Figure 2
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... Ad Hoc Network using the AODV protocol faces an attack named Blackhole attack where a malicious node or Blackhole node consumes the network traffic and drops all data packets. To explain the Black Hole Attack, an example is shown in the following Figure2. In Figure 2, we assume that Node B is the malicious node or Black hole node. When Node A broadcasts the RREQ message for Node D to establish a path for data transfer, Node B immediately responds to Node A with a false RREP message showing that it has the highest sequence number of Node D, as if it is coming from Node D. Node A assumes that Node D is behind Node B with 1 hop count and discards the newly received RREP packet come from Node C or E. Node A then starts to send out all data packet to the node B. Node A is trusting that these packets will reach Node D but Node B will drop all data packets. The malicious node or Black hole node takes all the routes coming up to itself. It stops forwarding any packet to any other nodes. The network operation is hampered as the black hole node B consumes the packets easily [7]. ...
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... A large portion of current MANET research is devoted to enhancing routing protocols based on various processes and establishing routing decisions on a range of constraints [12][13][14][15][16]. Furthermore, it has been demonstrated that making minor changes and adding new metrics to the operation and structure of routing protocols can enhance the security and performance of real-time applications [4]. ...
... Authors in [5] recently explored support vector machine (SVM) and classification trees techniques for identifying intrusions. The concepts of neuromorphic rules are used in developing another IDS model, which uses symbols rather than numeric values to determine each attack by indeterminacy, non-membership, and membership in a hybrid framework of genetic algorithms (GA) and self-organized features maps (SOFM) [14]. When employing this approach, the load of a network administrator, the required computations, and the communication overhead of the system remain major obstacles. ...
In terms of security and privacy, mobile ad-hoc network (MANET) continues to be in demand for additional debate and development. As more MANET applications become data-oriented, implementing a secure and reliable data transfer protocol becomes a major concern in the architecture. However , MANET's lack of infrastructure, unpredictable topology, and restricted resources, as well as the lack of a previously permitted trust relationship among connected nodes, contribute to the attack detection burden. A novel detection approach is presented in this paper to classify passive and active black-hole attacks. The proposed approach is based on the dipper throated optimization (DTO) algorithm, which presents a plausible path out of multiple paths for statistics transmission to boost MANETs' quality of service. A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron (DTO-MLP), and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical (LEACH) clustering technique. MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights of minor features. This hybrid method is primarily designed to combat active black-hole assaults. Using the LEACH clustering phase, however, can also detect passive black-hole attacks. The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach. For diverse mobility situations, the results demonstrate 1906 CMC, 2023, vol.74, no.1 up to 97% detection accuracy and faster execution time. Furthermore, the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.
... To avoid the BH and construct a safe protocol, this approach does not need a reply from an intermediary node. Unless RREP packets arrive from more than two nodes, the approach provided in [11,12] requires a source node to wait. As soon as the source node gets numerous RREPs, it checks to see whether there are any shared hops. ...
Wireless network technically, refers to the category of network in which communication is carried out without using wires. In modern era wireless network has great importance because the communication is taking place with the use of radio waves. Thus, the use of ad-hoc network starts yielding a great importance in variety of applications. The certain research work is carried out in this particular field. MANET is a constructed from various mobility in the form of mobile nodes and anytime without any need of fixed infrastructure. MANET can be made on fly due to lack of fixed infrastructure. MANET is numerous threats types of attacks due to dynamic changing topologies and wireless medium. Security of the MANET becomes one of the challenging tasks. Black hole attacks is the main type of attack that are possible in MANET. Black hole node not forward any data packets to the neighbour node instead it drops all the data packets. Black hole attacks are bit hard to detect due to lack of centralized access. This research work concentrates to enhance the security of MANET by identifying and blocking black hole assaults from occurring. A reactive routing system such as Ad-Hoc on Demand Distance Vector has previously been used to address security problems in the MANET (AODV). Various attack types were investigated, and the consequences of these assaults were detailed by describing how MANET performance was disrupted. Network Simulator 3 (NS3) is used for the simulation process.
... In [16], they provided a black-hole detection scheme developed using dynamic threshold values to severe changes in the regular performance of the network connections. Moreover, in [17] another solution for detecting the black-hole attack is investigated using the first route reply by which the response from the malicious nodes determined and therefore deleted this transaction. Since this solution leads to a decrease of data loss with the increase of the throughput, it cannot discriminate the maliciousness of the packet sinking. ...
Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value.
... A MANET network is distinguished by several features such as dynamic topology, energy constraint, limited and variable link capacity, and limited physical security. Mobility in this type of network is not only an advantage, but also a disadvantage that makes them vulnerable for all attacks [1,2]. One of the serious problems with MANET networks is security since attacks can be internal or external [3]. ...
... In our work, we try to find AACK-based schemas for detecting black hole attacks [2,10] on the AODV [1,3] protocol. The contribution of this study is threefold: (1) Detect the intrusion of a black hole attack or multiple black hole attack with a generic, fast and effective model; (2) Guarantee a breast path; (3) Increase the rate of packets received, which increases the packet delivery ratio (PDR) and Throughput with a minimum End To End Delay. ...
... Ad hoc on demand vector (AODV) [1,3] is based on a distance vector algorithm, and it only requests the route when needed. Each intermediate node that is in the route between a source node and a destination node must keep a routing table that contains: The address of the destination, the next node to use to reach the destination, the distance in node number, the sequence number of the destination and the expiry time of the table entry. ...
Mobile Ad hoc NETworks (MANET) are networks without infrastructure. The communication range among nodes is limited, where several hops are needed to transmit a packet from the source to the destination. These networks have a constantly changing topology due to its mobile nodes and their arbitrary connections, which make it vulnerable for different attacks. One of the most important attacks in MANET is the black hole attack which degrades the performance of the network by removing all the packets passing through it. There are several techniques for detecting black hole attacks in the ad hoc on demand vector protocol. In this paper, a new approach based on AACK is proposed. The proposed system is to detect the single and multiple black hole attacks by intrusion detection system with SPlitted AACK technique. The system is robust enough to detect all black hole attacks by using an iterative split of the main path until the detection of the malicious nodes. Network simulator 2 is used for simulation. We tested our system on different networks with different network sizes and different numbers of attacks, and we compared our results with some existing intrusion detection system techniques.
... The authors in [7] provided a blackhole detection mechanism that develops a dynamic threshold to detect the severe changes in the normal behavior of the network transactions. Additionally, in [8] another solution based on detecting the blackhole attack by always considering the first route reply is the reply from the malicious node and deleted this transaction. Although this solution decreases the data loss and increases the throughput, also it cannot distinguish between malicious and benign packet sinking. ...
MANETs are still in demand for further developments in terms of security and privacy. However, lack of infrastructure, dynamic topology, and limited resources of MANETs poses an extra overhead in terms of attack detection. Recently, applying modified versions of LEACH routing protocol to MANET has proved a great routing enhancement in preserving nodes vitality, load balancing, and reducing data loss. This paper introduces a newly developed active and passive blackhole attack detection technique in MANET. The proposed technique based on weighing a group of selected node's features using AdaBoost-SVM on AOMDV-LEACH clustering technique is considered a stable and strong classifier which can strengthen the weights of major features while suppressing the weight of the others. The proposed technique is examined and tested on the detection accuracy, routing overhead. Results show up to 97% detection accuracy in superior execution time for different mobility conditions.
... This protocol is the most useful because it prevents routing loops by relying on each node to maintain its destination sequence number. It operates into two phases where mobile nodes cooperatively discover and maintain routes by sharing RREQ, Route Reply, Route Errors and HELLO control messages [13], [14]. Figure 1 is constructed with seven nodes to demonstrate the route discovery process of AODV in steps. ...
A mobile ad hoc network (MANET) performs a routing and data forwarding obligations in a trustless environment where mobile nodes are not controlled and administered by third parties, it usually suffers from multiple attacks such as the blackhole attacks. This blackhole attacks cause two extreme effects on the network if the malicious nodes are not eliminated. Firstly, it deliberately alters the original data route sequence with an injection of false routing information comprises of the counterfeit destination sequence number and smallest hop count. Secondly, it becomes too greedy to send data to other nodes after receiving from a genuine node. Therefore, this may delay the data transmission process in the act of data dropping. To protect the routing discovery process of an ad hoc on-demand distance vector (AODV) protocol from the blackhole attack, we present a mitigation algorithm based on standard deviation outlier detection to determine a threshold value for validating the route reply (RREP) destination sequence number before route establishment. The simulation shows that the algorithm can detect the malicious nodes and performs better than AODV in a blackhole attack environment.
... The variations in the QoS parameters have depicted the network performance has decreased predominantly when there is a presence of black hole attack. [18] has given a solution for detecting black hole attack using AODV routing protocol, namely, Black hole detection system (bds)AODV The researchers has considered initial route reply as the feedback for malicious node and removed. The subsequent one is considered for the route reply saving method because it is taken as the destination node. ...
... The average value of TDR is 80.38%. The comparison has been drawn with bdsAODV [18], RID-AODV [19] and FSAODV [proposed]. Throughput represents the number of packets received at the destination with respect to simulation time. ...
... Here, Throughput has been calculated in kbps. The average value of bdsAODV [18] is 4.8 kbps; the average value of RIDAODV [19] is 1.84 kbps whereas the proposed FSAODV has an average value of 7.2 kbps. So, it is evident from the analysis that FSAODV [proposed] has better throughput as compared to a conventional methods. ...
A temporal network creates various issues which are managed by nodes, communicating with the base station. The flow of packets with different routes usually attacked by malicious nodes, such an attack is also termed as black hole attack. A novel FSAODV mechanism is proposed in this paper to prevent the information from malicious nodes by following the Ad-hoc on demand distance vector (AODV) protocol. The detection of threats due to the black hole and route enhancement is implemented using the bio-inspired algorithms. Firefly algorithm and Support Vector Machine (SVM) algorithms are developed to determine the throughput, Packet Delivery Ratio (PDR), and TDR. A comparative analysis has been done to portray the success rate of proposed work. For the comparison, research works of Ashok Koujalagi and Rushdi A. Hamamreh are considered. 33.33% enhancement has been noted in throughput with Ashok Koujalagi and74.44% with Rushdi A. Hamamreh. 21.4% enhancement has been seen in PDR with Ashok Koujalagi and 91.71% with Rushdi A. Hamamreh.
A mobile ad hoc network (MANET) is a group of mobile, wireless nodes which helpfully and
unexpectedly structure an IP-based network. Nodes that are a piece of the MANET, yet past one another's
wireless range impart utilizing a multi-hop course through different nodes in the network. The decision of
scheduling process which queued packet to process next will significantly affect the general end-to-end
execution when traffic load is high. There are a few scheduling strategies for different network situations. It is
seen that totally lowering the delays isn't for all intents and purposes conceivable, nonetheless, delays can be
controlled to go past certain threshold range. Hybrid Congestion Control is employed to minimize congestion
in MANETs through optimal data handling. The proposed model in our work is an innovative method to
manage congestion alongside reduction in time taken for transmission.
Mobile Ad hoc NETworks (MANET) are networks without infrastructure. The communication range among nodes is limited, where several hops are needed to transmit a packet from the source to the destination. These networks have a constantly changing topology due to its mobile nodes and their arbitrary connections, which make it vulnerable for diferent attacks. One of the most important attacks in MANET is the black hole attack which degrades the performance of the network by removing all the packets passing through it.
There are several techniques for detecting black hole attacks in the ad hoc on demand vector protocol. In this thesis, a new approach based on AACK Adaptative ACKnowledgement is proposed. The proposed system is to detect the single and multiple black hole attacks by intrusion detection system with SPlitted AACK technique. The system is robust enough to detect all black hole attacks by using an iterative split of the main path until the detection of the malicious nodes. Network simulator 2 (NS2) is used for simulation. We tested our system on diferent networks with diferent network sizes and diferent numbers of attacks, and we compared our results with some existing intrusion detection system techniques.
On the other hand a technique based on machine learning, more precisely on the random
forest algorithm with the selection of the best features,is also proposed. The latter is tested on the NSL-KDD dataset. The results found were very satisfying in terms of Accuracy 99,66%, Precision 99,85 %, Recall 99,83 % and F1-Score 99,84%. Thus, the results have improved when compared with those of other techniques.
