primarily driven by the
potential of such smart applications to significantly improve safety on public
roads and offer new possibilities for modern transportation concepts. Such
indispensable applications typically require wireless connectivity between the
vehicles and their surroundings, i.e. roadside infrastructure and cloud
services. Nevertheless, such connectivity to external networks exposes the
internal systems of individual vehicles to threats from remotely-launched
attacks. In this realm, it is highly crucial to identify any misbehavior of the
software components which might occur owing to either these threats or even
software/hardware malfunctioning.
In this paper, we introduce $\backslash$PaperAcronym, a host-based anomaly detection
algorithm which relies on observing four timing parameters of the executed
software components to accurately detect malicious behavior on the operating
system level. To this end, $\backslash$PaperAcronym formulates the task of detecting
anomalistic executions as a clustering problem. Specifically, $\backslash$PaperAcronym
devises a hybrid clustering algorithm for grouping a set of collected timing
traces resulted from executing the legitimate code. During the runtime,
$\backslash$PaperAcronym simply classifies a certain execution as an anomaly, if its
timing parameters are distant enough from the boundaries of the predefined
clusters. To show the effectiveness of $\backslash$PaperAcronym, we collected timing
traces from a testbed composed of a set of real and virtual control units
communicating over a CAN bus. We show that using our proposed $\backslash$PaperAcronym,
compared to baseline methods, we can identify up to 21$\backslash$\% less false positives
and 18$\backslash$\% less false negatives.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-36&engl=1}
}
@inproceedings {INPROC-2020-29,
author = {Ahmad Slo and Sukanya Bhowmik and Kurt Rothermel},
title = {{hSPICE: State-Aware Event Shedding in Complex Event Processing}},
booktitle = {Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems (DEBS '20), July 13--17, 2020, Virtual Event, QC, Canada.},
publisher = {ACM},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--12},
type = {Conference Paper},
month = {July},
year = {2020},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-29/INPROC-2020-29.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {In complex event processing (CEP), load shedding is performed to maintain a
given latency bound during overload situations when there is a limitation on
resources. However, shedding load implies degradation in the quality of results
(QoR). Therefore, it is crucial to perform load shedding in a way that has the
lowest impact on QoR. Researchers, in the CEP domain, propose to drop either
events or partial matches (PMs) in overload cases. They assign utilities to
events or PMs by considering either the importance of events or the importance
of PMs but not both together. In this paper, we propose a load shedding
approach for CEP systems that combines these approaches by assigning a utility
to an event by considering both the event importance and the importance of PMs.
We adopt a probabilistic model that uses the type and position of an event in a
window and the state of a PM to assign a utility to an event corresponding to
each PM. We, also, propose an approach to predict a utility threshold that is
used to drop the required amount of events to maintain a given latency bound.
By extensive evaluations on two real-world datasets and several representative
queries, we show that, in the majority of cases, our load shedding approach
outperforms state-of-the-art load shedding approaches, w.r.t. QoR.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-29&engl=1}
}
@inproceedings {INPROC-2020-28,
author = {Jonathan Falk and Frank D{\"u}rr and Kurt Rothermel},
title = {{Time-Triggered Traffic Planning for Data Networks with Conflict Graphs}},
booktitle = {26th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2020)},
address = {Sydney, Australia},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
type = {Conference Paper},
month = {April},
year = {2020},
keywords = {Real-Time; Traffic-Planning; Time-Triggered},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-28/INPROC-2020-28.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Traffic planning is the key enabler of time-triggered real-time communication
in distributed systems, and it is known to be notoriously hard. Current
approaches predominantly tackle the problem in the domain of the traffic
planning problem, e.g., by formulating constraints on the transmission
schedules for individual data streams, or the links used by the data streams.
This results in a high degree of coupling of the configuration of an individual
data stream and the global (network-wide) traffic configuration with
detrimental effects on the scalability and runtime of the planning phase.
In contrast, we present a configuration-conflict graph based approach, which
solves the original traffic planning problem by searching an independent vertex
set in the conflict graph. We show how to derive the configuration-conflict
graph, and discuss the conceptual advantages of this approach. To show the
practical advantages of the conflict-graph based traffic planning approach we
additionally present a proof-of-concept implementation and evaluate it against
a reference ILP-based implementation. In our evaluations, our proof-of-concept
implementation of the conflict-graph based approach outperforms the reference
ILP and is more memory efficient, making it a promising alternative to current
constraint-based traffic planning approaches.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-28&engl=1}
}
@inproceedings {INPROC-2020-05,
author = {Ben William Carabelli and Frank D{\"u}rr and Kurt Rothermel},
title = {{SCRaM -- State-Consistent Replication Management for Networked Control Systems}},
booktitle = {11th IEEE/ACM International Conference on Cyber-Physical Systems (ICCPS)},
address = {Sydney, NSW, Australia},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
type = {Conference Paper},
month = {April},
year = {2020},
language = {English},
cr-category = {C.2.4 Distributed Systems,
C.4 Performance of Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-05/INPROC-2020-05.pdf},
contact = {Ben Carabelli ben.carabelli@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Networked control systems (NCS) consist of sensors and actuators that are
connected to a controller through a packet-switched network in a feedback loop
to control physical systems in diverse application areas such as industry,
automotive, or power infrastructure. The control of critical real-time systems
places strong requirements on the latency and reliability of both the
communication network and the controller. In this paper, we consider the
problem of increasing the reliability of an NCS subject to crash failures and
message loss by replicating the controller component. Previous replication
schemes for real-time systems have focused on ensuring that no conflicting
values are sent to the actuators by different replicas. Since this property,
which we call output consistency, only refers to the values within one time
step, it is insufficient for reasoning about the formal conditions under which
a group of replicated controllers behaves equivalent to a non-replicated
controller. Therefore, we propose the stronger state consistency property,
which ensures that the sequence of values produced by the replicated controller
exhibits the same dynamical behaviour as a non-replicated controller. Moreover,
we present SCRaM, a protocol for replicating generic periodically sampled
controllers that satisfies both of these consistency requirements. To
demonstrate the effectiveness of our approach, we evaluated it experimentally
for the control of a cart-driven inverted pendulum.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-05&engl=1}
}
@inproceedings {INPROC-2020-02,
author = {David Hellmanns and Jonathan Falk and Alexander Glavackij and Ren{\'e} Hummen and Stephan Kehrer and Frank D{\"u}rr},
title = {{On the Performance of Stream-based, Class-based Time-aware Shaping and Frame Preemption in TSN}},
booktitle = {Proceedings of 2020 IEEE International Conference on Industrial Technology (ICIT), Buenos Aires, Argentinia, February 26–28, 2020},
address = {Buenos Aires},
publisher = {IEEE Xplore},
institution = {University of Stuttgart, Faculty of Computer Science, Germany},
pages = {1--6},
type = {Conference Paper},
month = {February},
year = {2020},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-02/INPROC-2020-02.pdf},
contact = {david.hellmanns@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed High-Performance Systems, Distributed Systems},
abstract = {Time-sensitive Networking (TSN) is an evolving group of IEEE standards for
deterministic real-time communication making standard Ethernet technology
applicable to safety-critical application domains such as manufacturing or
automotive systems. TSN includes several mechanisms influencing the timely
forwarding of traffic, in particular, a time-triggered scheduling mechanism
called time-aware shaper (TAS) and frame preemption to reduce the blocking time
of high-priority traffic by low-priority traffic. Although these mechanisms
have been standardized and products implementing them begin to enter the
market, it is still hard for practitioners to select and apply suitable
mechanisms fitting the problem at hand. For instance, TAS schedules can be
calculated for individual streams or classes of traffic, and frame preemption
with strict priority scheduling (w/o TAS) might seem to be an option in
networks with extremely high data rates. In this paper, we make a first step
towards assisting practitioners in making an informed decision when choosing
between stream-based TAS, class-based TAS, and frame preemption by comparing
these mechanisms in selected scenarios using our TSN network simulation tool
NeSTiNg. Moreover, to facilitate the application of class-based TAS, we derive
a formula for calculating class-based TAS configuration.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-02&engl=1}
}
@inproceedings {INPROC-2020-01,
author = {Mohamed Abdelaal and Mustafa Karadeniz and Frank Duerr and Kurt Rothermel},
title = {{liteNDN: QoS-Aware Packet Forwarding and Caching for Named Data Networks}},
booktitle = {Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC)},
address = {Las Vegas, USA},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--9},
type = {Conference Paper},
month = {January},
year = {2020},
keywords = {Named Data Networking; Forwarding Strategy; Caching Policy; Quality of Service},
language = {English},
cr-category = {C.2.2 Network Protocols},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-01/INPROC-2020-01.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Recently, named data networking (NDN) has been introduced to connect the world
of computing devices via naming data instead of their containers. Through this
strategic change, NDN brings several new features to network communication,
including in-network caching, multipath forwarding, built-in multicast, and
data security. Despite these unique features of NDN networking, there exist
plenty of opportunities for continuing developments, especially with packet
forwarding and caching. In this context, we introduce liteNDN, a novel
forwarding and caching strategy for NDN networks. liteNDN comprises a
cooperative forwarding strategy through which NDN routers share their
knowledge, i.e. data names and interfaces, to optimize their packet forwarding
decisions. Subsequently, liteNDN leverages that knowledge to estimate the
probability of each downstream path to swiftly retrieve the requested data.
Additionally, liteNDN exploits heuristics, such as routing costs and data
significance, to make proper decisions about caching normal as well as
segmented packets. The proposed approach has been extensively evaluated in
terms of the data retrieval latency, network utilization, and the cache hit
rate. The results showed that liteNDN, compared to conventional NDN forwarding
and caching strategies, achieves much less latency while reducing the
unnecessary traffic and caching activities.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-01&engl=1}
}
@article {ART-2020-21,
author = {Ahmad Slo and Sukanya Bhowmik and Kurt Rothermel},
title = {{State-Aware Load Shedding from Input Event Streams in Complex Event Processing}},
journal = {IEEE Transactions on Big Data},
publisher = {IEEE},
pages = {1--18},
type = {Article in Journal},
month = {December},
year = {2020},
isbn = {10.1109/TBDATA.2020.3047438},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/ART-2020-21/ART-2020-21.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {In complex event processing (CEP), load shedding is performed to maintain a
given latency bound during overload situations when there is a limitation on
resources. However, shedding load implies degradation in the quality of results
(QoR). Therefore, it is crucial to perform load shedding in a way that has the
lowest impact on QoR. Researchers, in the CEP domain, propose to drop either
events or partial matches (PMs) in overload cases. They assign utilities to
events or PMs by considering either the importance of events or the importance
of PMs but not both together. In this paper, we combine these approaches where
we propose to assign a utility to an event by considering both the event
importance and the importance of PMs. We propose two load shedding approaches
for CEP systems. The first approach drops events from PMs, while the second
approach drops events from windows. We adopt a probabilistic model that uses
the type and position of an event in a window and the state of a PM to assign a
utility to an event. We, also, propose an approach to predict a utility
threshold that is used to drop the required amount of events to maintain a
given latency bound. By extensive evaluations on two real-world datasets and
several representative queries, we show that, in the majority of cases, our
load shedding approach outperforms state-of-the-art load shedding approaches,
w.r.t. QoR.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-21&engl=1}
}
@article {ART-2020-03,
author = {Mohamed Abdelaal and Suriya Sekar and Frank Duerr and Susanne Becker and Kurt Rothermel and Dieter Fritsch},
title = {{MapSense: Grammar-Supported Inference of Indoor Objects from Crowd-Sourced 3D Point Clouds}},
journal = {Transactions on Internet of Things (TIOT)},
publisher = {ACM (Online)},
pages = {1--28},
type = {Article in Journal},
month = {January},
year = {2020},
doi = {10.1145/3379342},
keywords = {Indoor Mapping; Crowd-sensing; Machine Learning; Formal Grammars; QoS-aware Sensing},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/ART-2020-03/ART-2020-03.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Recently, indoor modeling has gained increased attention thanks to the immense
need for realizing efficient indoor location-based services. Indoor
environments de facto differ from outdoor spaces in two aspects: spaces are
smaller and there are many structural objects such as walls, doors, and
furniture. To model the indoor environments in a proper manner, novel data
acquisition concepts and data modeling algorithms have been devised to meet the
requirements of indoor spatial applications. In this realm, several research
efforts have been exerted. Nevertheless, these efforts mostly suffer either
from adopting impractical data acquisition methods or from being limited to 2D
modeling.
To overcome these limitations, we introduce the MapSense approach that
automatically derives indoor models from 3D point clouds collected by
individuals using mobile devices, such as Google Tango, Apple ARKit, and
Microsoft HoloLens. To this end, MapSense leverages several computer vision and
machine learning algorithms for precisely inferring the structural objects. In
MapSense, we mainly focus on improving the modeling accuracy through adopting
formal grammars which encode design-time knowledge, i.e. structural information
about the building. In addition to modeling accuracy, MapSense considers the
energy overhead on the mobile devices via developing a probabilistic quality
model through which the mobile devices solely upload high-quality point clouds
to the crowd-sensing servers. To demonstrate the performance of MapSense, we
implemented a crowdsensing Android App to collect 3D point clouds from two
different buildings by six volunteers. The results showed that MapSense can
accurately infer the various structural objects together with drastically
reducing the energy overhead on the mobile devices.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-03&engl=1}
}
