3,335 research outputs found
PIN generation using EEG : a stability study
In a previous study, it has been shown that brain activity, i.e.
electroencephalogram (EEG) signals, can be used to generate personal
identification number (PIN). The method was based on brainâcomputer
interface (BCI) technology using a P300-based BCI approach and showed that
a single-channel EEG was sufficient to generate PIN without any error for
three subjects. The advantage of this method is obviously its better fraud
resistance compared to conventional methods of PIN generation such as
entering the numbers using a keypad. Here, we investigate the stability of these
EEG signals when used with a neural network classifier, i.e. to investigate the
changes in the performance of the method over time. Our results, based on
recording conducted over a period of three months, indicate that a single
channel is no longer sufficient and a multiple electrode configuration is
necessary to maintain acceptable performances. Alternatively, a recording
session to retrain the neural network classifier can be conducted on shorter
intervals, though practically this might not be viable
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
Machine learning has become mainstream across industries. Numerous examples
proved the validity of it for security applications. In this work, we
investigate how to reverse engineer a neural network by using only power
side-channel information. To this end, we consider a multilayer perceptron as
the machine learning architecture of choice and assume a non-invasive and
eavesdropping attacker capable of measuring only passive side-channel leakages
like power consumption, electromagnetic radiation, and reaction time.
We conduct all experiments on real data and common neural net architectures
in order to properly assess the applicability and extendability of those
attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our
experiments show that the side-channel attacker is capable of obtaining the
following information: the activation functions used in the architecture, the
number of layers and neurons in the layers, the number of output classes, and
weights in the neural network. Thus, the attacker can effectively reverse
engineer the network using side-channel information.
Next, we show that once the attacker has the knowledge about the neural
network architecture, he/she could also recover the inputs to the network with
only a single-shot measurement. Finally, we discuss several mitigations one
could use to thwart such attacks.Comment: 15 pages, 16 figure
An Empirical Investigation of Using ANN Based N-State Sequential Machine and Chaotic Neural Network in the Field of Cryptography
Cryptography is the exchange of information among the users without leakage of information to others. Many public key cryptography are available which are based on number theory but it has the drawback of requirement of large computational power, complexity and time consumption during generation of key [1]. To overcome these drawbacks, we analyzed neural network is the best way to generate secret key. In this paper we proposed a very new approach in the field of cryptography. We are using two artificial neural networks in the field of cryptography. First One is ANN based n-state sequential machine and Other One is chaotic neural network. For simulation MATLAB software is used. This paper also includes an experimental results and complete demonstration that ANN based n-state sequential machine and chaotic neural network is successfully perform the cryptography
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
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