MITIGATION OF PERIODIC JAMMING IN A SPREAD SPECTRUM
SYSTEM BY ADAPTIVE FILTER SELECTION
∗
Bruce DeBruhl and Patrick Tague
Carnegie Mellon University, Pittsburgh, U.S.A.
Keywords:
Adaptive Anti-jamming, Digital Filtering, Jamming Mitigation, Spread Spectrum.
Abstract:
Jamming has long been a problem in wireless communication systems. Traditionally, defense techniques have
looked to raise the cost of mounting an equally effective jamming attack. One technique to raise the cost
of jamming is direct sequence spread spectrum (DSSS) which spreads data over a wider bandwidth and has
built-in error correction. To work around this, attackers have developed intelligent jamming techniques to
minimize the cost of mounting attacks on these systems. To lower the cost of attacking a DSSS system, an
attacker can use periodic jamming which alternates between an attacking and sleeping state. Previously, a
digital filter has been used to mitigate a periodic jamming attack at the center frequency of the attacker. In
this work, we expand this previous attack model by allowing an attacker to jam at any frequency and even to
move to different frequencies in the channel. To defend against the more general attack, we propose the use
of an adaptive filter selection technique. This technique monitors packet delivery ratio (PDR) at the receiver
and uses this information to infer whether it is being attacked. If the receivers PDR is low, it activates a filter
from a pre-defined filter bank and tests if performance improves. This process continues by activating different
filters from the filter bank until adequate PDR performance is achieved. We show that this approach can search
through a small set of filters and recover over 90% of packets with a search time of less than 3 seconds on
average for an attacker who randomly chooses its center frequency.
1 INTRODUCTION
The open nature of wireless communications allows
for transferring data without expensive and bulky
wired connections but also opens the communications
channel to any user. This open nature, allows for ma-
licious users to affect legitimate users experience by
intentionally broadcasting interference onto the wire-
less medium, an attack known as jamming (Torrieri,
1992).
Traditionally, to defend against jamming, spread
spectrum techniques are used (Torrieri, 1992;
Molisch, 2005). Two common variants of spread
spectrum are frequency hopping spread spectrum
(FHSS) and direct sequence spread spectrum. In
∗
This research was supported by CyLab at Carnegie
Mellon under grant DAAD19-02-1-0389 from the Army
Research Ofce and the Northrop Grumman Cybersecurity
Research Consortium. The views and conclusions con-
tained here are those of the authors and should not be in-
terpreted as necessarily representing the ofcial policies or
endorsements, either express or implied, of ARO, CMU,
Northrop Grumman, or the U.S. Government or any of its
agencies.
FHSS a transmitter and receiver synchronously
“hops” from channel to channel using a secret se-
quence. If the number of channels is sufficiently
high, this is effective at raising the cost of jamming,
since the jammer either needs to know the secret se-
quence or be able to mount a wide-band jamming
attack (Pelechrinis et al., 2009). In DSSS the re-
ceiver maps each bit to many chips which are sent
at rates greater than the data rate. This makes the
legitimate signal hard to detect and allows for eas-
ier bit recovery by providing bit level error correc-
tion. Spread spectrum technique do not eliminate
jamming but forces attackers to use more energy to
mount an equally effective attack. Another approach
to deter jamming, is to use detection technique and
retreat from the jammer. At the MAC layer, jamming
detection can be done by monitoring the packet de-
livery ratio (PDR) and flagging unexpected changes
(C¸ akıro
ˇ
glu and
¨
Ozcerit, 2008). To improve accuracy
of jamming detection in mobile systems, consistency
checks of PDR with physical layer information like
received signal strength can be used (Xu et al., 2006).
Once a jamming attack is detected, a legitimate sys-
431
DeBruhl B. and Tague P. (2012).
MITIGATION OF PERIODIC JAMMING IN A SPREAD SPECTRUM SYSTEM BY ADAPTIVE FILTER SELECTION.
In Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems, pages 431-439
DOI: 10.5220/0003936904310439
Copyright
c
SciTePress
tem can retreat in space or in the spectrum and try to
regain the ability to communicate.
To alleviate the additional cost of jamming a
spread spectrum system or to stealthily avoid detec-
tion, intelligent jamming techniques have been stud-
ied (Thuente and Acharya, 2006; Law et al., 2009;
Pelechrinis et al., 2011). These attacks can range
from using cross-layer information (Chan et al., 2007;
Tague et al., 2009) to signal conditioning (Xu et al.,
2006; Bayraktaroglu et al., 2008). In this work, we fo-
cus on periodic jamming (Bayraktaroglu et al., 2008)
in which the attacker continually alternates between
a sleeping and attacking state. Periodic jamming is
able to effectively attack common DSSS systems with
lower energy usage than tone jamming.
It has been shown that a periodic jammer modu-
lated to the same frequency as the legitimate system
can be mitigated using a digital filtering technique
(DeBruhl and Tague, 2011). This work added a digital
high-pass filter at the base-band of an IEEE 802.15.4
(IEEE 802.15.4, 2006) receiver architecture to miti-
gate the effects from a periodic jamming attacks. In
this work, we extend this previous work by consider-
ing an expanded attack in which the jamming center
frequency is not necessarily the same as that of the
legitimate signal and may occasionally change.
To defend against an attack that uses an arbitrary
center frequency and occasionally changes its cen-
ter frequencies we propose an adaptive filter selection
mechanism. This filter selection mechanism monitors
the system’s performance and uses this information
to infer if it is performing well. If it is not perform-
ing well it tries a different filter and tests to see if it
improves performance. Thus for any single-carrier,
narrow-band, periodic jamming attack a filter is even-
tually found. The major contributions of our work
include the following.
• We propose the inclusion of a adaptive filter se-
lection technique that searches through a set of
filters to mitigate the effect of periodic jamming
to a minimal level.
• We show three possible filter selection algorithms
to use in our jamming mitigation technique.
• We evaluate the effect of our proposed jamming
mitigation technique against various attack mod-
els using a software defined radio implementation.
The remainder of this paper is organized as fol-
lows. In Section 2, we introduce the models we use
for the attacker and defender. In Section 3, we mo-
tivate the filter selection approach and introduce our
proposed architecture. In Section 4, we present three
filter selection algorithms and in Section 5, we present
three attack models to test these algorithms. Finally,
In Section 6, we show implementation details and em-
pirical results for our architecture and we conclude
the paper in Section 7.
2 MODELS
In this section, we introduce our models for the legiti-
mate system and attacker, building on that of existing
work.
We consider a system where the sender and re-
ceiver are using the DSSS-based IEEE 802.15.4 phys-
ical layer with the 2450 MHz PHY specification
(IEEE 802.15.4, 2006). DSSS is achieved in this pro-
tocol by mapping each group of 4 bits into a 32 chip
sequence. Half these chips are modulated on the in-
phase and half on the quadrature channel using offset
quadrature phase shift keying (O-QPSK). At the re-
ceiver, the signal is demodulated and passes through
a low-pass filter to select the appropriate channel. The
receiver recovers chips using maximum likelihood es-
timation and then de-spreads 32 chips to the most
probable 4-bit symbol.
2.423 2.424 2.425 2.426 2.427
0
1
2
3
x 10
−7
Frequency (GHz)
Spectral Density
IEEE 802.15.4
Periodic Jammer
Figure 1: This figure we show the modulated frequency do-
main model of both the attacker and the IEEE 802.15.4 sig-
nal. Although, the attacker’s signal is much narrower then
that of the legitimate 802.15.4 signal, it is sufficient to force
packets to be dropped.
0 .001 .002 .003 .004 .005
0
0.5
1
1.5
Bandwidth (GHz centered at carrier frequency)
Cumulative Spectral Power
IEEE 802.15.4
Periodic Jammer
Figure 2: We show the cumulative sum of normalized spec-
tral power for IEEE 802.15.4 and a periodic jammer. Be-
cause the jamming power in contained in a much smaller
region than the 802.15.4 signal, the jamming signal can be
filtered out without significantly degrading the legitimate
signal.
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2.425 2.4252 2.4254 2.4256 2.4258 2.426 2.4262
0
0.2
0.4
0.6
0.8
1
Jamming Center Frequency (GHz)
Packet Delivery Ratio
One Filter
No Filter
Figure 3: We show the packet delivery ratio for a vanilla
receiver and a receiver with a jamming mitigation filter at
the base-band. The use of only a high-pass filter defends
against attacks at the channels center frequency, while the
filter is detrimental to the receiver when attacks are centered
at other frequencies.
The 802.15.4 protocol uses a 2 byte cyclic redun-
dancy check to test for packet errors (IEEE 802.15.4,
2006). This is calculated at the transmitter and ap-
pended to the packet. The receiver raises a flag if there
is an error and passes the data up the stack if no error
is detected. The use of the 2 byte CRC can pose a vul-
nerability since an intelligent attacker can completely
jam an 802.15.4 system using the two byte CRC by
only jamming one symbol in every packet.
We consider a periodic jamming attack which al-
ternates between a sleeping and attacking state. This
attack is advantageous because it looks to take advan-
tage of the weakness introduced in using a two-byte
CRC. The periodic jamming attack also reduces en-
ergy to allow for attacks to be mounted longer from
low-power devices. This attack is often assumed to
occur at the center frequency of the legitimate chan-
nel. We do not make this assumption but rather al-
low the attacker to choose any center frequency and
to occasionally change its center frequency within the
channel.
We revisit the model which is used to motivates
using a digital filter to mitigate a baseband jamming
attack (DeBruhl and Tague, 2011). Using this model
they arrive at the plot shown in Figure 1, which moti-
vates the fact that a filter can be used to mitigate the
effect of periodic jamming at the base band.
To further motivate this fact, we present Fig-
ure 2, showing the cumulative normalized power for
802.15.4 and a periodic jammer in for a bandwidth
centered around the carrier frequency. This plot
shows 85% of of the attacker’s power is contained in a
region with only 10% of the signal power of 802.15.4.
The related work showed that a filter at the center
frequency could thus increase packet delivery ratio
(PDR) for a periodic jammer at the center frequency
of the desired channel from under 5% to over 90%.
However, the previous approach fails if the at-
tacker adjusts the jamming center frequency beyond
the base-band filter. In Figure 3, we empirically show
how the single base-band filter yields worse perfor-
mance than an unfiltered receiver when the jamming
center frequency is shifted from the center of the
channel. In this work, we thus propose a technique
to mitigate the effects of the proposed jammer with
an arbitrary center frequency.
3 ADAPTIVE FILTER
SELECTION
As discussed in Section 2, it has previously been
shown that a filter can mitigate a jamming attack with
a center frequency at the center of the channel. In
this work, we allow that attacker to modulate to an
arbitrary center frequency. We consider two types of
attackers: one chooses its center frequency at the be-
ginning of the attack and never changes it, and the
other periodically changes its center frequency.
To mitigate such attacks, we propose to redesign
the 802.15.4 receiver as shown in Figure 4. In this
figure the light colored blocks represent the normal
802.15.4 receiver and the dark blocks represent pro-
posed modifications to the receiver, which include
filter-banks on the in-phase (I) and quadrature (Q)
channels as well as a controller to select the filter.
Our research hypothesis is that there exists a set
of filters Φ such that at least one filter φ ∈ Φ can al-
low a receiver to achieve a high PDR under a periodic
jamming attack at any center frequency assuming the
attack is of equal power to the legitimate signal. We
consider this through an empirical study in Section 6.
Since 802.15.4 uses a 2 byte CRC we propose
using information derived from these consistency
checks to keep track of packet delivery ratio for a
given time period. These calculations are performed
in the “Packet error detection” block of Figure 4.
Given a set of filters Φ and the PDR information,
we now have to derive the “Filter controller” block
from Figure 4. We propose a filter controller which
tests if PDR is higher then a threshold δ, if it is than
there is no need to attempt a defense, as there is no
effective attack being mounted. On the other hand, if
the PDR is lower than a threshold δ, there is either
an attack being mounted or environmental conditions
affecting the system. Since the system is already re-
ceiving a high amount of error, it is in the receiver’s
interest to try and mitigate this attack. To mitigate the
attack the receiver chooses a filter φ from the set Φ
and try receiving for a fixed amount of time τ. After τ
seconds the receiver then determines if PDR is again
under δ, if so it selects another φ ∈ Φ to try to increase
PDR. Once it finds a filter φ to maximize PDR, nor-
MITIGATION OF PERIODIC JAMMING IN A SPREAD SPECTRUM SYSTEM BY ADAPTIVE FILTER SELECTION
433
90°
In-Phase
Detection
Quadrature
Detection
Digital Filter
Digital Filter
LNA
LNA
LNA
BPF
LPF
LPF
32 Chip to
Symbol
Conversion
1 Chip
Length Delay
Filter Tuning
Packet Error
Detection
Filter
Controller
PDR
Packet
Consistency
Check
(2 byte CRC)
Figure 4: Our proposed approach incorporates a controller used to select from a digital filter bank mitigating the effect of
a periodic jamming attack. In previous work, a single filter was proposed which is shown insufficient if an attacker is not
constrained to a single center frequency.
mal communication can continue. In Section 4, we
show various algorithms to select which filter to try
once a reduced PDR is detected.
It is also possible to use environmental informa-
tion to avoid unnecessary searches for filters. For
example, the received signal strength could be used
to test if a transmitter is relatively close or far away.
If the received signal strength is low, it is likely that
there is in fact not an attack occurring but simply poor
communication conditions, in which case testing fil-
ters is not necessary. We leave such derivations of
consistency checks as future work.
4 FILTER SELECTION
ALGORITHMS
In this section, we introduce three methods to imple-
ment the filter search controller introduced in Sec-
tion 3. The main goal in designing these algorithms
is to allow for efficient searches while not open-
ing easy attacks against the algorithms. We first in-
troduce a straightforward filter selection technique,
which searches the space by always choosing the next
higher frequency filter. The second filter selection
technique looks to decrease the search time by arrang-
ing the filters in order of decreasing filter width. As-
suming a uniform likelihood of the attack occurring
at any frequency, this decreases search time by having
narrow filters that are less likely to find an attacker at
the end of the search. The last filter selection algo-
rithm is random filter search, which aims to increase
entropy, making an intelligent frequency hopping at-
tack difficult.
The first filter search algorithm we consider is In-
cremental Filter Selection as seen in Figure 5. This al-
gorithm selects the next higher frequency filter when-
ever it is not receiving packets properly. If it is at the
maximum frequency than it turns off the filter to see
if it can obtain better performance, if this fails it con-
0 500 1000 1500
0
2
4
6
8
10
Frequency (kHz)
Filter Number
(a)
ϕ
3
ϕ
2
ϕ
1
No
Filter ϕ
N
...
If PDR < δ
If PDR < δ
If PDR < δ
If PDR < δ
(b)
Figure 5: This figure illustrates the incremental filter selec-
tion algorithm. In (a) we see the ordering of filters by in-
creasing filter center frequency. In (a) 0Hz represents base-
band. In (b) we see the transition diagram, showing the filter
continuously shifting the filter to higher frequency unless it
is maxed out.
tinues searching with the first filter again.
The second filter selection algorithm we propose
is called Ordered Filter Selection. This algorithm,
shown in Figure 6, orders filters from widest to nar-
rowest. The reason for such an ordering is to allow
for a quicker search of the bandwidth assuming an at-
tacker is using a uniform distribution. This algorithm
always start searching from the widest filter which we
call filter 1. When its PDR first drops below δ it sets
a “searching” variable we denote as x to 1. This vari-
able remains at one until a solution is found such that
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0 500 1000 1500
0
2
4
6
8
10
Frequency (kHz)
Filter Number
(a)
(b)
Figure 6: This figure shows the ordered filter selection al-
gorithm. In (a) we see the selected filters arranged by de-
creasing bandwidth noting that the right most filter extends
to 2.5 MHz. In (a) 0Hz represents baseband. In (b) we
see the state diagram which uses the variable x to indicate
whether it has found a solution. We use the notation “A/B”
to indicate a transition is taken provided A is satisfied and
B is executed during the transition.
the PDR is over δ at which point x is set to 0. When
a jammer changes frequency and causes the PDR to
drop, the receiver restarts the search at the widest fil-
ter and resets x to 1. Against an attacker choosing
center frequencies randomly, this allows for a quick
search on average by covering the spectrum as quick
as possible.
The last filter search algorithm, shown in Figure 7,
is called Random Filter Selection. This algorithm
again uses a searching variable x like in the ordered
filter selection algorithm. We add a permute state to
this algorithm which is entered whenever x = 0 and
PDR is below δ. During the permute state the or-
dered of the filters is randomly rearranged. We denote
the mapping from the permuted set to the incremen-
tally ordered set as φ
Π(k)
= φ
n
. For example, in Fig-
ure 6(a) the permutation is ordered [7, 6, 3,8, 2, 1, 4, 5]
so φ
Π(4)
= φ
8
indicates the 4
th
filtered used is the
0 500 1000 1500
Π(1) = 7
Π(2) = 6
Π(3) = 3
Π(4) = 8
Π(5) = 2
Π(6) = 1
Π(7) = 4
Π(8) = 5
Frequency (kHz)
Filter Number
(a)
(b)
Figure 7: This figure shows the random filter selection al-
gorithm. In (a) we show the order of filters at any point
is unknown since it randomly permutes the filters when-
ever it leaves the not jammed state. In (a) 0Hz represents
baseband. In (b) we see its behavior is similar to ordered
jamming, except the added permute state changes the order
of filters every time the jamming center frequency changes.
We use the notation “A/B” to indicate a transition is taken
provided A is satisfied and B is executed during the transi-
tion.
eighth highest in frequency. Continually changing the
order of the search makes designing an attack against
this algorithm particularly difficult.
5 ATTACK ALGORITHMS
In this section, we introduce three attacks to test our
filter search algorithms. Each of these algorithms se-
lect a new center frequency every Ξ seconds. We pro-
pose one base-line attack and two attacks designed
against particular filter selection algorithms.
The base-line attack we present is called random
center jammer and is shown in Algorithm 1. In this
attack a jammer randomly selects a new center fre-
quency every Ξ seconds from a uniform distribution
ranging from the center of the channel to an offset of
MITIGATION OF PERIODIC JAMMING IN A SPREAD SPECTRUM SYSTEM BY ADAPTIVE FILTER SELECTION
435
f
max
. This attack represents an attacker who is aim-
ing to attack an adaptive filtering but does not know
what strategy and set of filters will be used. If the at-
tacker had this knowledge, this attack should be the
least effective.
An attacker aiming to disrupt an ordered filter se-
lection with a known set of filters Φ could use an at-
tack we call Bi-Modal Jamming. Since ordered filter
selection always tries filter in the same order there is
always two filters at the end of the order which take
the longest to find, we denote these filters as φ
N
and
φ
N−1
and denote the frequency at the middle of these
filters as f
N
and f
n−1
respectively. Bi-modal jam-
ming, shown in Algorithm 2, alternates its center fre-
quency between f
N
and f
N−1
every Ξ seconds, forcing
the algorithm to search through almost every filter.
The last attack algorithm is designed to attack an
incremental filter search. In this algorithm, called
Decremental jamming, a jammer aims to step its cen-
ter frequency down by f
dec
every Ξ seconds as shown
in Algorithm 3. This should be most detrimental
to incremental jamming since it searches in increas-
ing frequencies. We use the constants f
max
and f
min
to indicate the maximum and minimum allowable
frequency. Whenever the jammers frequency is de-
creased below f
min
the frequency is increased by the
difference in f
max
and f
min
. To find a slightly lower
frequency it has a high search time as it goes through
many different filters. In practice, the selection of f
dec
is difficult since the width of filters is variable.
Algorithm 1: This algorithm performs random center jam-
ming.
1: while True do
2: f
o f f set
= random(0, f
max
)
3: f
center
= 2.425GHz + f
o f f set
4: wait(Ξ seconds)
5: end while
Algorithm 2: This algorithm performs random center jam-
ming which randomly chooses a new center frequency every
Ξ seconds.
1: while True do
2: f
center
= f
N
3: wait(Ξ seconds)
4: f
center
= f
N−1
5: wait(Ξ seconds)
6: end while
6 IMPLEMENTATION AND
RESULTS
In this section, we present our implementation details
as well as empirical results for our jamming mitiga-
tion techniques and attack models.
6.1 Implementation
We implement a proof-of-concept system using adap-
tive filter selection for jamming mitigation on the
USRP2 platform (Ettus, 2011) using GNUradio
(GNURadio, 2011) and an IEEE 802.15.4 implemen-
tation (Schmid et al., 2007). We allow a filter to be
tested for half a second or τ = .5s and aim to keep a
PDR of at least δ = 80%. We test all three jamming
strategies suggested in Section 5 and set the amount
of time an attacker stays on a center frequencies to
Ξ = 10 seconds.
For implementation, a filter set Φ can be selected
by using either analytical or empirical methods. The
design of an optimal set of filters is out of the scope
of this work, so we selected a set of 8 filters using
empirical methods. The lowest frequency filter is a
high-pass filter, the highest frequency filter is a low-
pass filter, and all other filters are band-stop filters.
To empirically tune the filters, we use our SDR setup
to determine filter widths that would give adequate
performance to our system in benign conditions and
overlap to account for roll off. The selected filters are
outlined in Table 1, all FIR filters (Tan, 2007) with a
transition bandwidth of 40 kHz.
6.2 Results
In Figure 8, we show the adaptation for an incre-
mental filter search against a random center jamming
attack. This figure highlights the general operation
of adaptive filter selection as well as some of the
strengths and weaknesses than can be seen in it. The
top plot in this figure shows the PDR for every half
Algorithm 3: This algorithm performs decremental jam-
ming which decreases the jammers center frequency every
Ξ seconds.
1: f
center
= 2.425GHz
2: while True do
3: f
center
= f
center
− f
dec
4: if f
center
< f
min
then
5: f
center
= f
center
+ f
max
− f
min
6: end if
7: wait(Ξ seconds)
8: end while
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0 10 20 30 40 50 60 70 80
0
0.5
1
PDR
0 10 20 30 40 50 60 70 80
0
5
10
Filter Number
0 10 20 30 40 50 60 70 80
2.425
2.426
2.427
x 10
9
Time (seconds)
Jamming Frequency
Figure 8: We show a filter selection algorithm (incremental) running in real time.
Table 1: This table shows the empirically tuned filters.
Type Low Cutoff High Cutoff
High-Pass – 90 kHz
Band-Stop
90 kHz 300 kHz
260 kHz 440 kHz
400 kHz 530 kHz
490 kHz 610 kHz
570 kHz 725 kHz
710 kHz 1200 kHz
Low-Pass 1160 kHz –
second time step during the experiment. The middle
plot shows which of the eight filters is being used.
If the value is zero than no filter is being used in
that time step. The bottom plot shows the center fre-
quency of the random center jammer. Figure 8 gives
us a strong intuition to the strengths and weaknesses
of our proposed jamming mitigation technique. This
technique does provide for a system to mitigate the
effects of a jammer that chooses one frequency and
never changes quite well. It also allows for our sys-
tem to mitigate the effects of an attacker who changes
at a rate that is slower than our adaptation rate. When
a random center attacker change frequency every 10
seconds our system is able to effectively recover over
70% of the data, increasing the amount of good data
from under 1%. To determine the bounds of our
system, we consider whether our filter set is robust
enough to cover the whole spectrum and the average
search time for each filter selection algorithm.
We first consider whether our filters work at all
frequencies. To do this, we programmed an adap-
tive filter selection scheme and used a static frequency
jammer. We tested the the packet delivery ratio of the
receiver once the adaptive filtering scheme had found
an appropriate filter. The results for the PDR are
shown in Figure 9. This graph indicates that a trans-
mitter and jammer with equal sending power and dis-
tance from the receiver, our filtering technique yields
over 90% packet delivery ratio once the appropriate
filter is found.
Secondly, we consider the search time for each
filter selection algorithm given various attacks. We
implemented random center jamming, bi-modal jam-
ming, and decremental jamming with Ξ = 10. We also
implement all three filter selection algorithms and test
the performance of all 9 combinations. Figure 10
shows the results for each search algorithm and at-
tack model combination. It is clear that a random
center jammer has less effect than the other two types
of attacks. We also see that ordered filter selection
has a slight advantage in a random attack. Bi-modal
jamming is most effective against ordered filter selec-
MITIGATION OF PERIODIC JAMMING IN A SPREAD SPECTRUM SYSTEM BY ADAPTIVE FILTER SELECTION
437
2.425 2.4252 2.4254 2.4256 2.4258 2.426 2.4262
0
0.2
0.4
0.6
0.8
1
Jamming Center Frequency (GHz)
Packet Delivery Ratio
One Filter
No Filter
Figure 9: We show the performance of our the appropri-
ate filter from our filter-bank F at various frequencies com-
pared to a receiver without filters and a single filter ap-
proach. Our approach is the only one offering high packet
reception for attackers with an arbitrary center frequency.
Random Center Bi−Modal Incremental
0
1
2
3
4
5
6
Attack Type
Average Search Time (seconds)
Incremental Selection
Ordered Selection
Random Selection
Figure 10: We show how each filter selection algorithm per-
forms under various attack models. Ordered filter selection
does well except against a bi-modal attack designed against
it.
tion because it is designed in such a way to make the
algorithm always choose the last two filters, taking
the greatest amount of time. With a decremental jam-
ming attack ordered jamming again has the best per-
formance. Random and incremental filter selection
has similar performance. This is because choosing to
a value for f
dec
, the frequency step, to optimally at-
tack incremental filter selection is not trivial. A large
value will allow for quick searches and a small value
makes it probable filters do not have to change every
time step.
7 CONCLUSIONS
Intelligent jamming techniques have been shown
to mount effective attacks against spread spectrum
systmes with low energy. One of these attacks is peri-
odic jamming which alternates between an attacking
and sleeping state and is effective against DSSS sys-
tems. In this work, we consider how to mitigate the
effects of a periodic jammer which can choose an ar-
bitrary center frequency and occasionally changes its
center frequency. To do this, we monitor packet deliv-
ery at the receiver and if it is below a certain threshold
activate a filter from a preselected filter bank. This
process is repeated until an appropriate solution is
found. We introduce three filter selection algorithms
and three attack algorithms to consider the effects of
different filter selection methods in our system. Once
a filter is found, it is shown that over 90% of pack-
ets can be recovered, regardless of the jammers center
frequency, offering a considerable gain over previous
approaches. We also show the average search times
of our filter selection algorithms range from 2-5 sec-
onds.
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