197 research outputs found
ControlFreak: Signature Chaining to Counter Control Flow Attacks
Abstract:
Many modern embedded systems use networks to communicate. This increases the attack surface: the adversary does not need to have physical access to the system and can launch remote attacks. By exploiting software bugs, the attacker might be able to change the behavior of a program. Security violations in safety-critical systems are particularly dangerous since they might lead to catastrophic results. Hence, safety-critical software requires additional protection. We present an approach to detect and prevent control flow attacks. Such attacks maliciously modify program's control flow to achieve the desired behavior. We develop ControlFreak, a hardware watchdog to monitor program execution and to prevent illegal control flow transitions. The watchdog employs chained signatures to detect any modification of the instruction stream and any illegal jump in the program even if signatures are maliciously modified
Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
In this paper, we present our approach for solving the DEBS Grand Challenge
2018. The challenge asks to provide a prediction for (i) a destination and the
(ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the
maritime context. Novel aspects of our approach include the use of ensemble
learning based on Random Forest, Gradient Boosting Decision Trees (GBDT),
XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a
prediction for a destination while for the arrival time, we propose the use of
Feed-forward Neural Networks. In our evaluation, we were able to achieve an
accuracy of 97% for the port destination classification problem and 90% (in
mins) for the ETA prediction
Quality-Driven Disorder Handling for M-way Sliding Window Stream Joins
Sliding window join is one of the most important operators for stream
applications. To produce high quality join results, a stream processing system
must deal with the ubiquitous disorder within input streams which is caused by
network delay, asynchronous source clocks, etc. Disorder handling involves an
inevitable tradeoff between the latency and the quality of produced join
results. To meet different requirements of stream applications, it is desirable
to provide a user-configurable result-latency vs. result-quality tradeoff.
Existing disorder handling approaches either do not provide such
configurability, or support only user-specified latency constraints.
In this work, we advocate the idea of quality-driven disorder handling, and
propose a buffer-based disorder handling approach for sliding window joins,
which minimizes sizes of input-sorting buffers, thus the result latency, while
respecting user-specified result-quality requirements. The core of our approach
is an analytical model which directly captures the relationship between sizes
of input buffers and the produced result quality. Our approach is generic. It
supports m-way sliding window joins with arbitrary join conditions. Experiments
on real-world and synthetic datasets show that, compared to the state of the
art, our approach can reduce the result latency incurred by disorder handling
by up to 95% while providing the same level of result quality.Comment: 12 pages, 11 figures, IEEE ICDE 201
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