Essay: Mobile Ad Hoc Network (MANET)

Abstract: Mobile Ad Hoc Network (MANET) is a dynamic, infrastructureless, Self Configuring network. Due to its natural characteristics, a MANET is vulnerable to many security threats. Blackhole attack is one of such attacks, that attempts to degrade the performance and reliability of the network. Since nodes are allowed to move freely within the network, it becomes very important to protect the communication among mobile nodes for the sack of security. Blackhole attack is an attack that drops a number of packets by performing packet forwarding misbehavior and violates the security to cause Denial-of-Service in MANET. In this paper we are investigating various techniques that can detect Blackhole attack in Mobile Ad-Hoc network and we have compared detection techniques with different matrices such as Average packet Delivery ratio and Average End-To-End delay.
 
Keywords: MANET; Black hole Attack; Denial-of-Service.

Introduction

Ad hoc networks are decentralized, wireless networks. They are infrastructureless networks, suitable for situations where setting an infrastructure is either not feasible or is costly. Mingyan et.al.,(1999), A mobile Ad hoc Network is dynamic in nature and in such a network nodes are allowed to move freely during the communication. A multi hop communication takes place among the nodes which are not in each other’s range. Due to its characteristics the network is vulnerable to many security attacks and it is used in places where infrastructure networks do not work well like battle field, disaster management etc.Sakshi (2014)

MANET security attacks are classified into Active Attack and Passive Attack. In a passive attack, the attacker does not try to disrupt the operation of a routing protocol but only attempts to show the message and traffic pattern.An attacker may not change any message in passive attack. In an active attack, messages may be modified by the attacker, however, these attacks involve actions performed by various adversaries, modification and deletion of exchanged data to attract packets destined to other nodes to the attacker for analysis or just to disable the network.Active attack includes impersonation, disclosure and Denial of Service attack.

A.Impersonation: In impersonation the attacker node first joins the network and then sends false routing information and also modifies the message.

B. Disclosure: In disclosure the attacker node discloses the location information about the target node.

C. Denial of Service (DoS) Attack: In DoS attack, the attacker jams the network or overflows the routing table of the target node and continues to send false routing information.(Radhika,2015;Panagiotis,2002;Hu,2002;semih,2007;Wu,2007;)

BLACKHOLE ATTACK

Blackhole attack is a prime security threat in MANET. In a blackhole attack, one malicious node utilizes the routing protocol and misleads the network by showing a shortest path to the destination node. But instead of forwarding the packets to its neighboring node, the malicious node eventually drops routing packets.(Perkins,1999;Maan,2011)

A blackhole attacker first invades into the multicast forwarding group (e.g., by implementing rushing attack) in order to intercept the data packets of the multicast session. The attacker then drops some or all of the data packets that it receives instead of forwarding the packets to the next node on the routing path. This type of attack often results in very low packet delivery ratio. Hoang and Uyen(2008)

Ad hoc on-demand vector routing (AODV) protocol is one of the most popular routing protocol in MANET. This protocol offers several benefits such as dynamic, self starting, and multihop routing. Furthermore, it can adapt MANET topology changes and automatically reject inactive routes, Perkins,(1999).

Unfortunately, this routing protocol is vulnerable to many security attacks (Maan; 2011,Ramaswamy,2003).

Among the existing attacks, blackhole attack is the most severe attack in AODV-based MANET, Ramaswamy,(2003).This attack can be generated by sending false routing information to the victim nodes to cause bogus route entries in the node routing tables. As a result, many incorrect routing exist, and cause bottleneck in the communication channels.

Routing Protocols

There are a number of routing protocols in MANET. In this section, we will discuss some of the famous routing protocols. Since the current routing information is not known so for that purpose prior communicating with a target node, the mobile node should broadcast its present status to the neighbors.

Routing protocols are classified on the basis of how the information is acquired. In the below classification we are going to discuss:

1. Proactive Routing Protocol

2. Reactive Routing Protocol

Proactive (table-driven) Routing Protocol

The proactive routing protocol is also called table-driven routing protocol. In this routing protocol, routing information is periodically broadcasted to the neighbors. Each node maintain their own routing table which not only records the adjacent nodes and reachable nodes but also the number of hops. In other words, every node has to evaluate the neighborhood as long as the network topology is changing.

Therefore, there is a disadvantage of overhead rise because as the network size increases, communication overhead within a larger network topology also increases. However, the advantage is that the network status can be immediately reflected if the malicious attacker joins the network. The most familiar types of the proactive protocols are destination sequenced distance vector (DSDV) Fan et.al.,(2011), routing protocol and optimized link state routing (OLSR) Royer and Toh (1999) protocol.

(Ramaswsmy,2003;Deng,2002)

Reactive (on-demand) Routing Protocol

The reactive routing is equipped with on-demand routing protocol. In contrast to the proactive routing that broadcasts the routing information; the reactive routing is simply initiated when nodes desire to transmit data packets. The major advantage of this strategy is that the wasted bandwidth induced from the cyclic broadcast can be reduced. The weakness is that passive routing method leads to some packet loss. Here we briefly describe two famous on-demand routing protocols i.e. ad hoc on-demand distance vector (AODV) Sanzgiri and Dahill (2002), and dynamic source routing (DSR) Perkins and Bhagwat (1994) protocol.

In AODV, each node only records the next hop information in its routing table but maintains it for sustaining a routing path from source to destination node. If the destination node can’t be reached from the source node, the route discovery process will be executed immediately.

SECURITY CRITERIA

Earlier, encryption and firewalls were used to protect the network which did not proved much efficient for a MANET infrastructure, for the major concern in MANET security is integrity, authentication, confidentiality, non-repudiation, availability to mobile users and anonymity as described below:

Availability

L. Zhou et. al.,(1999) Availability maintains the activeness of the network despite various attacks. Its major concern is the unauthorized and illegal access of resources. In some attacks, there could be possible disruption of routing protocol and continuity of services in the network.

Confidentiality

Confidentiality ensures protection from passive attacks. In military, the leakage of information can’t be compromised. Confidentiality ensures authorized access of information that protects data. Even it ensures the confidentiality of router location and packet information.

Authentication

L. Zhou et. al.,(1999)Authentication ensures that communicating parties are authorized parties by verifying their identity before communication. Ubiquitous networks require mutual authentication and for which mutual authentication protocols are required to prevent from attacks.

Integrity

Integrity guarantees that message delivered is neither modified nor duplicated or reordered for replay of original message. It also ensures that only the authorized parties retrieve the information or messages and the message is not corrupted or lost. Integrity ensures that messages are delivered to the authorized parties as sent.

Nonrepudiation

Nonrepudiation ensures that sender can’t deny about its previous communications. Receiver can always prove later that the particular message was sent by that alleged sender. It is also used for isolation and detection of nodes.

Scalability

Although the scalability does not affect security directly but as ad hoc network may consist of hundreds or even thousands of nodes and if the network is not enough scalable so that no new nodes can be added then the attacker compromise by the newly added node and gets access to the network.

Anonymity

This simply helps in ensuring the privacy of the personal information about the owner or user and it is not disclosed by the node.

DETECTION TECHNIQUES

Sukla Banerjee(2008) proposed a mechanism that is capable of removing and detecting the malicious nodes launching attack. According to us the approach consists of an algorithm which works instead of sending total data traffic at a single point of time they divide the total traffic into some small sized blocks. So that by ensuring an end-to-end checking malicious nodes can be detected and removed in between the transmission of two such blocks. Source node sends the prelude message to the destination node before sending packet or message to any block to alert it about the incoming data block. Traffic flow monitored by the neighbors of the each node in the route. After the end of the transmission destination node sends an acknowledgement via a postlude message containing the total number of data packets received by destination node. This information is further by the source node to check if the data loss is in tolerable range during the transmission , if the data loss is very high then the process of detection is initiated and the malicious node is removed by aggregating response from the network and monitoring nodes.

Satoshi Kurosawa et. al.(2007) proposed an anomaly detection scheme that uses dynamic training method in which the training data is updated at regular interval of time where the Multidimensional feature vector is identified to express state and status of the network of each node. According to us here each dimension is counted on every time slot. It uses sequence number of the destination to detect attack. The feature vector also includes number of RREQ messages sent, number of RREP messages received, and the average of difference of destination sequence number in each time slot between sequence number of RREP message and the one held in the list. Here mean time is calculated by calculating some mathematical calculation. Comparison of distance between the mean vector and input data sample. There is an attack when the distance is greater than some threshold value.

Shalini Jain et. al (2010) proposed a technique which is based on sending data in terms of small packet of equal sized blocks instead of sending complete data in one continuous flow. According to us in this technique the message flow is monitored independently at the both source and destination node. The monitored result is gathered by the backbone network of trusted nodes. According to result each node can locally maintain their own table of malicious or black listed nodes whenever node attempts to send data to any destination node and it can also alert the network about the malicious or black listed nodes. This list of malicious nodes may be used to discover secure paths from source node to destination by avoiding multiple black nodes acting in cooperation.

Anishi (2013), have proposed a new method MEAODV (Modified Enhanced AODV), which is based on the previous work EAODV(Enhanced AODV).According to our review and study the MEAODV is based on route discovery process for mitigating black hole effect which has few different condition parameters for checking the RREP message for better route discovery mechanism but has similar logic as in EAODV. In simulation, by varying nodes it offers better PDR than EAODV. They have concluded that MEAODV has outstanding results in terms of better performance Delivery Ratio(PDR) and less End-to-End Delay as compare to EAODV method.

Sanjay (2013),with the control packets called CONFIRM, CHCKCNFRM and REPLYCONFIRM, they have successfully detected the presence of Black Hole and hence successfully diverted all the traffic from it. According to our study, here even a slight modification in the protocol shows that how single run of the algorithm can detect the presence of collaborative Black Hole chains. They were also able to detect time varying and target varying Black Holes called the gray Holes with slight modifications in our method which produces 90 percent DDR for dynamic topology with an end to end delay, 0.9 times greater than that of conventional AODV. So, simulation results also show that algorithm is packet traffic efficient as well as time efficient.

Rutvij(2013) have investigated on many existing approaches on how to tackle Blackhole and Grayhole attacks and have discussed their previous work. Here they have proposed the modified protocol viz. MRAODV which is based on our previous work viz. R-AODV that removes the limitations in the existing mechanisms. According to us in the purposed approach during the route discovery phase MR-AODV isolates Blackhole and Grayhole nodes as R-AODV and sets up a new secure route for the data transmission. It deals on how to reduce the normalized routing overhead by decreasing number of forwarded reply packets which are sent by the adversaries. A simulation result which has been presented in form of graphs proves that the MR-AODV is the reliable solution which under various network parameters and traffic conditions. gives the significant improvement in PDR with acceptable average end-to-end delay and normalized routing overhead.

Sakshi et.al (2015), purpose a mechanism on Ad-hoc On Demand Distance Vector (AODV) which is a self starting routing protocol for MANETs. According to us in this purposed mechanism the security of this protocol is degraded with a particular type of attack know as “Blackhole” attack. In such type of attack the malicious node advertise itself as having the best path to destination while discovering route therefore interrupt the real communication & degrade network performance. In the proposed plot it has been conveyed that the base node in the system that builds the likelihood of distinguishing different vindictive nodes in system and further disconnect them from participating in any correspondence.

Vaishali and lata (2015) ,To maintain a strategic distance from single blackhole attack in MANET. According to us they have considered a component that utilizations FurtherRouteRequest packets. For distinguishing and evading agreeable blackhole attack they propose another method which utilizes Cooperative Cluster Agents. In the proposed approach, pass DRI and SRT-RRT table as a contribution to Cooperative Security Agents. In view of these sources of info the CSAs utilize cross checking and location stream instruments for recognizing helpful blackhole attack, once it is identified that can be maintained a strategic distance from by passing ready warning in the MANET. For execution of the proposed conspire they will utilize organize test system – ns-2.35. the proposed arrangement and contrast it and standard AODV protocol as far as throughput, packet delivery ratio and end-to-end delay.

Ayesha et.al.(2015) In purposed approach, every node in a network listens to its neighboring nodes promiscuously. According to us here in promiscuous mode, every node monitors the packet being forwarded by its neighbors in order to observe the behavior of neighbor regarding packet operation. Every node compares the neighbor information with the information it stores in its knowledge table. If both are same the node assumes that the packet is forwarded further, otherwise node waits for particular amount of time and checks the reasons for packet dropping. In order to confirm packets are sent to its neighbor, the nodes monitor the control packets as well as data packets to prevent selective dropping, as black hole attack drops selected packets. In order to monitor the forwarded packets, every node has to maintain knowledge tables with following entries: fm, rm if the values differ, the nodes are black hole nodes. A secure knowledge algorithm for mitigating black hole attack in AODV protocol has been proposed. The algorithm monitors the data packets that are being forwarded in promiscuous mode to ensure that the packets are delivered to destination node. If any node drops a packet our algorithm checks for the packet drop reasons first before declaring it as a black hole node, thereby preventing a trusted node from becoming a black hole node.

Mohamed A. et.al.(2016) presents another idea of Self-Protocol Trustiness (SPT) in which distinguishing a pernicious interloper is refined by consenting to the ordinary convention conduct and baits the malevolent node to give a certain acknowledgment of its malignant conduct. According to us in this purposed idea a Blackhole Resisting Mechanism (BRM) oppose such attacks that can be consolidated into any responsive directing convention has been introduced. Which doesn’t require costly cryptography or confirmation instruments, yet depends on privately connected timer and thresholds to group nodes as pernicious. No changes to the packets configurations are required, so the overhead is a little measure of computation at nodes, and no additional correspondence.

Thi Ngoc and chai kait(2016) for identify the individual bad conduct, they characterize sending proportion metrics that can recognize the behaviors of assailants from typical nodes. According to us in this the Malevolent nodes may abstain from being distinguished by conniving to control their sending proportion metrics. To constantly drop messages and advance the metrics in the meantime, aggressors need to make fake experience records habitually and with high manufactured quantities of sent messages. they misuse the anomalous example of appearance recurrence and number of sent messages in fake experiences to outline a vigorous calculation to identify intriguing aggressors.

Jitendra and Vinit (2014) purpose A novel cluster situated idea is proposed to improve security and proficiency of the system. According to us in this procedure safeguards the ideal execution of MANET in nearness of dark opening attack. The reenactment of the proposed technique is completed utilizing NS2 organize test system and the simulation results reflects the performance of scheme for detection and prevention of the black hole.

K A Arun (2016) , purposed a mechanism on Manet or Mobile Ad-Hoc Networks are self-forming systems which does not require a settled framework for its communication. According to us in this mechanism the MANET is assumed as a basic part in Military Communication and Disaster Management framework. At first there will be different nodes with discrete address relegated from an address pool, which will frame the system when required. Worm-hole attack and black-hole attack are the serious security issues confronted by Manet. The typical security components like encryption and confirmations have no enormous parts in these sorts of atttacks. The paper talk about the FPGA execution of black hole warm hole recognition and avoidance algorithm. The packets from a black hole or worm-hole are detected in the MAC-Physical layer itself by arbitrarily changing the Packet Travel Time (PTT). The Mac layer and the physical layer are actualized using Partial-Reconfiguration procedure so that the symbol rate, modulation scheme and coding rate can be changed haphazardly while the framework is running without utilizing additional equipment. Probe request and probe reaction messages are utilized to guarantee verification for the nodes for shaping the system.

P. Rathiga and S. Sathappan (2016). In this hybrid approach, the initiated monitor nodes gather the bundle stream information’s about the neighboring nodes. According to us in this hybrid approach at the point the distance metric is registered utilizing which two location thresholds are resolved. Distance metric for all the nodes is compared with very first threshold. On the off chance that the distance metric of a node is more noteworthy than the principal threshold, then the node is thought to be malevolent nodes. On the off chance that the distance metric of the nodes are beneath the second threshold but not less than principal threshold, the nodes are set apart as grey hole assailants while in the event that they are more noteworthy than the second threshold, the nodes are set apart as black hole aggressors. Exploratory outcomes demonstrate that the proposed hybrid black/grey hole detection approach recognizes and wipes out the attacks adequately with better throughput, packet drop rate, packet delivery ratio and routine overhead.

Neha and Anand (2016) Black-hole and gray-hole attack is one sort of attack which damages and attacks on MANET. According to us According to us in this attack the malevolent (undesirable node) occupy the information packets that it feels is having most brief and the freshest course to the goal node so sender advances every one of the information packets to it. In the wake of getting the information packets, it drops them to make a Denial of administration attack or procedures to concentrate data from the packet. Here a method is being proposed for identification of the black-hole or malicious node. In this strategy, another system a sort of trap technique is included in AODV protocol for the recognition of malignant nodes. At the point when the Black-hole node is distinguished after that a disturbing strategy is activated to make different nodes mindful of vindictive nodes.

COUNTERMEASURES

Most primary concerns in MANET is its security which is for the protection of communication and security of information. A network may have one or more vulnerabilities which can be exploited by an action called attack. It is necessary in network to perform routing and packet forwarding. So the numbers of security mechanisms has been made to counter measure the attack on the malicious attacks by the attacker. Preventive and Reactive Mechanism are the types of mechanisms which are used for the protection of MANET.

Mitigation Techniques against Black Hole Attack

The Network Layer are more likely to be exploited as this layer is more vulnerable for attacks than any layer in MANET. Various security threats are imposed on this layer K. Sanzgiri et.al (2002).For the security maintenance, one way is to use the secure routing protocol. Source authentication is used in the provoking of routing messages. Digital signature, message authentication code (MAC), hashed MAC (HMAC) can be used to maintain security at a certain level and by the use of IPSec it can be attained at network layer in internet. Authenticated Routing for Ad-Hoc Networks (ARAN) is another routing protocol which provides the security and protection from Black Hole attack. This routing protocol are used where there is threat and possibilities of change in sequence number, hop count modification, change in source routing and spoofing of destination addresses H. Deng et.al.,(2002)

Mitigation Solution by Deng

The proposed solution by Deng et.al (2002) gives the approach of preventing black hole attacks by modifying the AODV routing protocol. This approach is used for identification of the existence of the advertised route of the black hole by appending in route reply (RREP) packets of the intermediate node by the address of the next hop node. After receiving the route reply (RREP) packet from intermediate node, source node takes out and finds information of the next hop node and sends supplementary request to the next hop node for verification of routing metric value with the next hope node. For confirming the route information next hop node of neighbor sends back the supplementary reply packet to the sender. In case the source does not get back this supplementary reply, it specifies that the route contains the malicious nodes. This route is removed from the routing table and an alarm message is sent to other nodes in the network to isolate malicious nodes. The drawback of this approach is that cooperative black hole attacks can be launched on it. Furthermore, this solution causes additional routing overhead due to supplementary request and supplementary reply for verification.

Mitigation by Destination Sequence number

The proposed solution by N.H. Mistry in (2009) give the approach that source node verifies the RREP destination sequence number by analyzing the RREP messages which arrive within the fixed and a predefined time interval by using heuristic method. If sequence number is found to be very high then the expected, then the sender node of the respective RREP will be marked as malicious node due to the high sequence number. The major issue in this method is the latency time during the route discovery process. This is because before routing table can be updated the source node has to wait until waiting time period expires. The node still suffers from the latency even if there is no attack in the network.

Mitigation by Securing Routing Table Update

Kamarularifin Abd Jalil et.al.,(2011) have presented novel called ERDA by studying the limitation of previous proposed methods. ERDA is used to detect, prevent and isolate the Black hole nodes in MANET. They have shown that ERDA enhances existing function recvReply() in the AODV protocol by implementing a simple mechanism to detect and isolate malicious nodes and improving the process of updating routing entry. The enhancement of this only involves minimum modification to existing AODV protocol flows. Moreover, ERDA does not incur high and delay overhead (Delay) and routing overhead (NRL).

Mitigation by Using Optimal Path Routing and Hash

Hizbullah Khattak et.al.,(2013) A slight modification in AODV can avoid the blackhole attack. In this solution, the source node initially works as per the AODV routing protocol. It broadcasts Route Request (RREQ) from source node to destination node. As soon as the destination node or intermediate nodes receive the Route Request (RREQ), they send back Route Reply (RREP) messages on the same route from which they have received the RREQ messages by the previous node or the source node. Also for the avoidance of black hole the first RREP message coming from intermediate node is always discarded when the source node sends RREQ to the neighbour node. Here, the second shortest route is preferred over the first shortest route for the transmission of the packets and data. This solution prevents the network from black hole attack by using the second shortest path for sending packets to destination. It would not be easy for black hole or grayhole node to monitor the entire network topology and examine where to place themselves in the network and mislead the source node that it has the second shortest route node to the destination. The black hole attack can easily be avoided by using this technique as the malicious node can never send the RREP message of the second shortest route to the source node as the malicious node usually generates the RREP message of high sequence number to be treated as the first shortest route node.

Mitigation by Time-Based Threshold Detection Scheme

The proposed solution by Tamilselvan et.al (2007) gives the solution for the detection of black hole and ensuring the reliability of the route before sending the data packets over it. This solution provides the modification of AODV protocol for obtaining the desired goals as follows: The source node does not start sending data packets immediately after receiving the RREP message from any intermediate node. It ensures the safe route for sending data packets by waiting to receive the RREP messages from other neighboring nodes. The source node then sets the timer for collecting the RREP messages from neighbor nodes and maintains a table for all the received RREP messages. When the times get over, source node is considered and selects the most reliable route for packet transmission which contains the more repeated common nodes from the table. If no repeated common nodes are found, then the source node considers the route which provides information about its next hop in the route. It has a drawback of processing delay due to causes additional delay and wait strategy for waiting for the reply from neighboring node.

Simulation & Results:-

Figure1: Average Packet delivery ratio for various detection techniques

In G-AODV with the help of control packets called CONFIRM, CHCKCNFRM and REPLYCONFIRM and has diverted traffic from it. MRAODV, ADHOC routing are prone to various attacks such as DoS attack. This is only due to ignorance of security aspect during their designs. MEAODV migrates the black hole attack by controlling the routing update with new condition, parameter and removing the redundancy in detecting malicious nodes. TAPPING-AODV gives the facility to choose the best solution for the routing protocol and also provides the knowledge on how to use those schemes in any environment

Average Packet Delivery Ratio is defined as the average of the ratio of the received packets by the destination and the total number of packets generated by the source. Here in the figure 1 as the number of Malicious nodes increase PDR of standard AODV under all parameters starts declining. There is very sharp decline in TAPPING-AODV, the performance of G-AODV is better than the TAPPING-AODV as the decrease in PDR, also the performance of MR-AODV is better than the G-AODV. There is very small decrease in PDR in MR-AODV as it does not breakout under attack and isolates all the malicious nodes and its performance is better than the other four and gives approx 87% PDR in this case.

Average End-to-End Delay refers to the average time taken to transmit packet from source node to destination node. Here in this figure we can conclude that there is increase in end-to-end delay as the number of malicious nodes increases in the network. This is because during the transmission process of packets from source node to destination node the the number of Malicious nodes are found and an algorithm is called either to drop that packet or to start the packet transmission from the initial state. This consumes time whenever a malicious node is found that results in an increase in average end-to-end delay as the number of malicious nodes increases. In figure2 we can conclude that the average ETE Delay is increasing in all. Tapping AODV has the maximum ETE Delay than other four and MR-AODV has the minimum ETE Delay than all the other four.

Figure2: Average End to End delay for various detection techniques

Figure3: Average Throughput for various detection techniques

Average Throughput is the total amount of packets successfully transmitted from source node to destination in a particular time.In figure 3 we can see that the average throughput is decreasing as the number of malicious node is increasing. Here we have concluded that the BHAODV (Black hole AODV) attack has the minimum throughput. This is because a large amount of packets are dropped during the transmission of packets, so in this attack the number of malicious nodes in the network increases so the packet drop also increases. MRAODV and MEAODV detection technique has the maximum Average throughput which indicates that these two detection technique is the best among all when we use this technique because there is very less packet drop.

Figure 4: Detection rate for various detection techniques

Detection Rate is the ratio of the total number of nodes attacked to the total number of attacks detected in the network.

Detection Rate = No. of Detected Attacks

Total Attack

In the figure 4, it can be seen that there is an increment in the detection rate as the number of malicious nodes increases. The probability of detection of attack increases as the size of the blackhole increases. Tapping AODV detection technique has the least detection rate which shows it is not capable of detecting the attacks on node on large scale. MEAODV and MRAODV has the highest detection rate i.e. they are capable of detecting the maximum attack in the network.

Conclusion

The simulation result shows that the performance of tapping AODV is better than AODV.

IN MRAODV, during route discovery phase MRAODV isolates Black hole and sets up a secure route for the transmission of the data. It also attempts to reduce normalized overhead by decreasing the number of the forward reply packets which have been sent by the adversaries. Simulation result which has been presented in the form of graphs proves that MR-AODV is a reliable solution that gives significant improvement under various parameter and various traffic conditions in PDR with acceptable average end-to-end delay .

In GAODV, slight modification in the protocol can show that a single run algorithm can detect the presence of Black Hole, also with the modification in their method they have also achieved the success in detection of time varying and target varying black holes. Their simulation result also shows that their algorithm is packet traffic efficient as well as time efficient

In MEAODV, performance by varying the malicious node, is slightly greater than the End-To-End delay and is slightly less and PDR increases comparatively on increasing the number of nodes, but end-to-end delay fluctuates. Here they have concluded that the AODV with MEAODV method gives comparatively better performance.

According to the above study we have concluded that the ME-AODV and MRAODV detection techniques are the best detection technique as it provides the best solution for mitigating black hole attack by controlling the routing update with new condition parameter and removing the redundancy in detecting malicious nodes and varying different parameters.

MRAODV and MEAODV detection technique has the maximum Average throughput which indicates that these two detection technique is the best among all when we use this technique because there is very less packet drop.

MEAODV and MRAODV has the highest Detection rate i.e. they are capable of detecting the maximum attack in the network due to the increase in the black hole attack that the hop count of the neighbor.

Author’s Contributions

We have studied various detection techniques that have been proposed by various authors and have taken some of the detection techniques and generated a matrix graph. We have analyzed the various detection techniques in different parameters.

Sunil Kumar Jangir : Survey work, analyzed all schemes and compared them, worked on performance matrices and drafted the manuscript.

Naveen Hemrajani : Survey work, analyzed all schemes and compared them and drafted the manuscript.

All authors have done brief reading and have approved the final manuscript.

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