IEEE Consumer Electronics Magazine - November/December 2022 - 29

Table 2. Summary of various attacks detection mechanisms in vehicular networks.
Type of
attack
Denial of
Service
Blackhole
Timing
Sybil
Man-in- themiddle
Gray
hole
Warm hole
Malware
injection
Key idea
To mitigate jammer and improve
valid transmissions
To reduce malicious nodes and
improve reliable transmission
To study the safety-critical
vehicular communication
To detect Sybil nodes and reduce
false identities
To identify the malicious nodes
from sharing content
To find the presence of malicious
nodes
To detect in-band and out-ofband
wormholes
Malware injection mechanisms
using an edge-centric framework
Mode of
routing
Networkcentric
Nodecentric
Datacentric
Nodecentric
Nodecentric
Nodecentric
Nodecentric
Edgecentric
Mode
of
transmission
V2I
V2V
V2V/V2I
V2V
V2V
V2V
V2V
V2I
Limitations
Not able to handle jammers from
multiple malicious vehicles
No critical path detection mechanism
used between two-hop transmissions
Not considered any security detection
mechanisms
Not scalable when the number of Sybil
nodes in the IoV network are very high
No intelligent detection mechanism used
to identify the malicious vehicles
Difficult to identify similar types of
malicious nodes in an edge-centric IoV
Not suitable for large number of
malicious vehicle nodes, present in the
IoV network.
No mechanisms developed to reduce the
device or server-side malware injections
Confidentiality This type of security requirement
must prevent the critical safety data and
sensitive information of the users from unauthorized
users. Thus, the stored data should
be accessed and shared only with legitimate
entities.
ATTACK DETECTION STRATEGIES
FOR EDGE-CENTRIC IOV NETWORK
There are plenty of research works available in
the literature to mitigate the security issues in IoV
networks. In such networks, all the participating
vehicles on the road need to exchange traffic information
at any time, any place, any path, and to any
relevant network. Despite benefits, this network
faces a lot of security challenges due to highmobility
and frequent changes in topology. Therefore, it
is very important to inspect various detection
mechanisms to minimize the attacks and provide
more secure communication in edge-centric IoV
networks. Table 2 describes the importance
and limitation of some key attack detection
mechanisms in the vehicular networks.
Boche et al.6, have designed a suitable security
detection mechanism using Turing machines to
mitigate the jammer in between senders and
receiver node. However, this mechanism failed to
focus on detecting multiple jammers within the
same network. To reduce the packet drop ratio
November/December 2022
and detect the malicious nodes, Hassan et al.7
have presented an intelligent black hole attack
detection scheme. Based on the recent real-world
security attacks on automotive systems, Hasan
et al.8 have extensively studied the potential
threats and security detection mechanisms in the
vehicle-to-everything (V2X) communication framework.
Further, a novel mechanism has been proposed
in Quevedo et al.'s work9 to detect Sybil
nodes and false identities in the vehicular networks.
However, this technique is not scalable
when thenumber of Sybil nodes is very high.
To identify the malicious vehicle nodes, a
man-in-the-middle attack resistance trust model
has been developed for connected vehicles in
Ahmad et al.'s work.10 However, this model
contains no intelligent detection mechanism to
automatically predict the opposing vehicles. An
software-defined networking based EC-SDIoV
approach has been considered to quickly identify
the presence of malicious vehicle nodes.11 However,
this approach cannot able to support a large
number of malicious vehicular nodes at the
edge-centric IoV network. In the work of Tahboush
and Agoyi,12 a hybrid wormhole attack
detection algorithm has been proposed to
detect in-band and out-of-band wormhole attack
between two successive vehicle nodes based on
the hop count. Based on this algorithm, only
the detection of wormhole attack between two
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