The following is the explanation of our concealing-restoring system for the physical layer data, and the introduction of its application to the some data on a upper layer of the OSI reference model. Our concealing-restoring system is based on the stochastic filtering theory.
We propose a concealing-restoring system (CRS) for data on physical layer of the OSI reference model. CRS conceals those data by disturbing them with some random noises, and restores the data from the concealed ones to the original ones by using the noise elimination based on a proper stochastic filtering theory. Although we introduced the outline of the almost linear version of CRS in our previous work [Fujii & Hirokawa, 2022a], we explain its details, and study its nonlinearization to improve the security of CRS in our paper [Fujii & Hirokawa, 2022b]. We make some theoretical studies of accuracy of CRS in the paper [Hirokawa, 2022] as well as the above one.
The practical use of microdevices for the Internet of Things (IoT) interfaces
has made remarkable advance in recent years.
Due to its current cutting-edge technologies,
IoT including the brain-machine interface (BMI)/
brain-computer interface (BCI) has been turned into the reality.
For instance, Benabid et al succeed in controlling
an exoskeleton by brain signals of a tetraplegic patient
through an epidural wireless BMI.
Neuralink reports the news that they have been developing the N1 Link, a fully-implanted, wireless,
high-channel count BMI chip.
Neuralink White Paper is here.
Flesher et al experimentally show that
tactile percepts of signals from a robotic, prosthetic arm
can be evoked by using a BCI by establishing an afferent channel
to the BCI to mimic sensory input from the skin of a hand.
It is naturally feared that someone hacks into
some embedding type medical devices and hijacks them.
The serious apprehension may be beginning to become a reality.
It is reported by the US Government Accountability Office (GAO)
that a cardiac pacemaker device can be tampered from remote place
by radiocommunication.
A demonstration of hacking a live jellyfish
and the controlling its neural signals
is performed by Xu and Dabiri.
These are becoming increasingly alarming problems,
and we must establish the security in the microdevices.
In addition to the security problems above,
there are some other problems for the drone (i.e., the flying IoT system
in our real life): the hijack of the drone operation,
and the exploitation of data on it.
We should mind that
someone can tap and steal signals between a drone and its remote controller.
We are interested in the security for data in the space
which has too small arithmetic capacity
to install an encryption technology.
The scenes we envision also include countermeasures for the firmware attack and
side-channel attack in a low layer of the computer architecture.
The firmware attack bypasses some softwares for antivirus and encryption
on the higher-layer,
and infects the lower-layer data in a device.
The side-channel attack bypasses the cryptographic technique
based on mathematical complexity
and taps the cryptographic key.
Several sorts of side-channel attacks have been proposed,
and many new side-channel attacks have been presented.
In particular,
CacheBleed and TLBleed have come under the industrial spotlight.
We propose a concealing-restoring system (CRS)
with some secret common keys
for the data on the physical layer of the OSI reference model.
Here, OSI is the abbreviation of the open systems interconnection,
and the OSI reference model consists of 7 layers:
the physical layer, the data link layer,
the network layer, the transport layer, the session layer,
the presentation layer, and the application layer
from the lowest layer to the highest one.
We restrict our idea to scenes such as
the instances described above,
and we do not expect general scenes in wireless communication.
Thus, the specification and construction of CRS
should be for exclusive use among the device users,
not be opened to the public.
The secret common keys of CRS must be shared by the device users in advance with another method
prior to the use of CRS.
Our target scenes requiring such the security include
countermeasures for the firmware attack and
side-chanel attack in a low layer of the computer architecture.
The firmware attack bypasses some softwares for antivirus and encryption
on the higher-layer,
and infects the lower-layer data in a device.
The side-channel attack bypasses the cryptographic technique
based on mathematical complexity
and taps the cryptographic key.
Several sorts of side-channel attacks have been proposed,
and many new side-channel attacks have been presented.
In particular,
CacheBleed and TLBleed have come under the industrial spotlight
from the point of view of computer architecture.
We propose a concealing-restoring system (CRS)
with some secret common keys
for the data on the physical layer of the OSI reference model.
Here, OSI is the abbreviation of the open systems interconnection,
and the OSI reference model consists of 7 layers:
the physical layer, the data link layer,
the network layer, the transport layer, the session layer,
the presentation layer, and the application layer
from the lowest layer to the highest one.
Our concealing-restoring system processes signals on the physical layer from the data link layer of the OSI reference model (Example 1). Based on this process, using some several transformations on upper laysers of the OSI reference model, we can apply our concealing and restoring methods to other objects such as pictorial images and ASCII codes (Examples 3&4). In the examples below, we set N=2 for the parameter N appearing in our paper. Thus, we have 3(=N+1) concealed signals.
A binary word that we want to conceal is given by
100111101100001110011000101110010101001011011100110011010001011000010101101111101110000111111111110.
Using the D/A transformation that we define, we can get the signal
Now, we apply our concealing-restoring system to a digital pictorial image.
We use binary data of a digital pictorial image in the ORL Database of Faces,
an archive of AT&T Laboratories Cambridge.
The data have the greyscale value of 256 gradations (8bit/pixel).
The original pictorial image and its signal are
obtained as in the below:
Next, we apply our concealing-restoring system to an analog pictorial image
in the Olivetti faces database,
where the data of pictorial image are transformed to analogue data
from the original one in the ORL Database of Faces,
an archive of AT&T Laboratories Cambridge.
The data have the greyscale value of 256 gradations (8bit/pixel).
The original pictorial image and its signal are as in the below:
We introduce nonlinearity into our concealing-restoring system which processes signals on the physical layer from the data link layer of the OSI reference model (Example 1). In addition to the process, using some several transformations on upper laysers of the OSI reference model, we can handle the security for other objects such as pictorial images and ASCII codes (Examples 5&6). In the examples below, we set N=2 for the parameter N appearing in our paper. Thus, we have 3(=N+1) concealed signals.
We can improve CRS by introducing nonlinearity into it.
For the signal of the original binary word in Example 1,
we conceal it by the concealing system with the nonlinearity.
Then, we have the concealed data:
We use the signal of the digital pictorial image
already used in Example 2.
We use the concealing system with the nonlinearity,
and then, get the concealed data.
We use the signal of the analog pictorial image
already used in Example 3.
We use the concealing system with the nonlinearity,
and then, get the concealed data.
We apply CRS to the pictorial image
with bigger pixel than the above pictorial images.
We use a pictorial image in the Standard Image Data-Base.
The number of its total pixels is 512x512=262,144.
The pictorial image and the first part of
its binary pulse are in the following.