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International Journal of Computer Applications (0975 - 8887)
Volume 183 - No.29, October 2021
Black Hole Attack in Mobile Ad Hoc Networks:
Challenges and Directions
Noble Arden Kuadey
Ho Technical University (HTU)
Department of Computer Science
Lily Bensah
Ho Technical University (HTU)
Department of Computer Science
Baidenger Agyekum Twumasi
Ho Technical University (HTU)
Department of Electrical/Electronics Eng.
Carlos Ankora
Ho Technical University (HTU)
Department of Computer Science
Gerald Tietaa Maale
McCoy College of Education
Department of ICT
Anthony Mawuena Kuadey
St. Francis College of Education
Department of Mathematics/ICT
ABSTRACT
A mobile ad hoc network (MANET) is a dynamic wireless net-
work with no fixed infrastructure. The nodes move arbitrarily
and are capable of communicating with each other without a
central authority. MANETs are well suited for several situations
such as emergencies, vehicular networks, and military operations.
However, the flexible nature of MANET exposes it to attacks
such as black hole attacks. The black hole attack is considered
as one of the most predominant attacks that poses a threat to
MANET. In this attack, an illegitimate node informs a source
node of having the optimal route to the destination node, result-
ing in data packets being redirected and eventually dropped by
this illegitimate node. Several works have been carried out to ad-
dress this issue. This paper presents an overview of solutions pro-
posed to mitigate black hole attacks, research limitations to the
proposed works, and future works that need to be carried out.
General Terms
Security, Mobile Ad Hoc Network (MANET), Routing Protocol
Keywords
Black Hole Attack, Cooperative Black Hole Attack, Malicious
Node, Packets
1. INTRODUCTION
MANET is a type of network in which nodes are mobile, their net-
work topology changes dynamically and has no fixed infrastruc-
ture [1]. The mobile nodes in MANET can enter or leave the net-
work at any time. Furthermore, they use a multi-hop wireless net-
work to communicate with each other. Also, the nodes in MANET
communicate by using routing protocols. This enables the nodes
to discover the shortest route between the source and destina-
tion nodes. In addition, during communication between two mo-
bile nodes in a multi-hop network, each node can act as a router.
MANET’s infrastructure-less, multi-hop communication, mobile,
and dynamic features make it suitable for several scenarios such as
emergencies, vehicular networks, meetings and military operations.
However, due to the features of MANET, nodes in the network are
vulnerable to a wide range of attacks, such as black hole attack.
Black hole attack is very common and one of the security threats in
MANETs. Thus, it is important to detect and prevent black hole at-
tacks in MANET. Attacks in MANETs can be generally classified
into two main groups, passive and active [2]. In a passive attack,
an attacker keeps track of the information between the nodes with-
out modifying or disrupting the flow of information between the
nodes [2]. On the other hand, an attacker modifies or changes the
information exchanged between the nodes in active attacks [2]. Fur-
thermore, the attacker can disrupt the routing process by dropping
packets, injecting the packets, modifying the packets, etc. Secu-
rity mechanisms are required to mitigate various attacks, such as a
black hole in MANET. Several research works have been carried
out in order to propose security mechanisms and other techniques
for detecting and mitigating black hole attacks and their variants.
However, these security mechanisms have their drawbacks in miti-
gating black hole attacks in MANET. This paper discussed several
security mechanisms that researchers proposed to mitigate black
hole attacks, their existing challenges, and proposed future work
that can be carried out to mitigate black hole attacks in MANET.
The rest of the sections in this paper are organized as follows. Sec-
tion II explains routing protocols and discusses black hole attack in
MANET. Section III presents research works proposed to address
black hole attacks. Section IV presents research gaps and future
directions. Section V finally concludes the paper.
2. ROUTING PROTOCOLS AND BLACK HOLE
ATTACK
2.1 Routing Protocols
In MANET, routing protocols are responsible for determining the
optimal route to carry out communication between the source and
destination node [3]. They can be grouped into three major cate-
gories: proactive, reactive and hybrid [3], [4], [5]. Proactive rout-
ing protocols have routing tables that store information about all the
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Volume 183 - No.29, October 2021
nodes residing in the network, which is updated whenever there is
a change in the network [3]. Furthermore, each node has a rout-
ing table from which it checks the next hop node in the route
for a destination node before sending a packet to that node [6].
Proactive routing protocols are useful in network topologies that
do not have a large number of nodes. Landmark ad hoc routing
(LANMAR), Destination Sequence Distance Vector (DSDV), Fish-
eye State Routing (FSR), Global State Routing (GSR), Optimized
State Link (OSLR) routing protocol, Hierarchical State Routing
(HSR), Cluster Gateway Switch Routing Protocol (CGSR), Wire-
less Routing Protocol (WRP) and Zone-Based Hierarchical Link
State routing protocol (ZHLS) are examples of proactive routing
protocols [3], [5], [6]. Reactive routing protocol, also known as
on demand routing protocol, establishes routes for communication
between a source node and a destination node only when there is
a need to send a packet [3], [5], [6]. Also, reactive routing pro-
tocols do not periodically transmit topological information of the
network. Temporary Ordered Routing protocol (TORA), Cluster-
Based Routing Protocol (CBRP), Signal Stability-Based Adaptive
routing protocol (SSA), Ad hoc On-demand Distance Vector rout-
ing (AODV), Dynamic Source Routing (DSR), and Associativity
Based Routing (ABR) are some examples of reactive routing pro-
tocols [3], [5], [6]. The features of proactive and reactive routing
protocols are combined to form the hybrid routing protocol. This
routing protocol reduces the control traffic overhead that occurs
from proactive systems. Also, the hybrid routing protocol reduces
the route discovery delays that occur in reactive systems by main-
taining a routing table [3]. Some examples of hybrid routing pro-
tocol are Dual-Hybrid Adaptive Routing (DHAR), Adaptive Dis-
tance Vector routing (ADV), Zone Routing Protocol (ZRP), Sharp
Hybrid Adaptive Routing Protocol (SHARP), and Neighbor-Aware
Multicast Routing Protocol (NAMP) [3], [5], [6].
2.2 Black Hole Attack
The black hole attack is one of MANET’s most predominant and
dangerous attacks [6]. This attack occurs on the network layer [1].
In addition, it is an attack that enables an illegitimate node to re-
ceive a route request (RREQ) packet and reply with a fake route
reply (RREP) packet. The RREP packet contains a small hop count
and destination sequence number, thus making the source node be-
lieve that the malicious node is trustworthy and has the shortest
route to that particular destination node [6], [7]. Therefore, if the
source node transmits a data packet to the malicious node, which
happens to be the black hole node, the packets are dropped and
not forwarded by the malicious node [6], [7]. A black hole attack
is illustrated in Figure 1, where node 1 represents the source and
initiates an RREQ packet to find a path to node 7, the destination
node.The nodes 2, 3, 4, 5, and 6 are intermediary nodes. When node
6 receives the RREQ packet from node 1, it generates a fake RREP
packet and transmits it to node 1. Thus, upon receiving the fake
RREP packet from node 6, node 1 gets assurance that the shortest
route to node 7 is through node 6. Subsequently, it sends a data
packet to node 7. However, node 6, upon receipt of the data packets
from node 1, drops it and does not forward further to node 7. Thus,
node 6 is considered a malicious node that has initiated a black hole
attack. A variant of a black hole attack is a cooperative black hole
attack in which two or more illegitimate nodes collude together to
drop packets received [6]. One of the illegitimate nodes serves as a
forwarding node which replies the source node with a fake RREP
packet. As a result, when the source node sends the data packet
through that particular illegitimate node, it transmits the received
2 4
31 7
56
Destination
Source
RREQ
RREP
Fig. 1. A Black hole attack: Node 6 acting as black hole by sending fake
RREP
data packet to its co-operative partner in attack, who then drops the
forwarded data packet.
3. LITERATURE REVIEW
The authors in [8] proposed a mechanism that mitigates against
both the cooperative black hole and black hole attacks. Their work
incorporated check bit into the data routing information (DRI) table
and modified the AODV protocol. Their proposed solution detects
and eliminates black hole attacks and provides a secure path to the
destination node. Similarly, an identification mechanism was pro-
posed to identify malicious nodes [9] in MANETs. In their work,
”N” route request messages, further request messages, and further
reply messages were used in cross verification to identify malicious
nodes. Also, in [10], the authors proposed a tracking mechanism
that detects black and gray hole attacks.Their proposed solution
consists of two phases. In phase 1, data is securely transmitted to
the destination node. However, if there is a drop in packet forward-
ing, data transmission is stopped and the detection process is ini-
tiated. The second phase consists of detecting illegitimate nodes
and rendering the network inaccessible to them. However, their
proposed mechanism results in computational complexity. The au-
thors in [11] presented a detection mechanism using an Adaptive
Neuro Fuzzy Inference System (ANFIS) and Particle Swarm Opti-
mization (PSO) to detect black hole attack. However, their mech-
anism leads to an increase in computational load on the nodes in
the network and an increase in routing overhead due to accuracy
in decision making based on information sharing among partici-
pating nodes. Also, in [12], a detection mechanism was proposed
to prevent black hole attack. In their work, each node has a de-
tection mechanism that determines the suspicious value in order to
detect the high capability node in the network. Suppose the detec-
tion mechanism detects that a neighboring node has a suspicious
value that exceeds the threshold. In that case, it is flagged as an
illegitimate node and isolated from the network to prevent other
nodes from forwarding their packets to it. However, an illegitimate
node will not be detected as a black hole node if it can intelligently
keep its value within the acceptable threshold value. Saurabh et
al. [13] proposed a clustering technique in the AODV routing pro-
tocol to mitigate black hole attack. The nodes are grouped into clus-
ters, with each cluster having a head. Furthermore, the cluster head
is randomly selected. Check points are deployed in the network
to compare the number of received data packets to the number of
packets sent by the nodes. If the probability of packets reaching
the specified destination is less than the threshold value, then the
node is flagged as an illegitimate node. However, due to the mo-
bility nature of MANET, there may be some complications result-
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Volume 183 - No.29, October 2021
ing in nodes exiting and joining new clusters. Thus, it could lead
to false detection of the black hole node. Relief classification al-
gorithm was adopted to mitigate black hole attacks [14]. In their
work, they have an offline and online phase. In the offline phase,
the most important features from the black hole detection dataset is
used to improve the level of the detection rate. In the online phase,
network nodes with the previous features are frequently identified.
If an identified network node exceeds a predefined threshold value,
then it is flagged as a malicious node and excluded from the routes.
However, a malicious node will not be detected as a black hole
node if it can intelligently keep its value within the acceptable
threshold value. Naveena et al. [15] presented a trust-based rout-
ing scheme to prevent black hole attacks. Their solution consists of
the data retrieval (DR) table phase and the route formation phase.
The DR table phase identifies and manages data transfer of each
node, while the route formation phase predicts a safe path for the
transfer of data packet to the destination node. However, due to
the periodic update of trust values, their proposed solution leads to
routing overhead. Similarly, Arulkumaran and Gnanamurthy [16]
proposed a fuzzy logic rule scheme to detect black hole attack. In
their work, each node maintains its neighbor node’s trust value.
The trust value is computed prior to packet transmission. Further,
the route trust is computed based on the route trust and the trust
value is updated in each node’s routing table. If the valid route is
valid, then the most trusted node route is selected for transmission
of packets. However, due to the periodic update of trust values, their
proposed solution leads to routing overhead. Also Veeraiah and Kr-
ishna [17] proposed a detection mechanism using fuzzy clustering
and Bayesian rule to mitigate black hole attacks. In their work, the
nodes are grouped into clusters using a fuzzy clustering technique,
which uses the optimal centroid. Furthermore, the node trust table
consists of all trust values associated with each node in the network.
The detection mechanism analyses the node trust table and if a node
is found not to be trustworthy, it is flagged as an illegitimate node.
However, their proposed solution leads to maintenance overhead.
Furthermore, this detection mechanism is not suitable for networks
with many mobile nodes due to the clustering of nodes. Gurung and
Chauhan [18] presented a solution using a dynamic threshold value
to mitigate against black hole attack. Their proposed solution con-
sists of a dynamic threshold computing module, a detection mod-
ule and a prevention module. In the dynamic threshold computing
module, the source node calculates the dynamic threshold value for
the destination sequence number. The source node sends the SUS-
PECT packet to find an illegitimate node in the detection module
and then transmits the ALERT packet in the network. The illegit-
imate node does not participate in the route discovery process in
the prevention module, and other nodes ignore its reply. However,
their proposed solution cannot detect a black hole attack if the cal-
culated value is within the threshold value. Furthermore, their de-
tection scheme leads to a high routing overhead. Similarly, the au-
thors in [19] proposed a mechanism to mitigate black hole attack.
They defined a threshold value and verified RREP messages using
the defined threshold value. The source node verifies the destina-
tion sequence number of the RREP messages. If the destination se-
quence number is less than the defined threshold value, the node is
flagged as a malicious node. However, their proposed system is sus-
ceptible to black hole attacks if the attacker can intelligently keep
its destination sequence number within the defined threshold value.
A solution that modifies the AODV routing protocol and provides
a secured communication in the network was presented by the au-
thors in [20]. In their work, each node stores the node’s identifier,
the number of data packets, the number of RREQs and RREPs in an
activity table. Furthermore, the public keys of each node are stored
in a directory. In addition, each node digitally signs a packet before
sending it to another node. A node is flagged as an illegitimate node
if it is not a trusted node and its packet is not digitally signed. How-
ever, due to the involvement of keys, their proposed solution leads
to a high computational overhead. The authors in [21] presented
MBDP-AODV protocol, a protocol that uses dynamic sequence
number threshold to mitigate black hole attacks. Their protocol has
three phases. The source node computes the mean and standard de-
viation in the first phase using the destination sequence number.
The computed standard deviation number represents the threshold
value. The source node sends the suspect packet to the next hop
to identify a malicious node with a suspected destination sequence
number in the second phase. If any node has a hop count of 1 and
a suspected sequence number, an alert packet that has a suspected
sequence number and illegitimate ID is transmitted by the source
node. The malicious node is stopped from taking part in the route
discovery process in the final phase. However, their proposed solu-
tion results in a high routing overhead. Similarly, the authors in [22]
proposed an agent-based AODV protocol to mitigate black hole at-
tacks. In their work, when a route reply message is received, a node
designated as an agent examines the probability of all the nodes
in the incoming route request message. The nodes with the high-
est probability are forwarded to the blacklist and further examined
to see whether they are already part of the list. ALERT packets are
transmitted across the networks. Subsequently, route reply from the
nodes that are blacklisted is avoided. Panos et al. [23] developed
a detection mechanism that detects sudden changes in AODV’s se-
quence number parameter’s normal behaviour. Their mechanism
has training and normal phase. In the training phase, the cumula-
tive sum algorithm computes a random sequence Xn. This is trans-
formed into another random sequence Zn. Furthermore, the algo-
rithm computes the random sequence Yn. In addition, the thresh-
old value Nis calculated. The cumulative sum algorithm computes
Xn,Zn,Ynat each time interval in the normal phase. If Yncrosses
the threshold value Nat any time interval, a black hole attack is de-
tected. Thus, the normal phase triggers an alarm and notifies other
nodes on the network. However, should there be an attack during
the training phase of their detection mechanism, the attack cannot
be detected since they assume there was no attack during the train-
ing phase. The authors in [24] presented a routing algorithm that
sends forged packets. In their work, the source node sends a forged
RREQ packet that does not have a legitimate destination node ad-
dress. If a node with a fake RREP packet responds to the source
node, it is flagged as a black hole node. Furthermore, it is separated
from the routing table of nodes from the network by sending a le-
gitimate RREP message. An algorithm based on reliability factor
to prevent black hole attacks was developed by the authors in [25].
The algorithm initially assigns each node a reliability factor value
of 0.5. When a source node receives an RREP packet, it verifies the
reliability factor and the sequence number. If the reliability factor is
less than 0.5, the source node further sends a fake RREQ. If a node
replies, that node is flagged as a black hole node and the attack
is prevented. Similarly, Pathan et al. [26] implemented a detec-
tion mechanism that modified the AODV routing protocol to detect
black hole attacks. A bait timer is placed in the source code with a
value of Tseconds randomly chosen. The source node generates a
false RREQ packet and broadcasts it with an illegitimate destina-
tion address randomly created when the timer reaches Tseconds.
Thus, a node that replies to the fake RREQ packet is considered an
illegitimate node. Furthermore, to determine the illegitimate node
that responded to the fake RREQ packet, its identity is traced from
the RREP generator address field and added to a list of black hole
nodes. A source node broadcasts a genuine RREQ packet with an
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Volume 183 - No.29, October 2021
alert field, an intermediary node upon receipt of the RREQ packet
verifies the black hole node entry and marks it in the routing table.
Thus, all RREP packets from the black hole node are discarded.
Khan et al. [27] proposed a detection mechanism using an Ant
Colony Optimization technique to mitigate black hole attack. Sim-
ilarly, the authors in [28] proposed a detection scheme using hy-
brid Weighted Trust Based Artificial Bee Colony (WTABC) algo-
rithm to detect black hole attacks. Also, a detection scheme based
on Gray Wolf Optimization and trust setup data aggregation was
proposed to mitigate black hole and gray hole attacks [29]. In their
work, one node is selected as a trusted authority. All nodes’ infor-
mation is investigated and processed automatically by the trusted
authority. If malicious nodes are detected, the trusted authority re-
arranges them. The authors in [30] presented a detection mecha-
nism based on data control packet and a black hole check table that
mitigates and eliminates black hole nodes. Their work introduced
a data control packet that verifies the path taken by all nodes in all
steps. Furthermore, each node maintains a black hole check table to
decide which nodes are trustworthy. Zardari et al. [31] proposed a
dual attack detection mechanism based on intrusion detection sys-
tem (IDS) and connected dominating set (CDS) technique to detect
black hole and gray hole attacks in MANET. In their work, the
CDS technique creates small groups of nodes within the network.
The proposed technique then selects the IDS set of nodes from the
CDS subsets of CDS nodes that have sufficient energy. The IDS
node with the highest energy and that is trusted is then chosen to
frequently transmit status packets in order to detect the malicious
node. If an IDS node suspects a node to be malicious, it broadcasts a
block message to all nodes. Subsequently, all nodes stop communi-
cating with the suspected malicious node. Yasin and Abu Zant [32]
incorporated a timer and baiting technique in the AODV routing
protocol to mitigate black hole attacks. In their work, each node
has a bait-timer that is set to Tseconds at random. When it reaches
Tseconds, the source node generates and sends a bait request with
a randomized fake id. When a node replies to the source node with
a fake request, that node is marked as a black hole node and further
added to a list created for black hole nodes. In addition, they have
deployed a hello message transmission mechanism that enables ad-
jacent nodes to know each other. Thus, when a source node receives
a reply, it verifies the node’s ID with the node that has the opti-
mal path. In addition, if the verified node’s ID is in the list created
for black hole nodes, then it is dropped; otherwise, it verifies the
node’s id in the created neighbour nodes list and responds if it is in
the list. A detection mechanism called Secure-DSR was proposed
to mitigate black hole attack and to enable secured communication
in the network [33]. In this detection mechanism, the black hole
nodes are identified by examining the control packets used in net-
work routing. The drawback to this work is that they assumed all
participating nodes in the network are legitimate. In [34], an intru-
sion detection system was proposed to mitigate black hole attacks.
An Identification and Confirmation system was proposed in [35]
to identify black hole attacks in MANET. In their work, they con-
structed an attack tree for a black hole attack. Further, the authors
adopted a honeypot that makes use of the black hole attack tree to
identify various kinds of black hole attacks. Once a black hole at-
tack is identified, it is confirmed using the attack history database’s
record of previous attacks. However, their proposed system will not
be able to identify a black hole attack if prior information about the
black hole attacker is not captured in the constructed tree for the
black hole attack. In their work, Hossain et al. [36] proposed a
cryptography solution to mitigate the black hole attack in MANET.
However, their proposed mechanism may lead to high computation
overhead in the network due to the computation of keys and ci-
phers. Furthermore, the authors in [37] proposed a secure routing
protocol called SAODV to prevent black hole attack. In SAODV,
a requesting node does not immediately respond to a node with an
RREP data packet but waits until all other neighboring nodes reply
with their next hop details. A timer is set in the Timer Expired Table
upon receipt of the first request and collecting other requests from
different nodes. The requesting node stores the sequence number
and packet’s arrival time from each node in a Collect Route Reply
Table (CRRT). The time at which the first route request is received
is used to compute a timeout value. Furthermore, the requesting
node checks from CRRT whether there is any repeated next hop
node after the timeout value. Thus, if there is any repeated next hop
node present in the reply path, then it is assumed the path is safe,
otherwise the path is flagged as malicious. However, their proposed
SAODV is vulnerable to cooperative black hole attacks. El-Semary
and Diab [38] improved upon the works of the authors in [37] to
mitigate cooperative black hole attack. They proposed a protocol
called BP-AODV that mitigates both black hole and cooperative
black hole attacks initiated during the process of routing. Their
proposed work implemented a technique that established trusted
routes. The source node creates a challenge value and transmits it to
a destination node during a route request. Upon receipt of the chal-
lenge value by the destination node, it calculates the response value
as a function of the received challenge value and other generated
secret values. Furthermore, it transmits the response value to the
source node during the route reply while keeping the secret values.
In addition, it verifies the route by sending the secret values. Mistry
et al. [39] focused on improving the secure AODV. They proposed
a protocol called MOSAODV to guard against black hole attack.
In MOSAODV, the source node does not respond immediately to
the first RREP received. It rather stores all the RREPS received
from neighboring nodes. It analyses all the stored RREPS and dis-
cards RREPS that have very high destination sequence numbers.
Also, any node that transmits RREP with such a high destination
sequence is flagged as a malicious node. The MOSAODV proto-
col maintains the identity of the malicious node to prevent further
packets from such a malicious node. However, their proposed pro-
tocol leads to high computation and may also lead to false positives
where the proposed protocol classifies legitimate nodes as mali-
cious nodes. A detection mechanism called CBDAODV was intro-
duced by the authors in [40]. A source node in CBDAODV accepts
at least two RREP packets from different neighboring nodes. In ad-
dition, it uses an alternative route to validate the selected optimal
route. If the destination node confirms that no route exists between
the selected route, then the source node flags that node as an ille-
gitimate node that executes a black hole attack. Furthermore, it dis-
cards the earlier selected optimal route and chooses another routing
path for onward transmission of data packets. The authors in [41]
proposed a solution by modifying the AODV protocol thus pre-
venting any intermediate or destination nodes from modifying their
default operations. In their work, the source node stores all RREP
messages and calculates peak value. In addition, if the RREP’s se-
quence number is higher than the peak value, then that node associ-
ated with the high sequence number is flagged as a malicious node.
However, their solution involves high computation and comparison
of sequence numbers against peak value in determining a malicious
node. It can also lead to false positives. In their work, Chavan et
al. [42] modified AODV protocols to prevent black hole attacks.
The modified protocol uses two message techniques sent from the
source node to a destination node for verification. A source node
first sends a VERIFY packet to a destination node via an interme-
diate node and subsequently sends CHECKVRF. When the desti-
nation node receives the CHECKVRF packet, it verifies whether
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the VERIFY packet received earlier from the intermediate node
matches the source node ID. Thus, if there is a match, it sends a
FINALREPLY packet to establish a legitimate path. However, if
there is no match and the destination node does not reply with a
FINALREPLY packet, the intermediate node is flagged as a black
hole node. In [43], a secure AODV routing mechanism was pro-
posed to mitigate and eliminate black hole attacks. They introduced
a validity value in RREP. The source node verifies the validity value
of an RREP packet it receives it. Thus, if the validity value is null,
the source node flags that particular node as an illegitimate node
and drops the RREP packet. Their work is based on the assump-
tion that the illegitimate node has no idea about the validity value
in RREP. However, if the illegitimate node uses the same protocol,
it can analyze it and set a validity value before launching an at-
tack. Tamilselvan and Sankaranarayanan [44] introduced a protocol
called PCBHA to mitigate against cooperative black hole attack.
They proposed a fidelity table that will contain fidelity levels of ev-
ery node that participates. When a source node broadcasts RREQ
packets to its neighbouring nodes, it awaits RREP packets from its
neighbouring nodes. It chooses a neighbouring node with a higher
fidelity level and exceeds a predefined threshold value, and then
transmits data packets to the destination node. Furthermore, upon
receipt of the packet, the destination node sends an acknowledge-
ment to the source node. Subsequently, the source node increases
the fidelity level of the intermediate node to ensure a safe path to
the destination node. However, suppose the source node does not
receive any acknowledgement from the destination node. In that
case, it reduces the fidelity level of the intermediate node and con-
siders a possible black hole node on this path. The PCBHA protocol
is based on the source node receiving acknowledgement from the
destination node. However, a malicious node could send a forged
acknowledgment packet upon receiving the RREQ packet from the
source node, increasing its fidelity level. Dokurer et al. [45] pre-
sented a modified AODV routing protocol that mitigates black hole
attack. When a source node transmits an RREQ packet, it discards
either the first RREP or the first two RREP packets receive from
neighboring nodes. It rather chooses any subsequent RREP pack-
ets from the next hop. However, their solution is vulnerable to co-
operative black hole attacks. An authentication mechanism using
enhanced certificates was proposed by the authors in [46]. In their
work, nodes authenticate each other by creating certificates and is-
suing them out to neighboring nodes. In addition, without the use
of centralized authority, they generate a public key. Furthermore, to
support certification, they used Multicast Ad-hoc On-Demand Dis-
tance Vector Routing protocol. However, due to the generation and
involvement of keys, their proposed solution leads to a high com-
putational overhead. The authors in [47] presented a secure AODV
routing protocol that is able to mitigate black hole attack. Their
work enables the verification process directly between a source
node and a destination node through an exchange of random num-
bers. Similarly, the authors in [48] formulated a detection mech-
anism that mitigates black hole attack. In their work, the trueness
level helps avoid packet drop attacks by generating a trust hierarchy
and cooperation among legitimate nodes. Furthermore, their cryp-
tography mechanism enables the confidentiality of information in
data packets. In addition, it ensures that there is secured communi-
cation between two nodes and helps in the authentication. However,
due to the calculation of keys, their solution involves a high com-
putational overhead. A TRACEROUTE mechanism was introduced
by the authors in [49] to mitigate the source of collaborative black
hole attack. The mechanism breaks the collaboration between the
illegitimate nodes by eliminating and marking the source of col-
laboration. In their work, the source node transmits a trace packet
to the destination node and then sets a timer for Reversetrace. Fur-
thermore, the trace packet is forwarded by each intermediate hop
and also sets the timer for Reversetrace upon receipt of the trace
packet. Thus, if the timer set expires before the Reversetrace is re-
ceived, that particular next hop is marked as a collaborative black
hole node and the Reversetrace is sent through previous nodes to
the source node. However, their solution results in communication
overhead. Venkanna et al. [50] proposed a modified AODV routing
protocol that achieves cooperative routing. Their proposed mecha-
nism computes the final trust value (FTV) and the remaining energy
value of neighbouring nodes in the network. The computed values
determine a cooperative and trustworthy route between a source
and a destination node. However, their solution leads to an increase
in the consumption of energy as well as routing overhead.
4. RESEARCH GAPS AND FUTURE WORKS
Some of the research works proposed mechanisms that address sin-
gle black hole attacks. However, their proposed detection mecha-
nisms could not address cooperative black hole attacks. Further-
more, some other proposed mechanisms result in routing over-
heads. Similarly, other proposed solutions result in computational
overhead due to the generation and involvement of keys. Further-
more, some other proposed solutions result in false positives where
legitimate nodes are flagged as black hole nodes. Also, some de-
tection mechanisms use threshold values to prevent black hole at-
tacks. However, such mechanisms cannot prevent black hole at-
tacks if malicious nodes can keep their values within acceptable
threshold values. Future work should propose solutions that ad-
dress the increase in computation and routing overheads while pre-
venting black hole and cooperative black hole attacks. Furthermore,
future proposed works should address the drawbacks of threshold
values and false positives. To the authors’ best of knowledge, few
research works have proposed using blockchain technology to mit-
igate attacks in MANETs. However, not much work has been done
on using blockchain technology to mitigate black hole attacks in
MANETs. Blockchain technology, which has key features such as
decentralized, immutable, transparent and secure, can be leveraged
to implement a security mechanism that addresses black hole and
cooperative black hole attacks in MANET. The authors propose
that future works adopt blockchain technology to address some
weaknesses identified in the discussed research works.
5. CONCLUSION
MANET’s dynamic and infrastructure-less nature exposes it to
some security attacks, such as a black hole attack. Some research
works have proposed several variants of secured AODV rout-
ing protocols. Others proposed cryptography techniques, optimiza-
tion techniques, statistical threshold approach, control packets ap-
proach, and other detection mechanisms to detect black hole at-
tacks. This paper presented various proposed solutions that address
black hole attacks and cooperative attacks. In addition, this paper
identified some weaknesses in the proposed solutions. Furthermore,
it proposes future research work that needs to be carried out to de-
tect and prevent black hole and cooperative black hole attacks in
MANET.
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