Keywords |
| Network security, mobility models, cooperative protocol. |
INTRODUCTION |
| A wireless sensor network (WSN) of spatially distributed autonomous sensors to monitor physical or
environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the
network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The
development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today
such networks are used in many industrial and consumer applications, such as industrial process monitoring and control,
machine health monitoring, and so on. |
| The adversary can compute secrets for round r0 > r, but it cannot compute any secrets used in prior rounds. Note
that this is possible even if the adversary is no longer in control of the sensor in round r0. Backward secrecy is much more
challenging, because knowledge of Kr allows the adversary to compute secrets for future rounds. It would be trivial to
obtain backward secrecy if each sensor had a True Random Number Generator (TRNG). Because a TRNG yields
information- theoretically independent values, even if the adversary learns many (but not all) TRNG outputs, it cannot
compute the missing values, whether they correspond to the past or to the future. In other words, when the adversary
compromises the sensor, it cannot learn past secrets; once the adversary leaves the sensor it will not be able to compute
future sensor secrets. Unfortunately, TRNGs are not found on commodity sensors and not expected to be available for the
near future. An alternative to per-sensor TRNGs is the presence of a trusted third party (TTP); this is assumed in keyinsulated
schemes. In such schemes, forward and backward secrecy is achieved by having end-devices (e.g., the sensors)
evolve their secrets in cooperation with a TTP, called a base. Unless both the end-device and the base are compromised at
the same time, per-round keys are insulated. Key-insulated schemes are well matched for WSNs with a constantly present
sink, where the latter acts as a base. However, there are settings where the constant presence of a sink is not viable. |
| The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or
sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal
antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an
energy source, usually a battery or an embedded form of energy harvesting. A sensor node might vary in size from that of a
shoebox down to the size of a grain of dust, although functioning "motes" of genuine microscopic dimensions have yet to
be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of dollars, depending on the
complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on
resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs can
vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the
hops of the network can be routing or flooding. |
RELATED WORK |
| Di Ma and Gene Tsudik: Suggested, Unattended wireless sensor networks (UWSNs) operating in hostile environments face
the risk of compromise. Unable to off-load collected data to a sink or some other trusted external entity, sensors must
protect themselves by attempting to mitigate potential compromise and safeguarding their data. It refers on techniques that
allow unattended sensors to recover from intrusions by soliciting help from peer sensors. Hence define a realistic
adversarial model and show how certain simple defense methods can result in sensors re-gaining secrecy and authenticity of
collected data, despite adversary's efforts to the contrary. Here presents an extensive analysis and a set of simulation results
that support observation and demonstrate the effectiveness of proposed techniques. |
| Mauro Conti, Roberto Di Pietro and Luigi V. Mancini: Suggested, the nature of mobile ad hoc networks (MANETs), often
unattended, makes this type of networks subject to some unique security issues. In particular, one of the most vexing
problems for MANETs security is the node capture attack: An adversary can capture a node from the network eventually
acquiring all the cryptographic material stored in it. Further, the captured node can be reprogrammed by the adversary and
re-deployed in the network in order to perform malicious activities. In particular, one of the most vexing problems for
MANETs security is the node capture attack: The nature of mobile ad hoc networks (MANETs), often unattended, makes
this type of networks subject to some unique security issues. In particular, one of the most vexing problems for MANETs
security is the node capture attack: An adversary can capture a node from the network eventually acquiring all the
cryptographic material stored in it. It also addresses the node capture attack in MANETs. Start from the intuition that
mobility, in conjunction with a reduced amount of local cooperation, helps computing effectively and with limited resource
usage network global security properties. Then, developed this intuition and use it to design a mechanism that to detect the
node capture attack. It support our proposal with a wide set of experiments showing that mobile networks can leverage
mobility to compute global security properties, like node capture detection, with a small overhead. |
| Jokhio, I.A and Kemp, A.H: Suggested, one of the most vexing problems in wireless sensor network security is the node
capture attack. An adversary can capture a node from the network as the first step for further different types of attacks. For
example, the adversary can collect all the cryptographic material stored in the node. Also, the node can be reprogrammed
and re-deployed in the network in order to perform malicious activities. To the best of the knowledge no distributed
solution has been proposed to detect a node capture in a mobile wireless sensor network. And hence propose an efficient
and distributed solution to this problem leveraging emergent properties of mobile wireless sensor networks. In particular,
introduce two solutions: SDD, that does not require explicit information exchange between the nodes during the local
detection, and CCD, a more sophisticated protocol that uses local node cooperation in addition to mobility to greatly
improve performance. Here also introduce a benchmark to compare these solutions with. Experimental results demonstrate
the feasibility of our proposal. For instance, while the benchmark requires about 9,000 seconds to detect node captures, CDD requires less than 2,000 seconds. These results support our intuition that node mobility, in conjunction with a limited
amount of local cooperation, can be used to detect emergent global properties. |
| Mauro Conti, RobertoDi Pietro, AndreaGabrielli, Luigi Vincenzo Mancini, and AlessandroMei: Suggested, Mobile Ad
Hoc networks are subject to some unique security issues that could delay their diffusion. Several solutions have
already been proposed to enforce specific security properties. Hover, mobility pattern nodes obey to can, on one
hand, severely affect the quality of the security solutions that have been tested over”synthesized” mobility pattern.
On the other hand, specific mobility patterns could be leveraged to design specific protocols that could outperform
existing solutions. Hence, there investigate the influence of a realistic mobility scenario over a benchmark
mobility model (Random Waypoint Mobility Model), using as underlying protocol a recent solution introduced for
the detection of compromised nodes. Extensive simulations show the quality of the underlying protocol. However,
the main contribution is to show the relevance of the mobility model over the achieved performances, stressing out
that in mobile ad-hoc networks the quality of the solution provided is satisfactory only when it can be adapted to
the nodes underlying mobility model. |
THE NETWORK SIMULATOR 2.33 (NS2) |
| Network Simulator (NS2) is a discrete event driven simulator developed at UC Berkeley. It is part of the VINT project. The
goal of NS2 is to support networking research and education. It is suitable for designing new protocols, comparing different
protocols and traffic evaluations NS2. A large amount of institutes and people in development and research use, maintain
and develop NS2. This increases the confidence in it. Versions are available for FreeBSD, Linux, Solaris, Windows and
Mac OS X. |
 |
| NS2 interprets the simulation scripts written in OTcl. A user has to set the different components (e.g. event scheduler
objects, network components libraries and setup module libraries) up in the simulation environment. The user writes his
simulation as a OTcl script, plumbs the network components together to the complete simulation. If he needs new network
components, he is free to implement them and to set them up in his simulation as ill. The event scheduler as the other major
component besides network components triggers the events of the simulation (e.g. sends packets, starts and stops tracing).
Some parts of NS2 are written in C++ for efficiency reasons. The data path (written in C++) is separated from the control
path (written in OTcl). Data path object are compiled and then made available to the OTcl interpreter through an OTcl
linkage (tclcl) which maps methods and member variables of the C++ object to methods and variables of the linked OTcl
object. The C++ objects are controlled by OTcl objects. It is possible to add methods and member variables to a C++ linked
OTcl object. |
PROTOCOLS |
Table Driven (Or Proactive) |
| The nodes maintain a table of routes to every destination in the network, for this reason they periodically exchange
messages. At all times the routes to all destinations are ready to use and as a consequence initial delays before sending data
are small. Keeping routes to all destinations up-to-date, even if they are not used, is a disadvantage with regard to the usage
of bandwidth and of network resources. These protocols ire designed to overcome the wasted effort in maintaining unused
routes. Routing information is acquired only when there is a need for it. The needed routes are calculated on demand. This
saves the overhead of maintaining unused routes at each node, but on the other hand the latency for sending data packets
will considerably increase. |
On-Demand (Or Reactive) |
| These protocols are designed to overcome the wasted effort in maintaining unused routes. Routing information is
acquired only when there is a need for it. The needed routes are calculated on demand. This saves the overhead of
maintaining unused routes at each node, but on the other hand the latency for sending data packets will considerably
increase. The nodes maintain a table of routes to every destination in the network, for this reason they periodically
exchange messages. |
 \ |
DYNAMIC SOURCE ROUTING PROTOCOL |
| DSR is a reactive routing protocol which is able to manage a MANET without using periodic table-update messages like
table-driven routing protocols do. DSR was specifically designed for use in multi-hop wireless ad hoc networks. Ad-hoc
protocol allows the network to be completely self-organizing and self-configuring which means that there is no need for an
existing network infrastructure or administration. |
Path-finding-process: Route Request & Route Reply |
| If node A has in his Route Cache a route to the destination E, this route is immediately used. If not, the Route Discovery
protocol is started: |
| 1. Node A (initiator) sends a RouteRequest packet by flooding the network |
| 2. If node B has recently seen another RouteRequest from the same target or if the address of node B is already listed in
the Route Record, Then node B discards the request! |
| 3. If node B is the target of the Route Discovery, it returns a RouteReply to the initiator. The RouteReply contains a list of
the “best” path from the initiator to the target. When the initiator receives this RouteReply, it caches this route in its Route
Cache for use in sending subsequent packets to this destination. |
| 4. Otherwise node B isn’t the target and it forwards the Route Request to his neighbors (except to the initiator). |
 |
ANALYSIS |
Topology Formation |
| Constructing Project design in NS2 should takes place. Each node should send hello packets to its neighbor node which are
in its communication range to update their topology. |
Adversarial Compromising |
| In UW SN setting, sensors are mobile, while the adversary is static. The envisioned adversary differs from other adversarial
models considered in most prior WSN security literature. The latter is static in terms of the set of sensors it corrupts, i.e., it
compromises k out of n sensor throughout the network lifetime. Our adversary (ADV) is stationary with respect to the
portion of the deployment area it controls; but, the set of compromised sensors changes as nodes move in and out of the
adversary-controlled area. |
Collaborative Intrusion Resilience |
| A number of techniques discover the sensor compromise when the adversary modifies the sensor code. Hence, if the
adversary is limited to “read-only” attacks and keeps the sensor code unchanged, there is no way to tell whether that sensor
has ever been compromised. This allows ADV to stay undetected and benefit from repeated attacks to the network. For
small neighborhood sizes (i.e., B ’ 2), the network exhibits a self-healing property that, with high probability, allows
sensors to regain secret state as soon as they move away from the adversary-controlled regions. |
ADVANTAGES |
| Node mobility helps to solve network connectivity problems caused by sensor failures and allows sensors to adapt their
sampling power to respond to precise events. |
| 1. a green sensor remains green until it moves at distance less from ADV ; |
| 2. a red sensor cannot become green without becoming yellow first as ADV eavesdrops on red sensors; |
| 3. a yellow sensor can become green only if it receivesat least one contribution from a green sensor. |
| Main motive of this paper is Minimize the red sensors, maximizing the green sensors using self healing mechanism. |
CONCLUSION |
| In this paper, it provides several contributions to the UWSN field. First, a new adversary model that spreads over different
areas of the deployment field is introduced. Second, when assessing self-healing protocols in autonomous distributed
systems there exist two novel metrics that, other than being interesting on their own, are of general help. Third, it is studied,
for a large range of system parameters, it understood that how the degree distribution of the adversary affects the selfhealing
protocol. Especially the latter shows a great capability to recover from compromising for several deployment
settings while incurring a negligible overhead—only local communications are required. Finally, repeated analysis and
extensive simulation do support these findings. |
References |
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