Evaluation of Detection Method to Mitigate DoS Attacks in MANETs

Alsumayt, Albandari; Haggerty, J. S.; Lotfi, Ahmad

doi:10.1109/cais.2018.8441952

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Therefore, a malicious and fake route will be created. When this route has been set up, now it is depending on the node whether to drop the packet or to forward them to an unknown (Alsumayt et al, 2018).…”

Section: Black Hole Attackmentioning

confidence: 99%

Smart detection and prevention procedure for DoS attack in MANET

Muntaheen¹,

Bhowmick²,

Rumman³

et al. 2021

RRST

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A self-organized wireless communication short-lived network containing collection of mobile nodes is mobile ad hoc network (MANET). The mobile nodes communicate with each other by wireless radio links without the use of any pre-established fixed communication network infrastructure or centralized administration, such as base stations or access points, and with no human intervention. In addition, this network has potential applications in conference, disaster relief, and battlefield scenario, and have received important attention in current years. There is some security concern that increases fear of attacks on the mobile ad-hoc network. The mobility of the NODE in a MANET poses many security problems and vulnerable to different types of security attacks than conventional wired and wireless networks. The causes of these issues are due to their open medium, dynamic network topology, absence of central administration, distributed cooperation, constrained capability, and lack of clear line of defense. Without proper security, mobile hosts are easily captured, compromised, and attacked by malicious nodes. Malicious nodes behavior may deliberately disrupt the network so that the whole network will be suffering from packet losses. One of the major concerns in mobile ad-hoc networks is a traffic DoS attack in which the traffic is choked by the malicious node which denied network services for the user. Mobile ad-hoc networks must have a safe path for transmission and correspondence which is a serious testing and indispensable issue. So as to provide secure communication and transmission, the scientist worked explicitly on the security issues in versatile impromptu organizations and many secure directing conventions and security measures within the networks were proposed. The goal of the work is to study DoS attacks and how it can be detected in the network. Existing methodologies for finding a malicious node that causes traffic jamming is based on node’s retains value. The proposed approach finds a malicious node using reliability value determined by the broadcast reliability packet (RL Packet). In this approach at the initial level, every node has zero reliability value, specific time slice, and transmission starts with a packet termed as reliability packet, node who responded properly in specific time, increases its reliability value and those nodes who do not respond in a specific time decreases their reliability value and if it goes to less than zero then announced that it’s a malicious node. Reliability approach makes service availability and retransmission time.

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Section: Black Hole Attackmentioning

confidence: 99%

Smart detection and prevention procedure for DoS attack in MANET

Muntaheen¹,

Bhowmick²,

Rumman³

et al. 2021

RRST

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“…Also, it has been observed that the detection rate has not been affected by the node movement and network's size. Alsumayt et al [9] presented a new technique named "monitoring detection and rehabilitation" to prevent the network from DoS attack. This technique is based on measuring the number of actual values that the sensor node can be trustworthy or not.…”

Section: Related Workmentioning

confidence: 99%

Ddos Attack Detection and Prevention using Aodv Routing Mechanism and FFBP Neural Network in a Manet

Batra¹,

Krishna²

2019

IJRTE

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Security is considered as the most important feature in a Mobile Ad-hoc Network (MANET). There are different types of attacks which may affect the data transmission in MANET but Distributed Denial of Service (DDoS) attack is one of the complex and harsh worthy in a MANET. In the existing work, it has been found that the researchers have utilized Support Vector Machine (SVM) and fuzzy logic as a classification algorithm to identify the DoS attack in MANET. The problem with SVM and Fuzzy logic is that they are more complex and more time consuming mechanism to detect attackers. Also, in the existing work, Optimized Link State Routing (OLSR) routing protocol is used to find route and it is a searching mechanism which does not include the concept of trust routing table and hence the searching mechanism consumes more energy. To solve the mentioned problems, we are presenting a machine learning approach that is Feed Forward Back Propagation Neural Network (FFBPNN) as a classifier and Ad hoc On-Demand Distance Vector(AODV) routing protocol for route discovery to shield the network from Distributed Denial of Service (DDoS) attack. The MANET is trained using FFBPNN. Therefore, when malicious node appears in the network, the node is identified on the basis of the node properties like energy consumption and delay. The route is changed by discarding the malicious nodes from the route and hence the network is protected. The throughputs, PDR have been increased by 60.71%, 53.57% and delay has been reduced by 42.21%.

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“…Other methods use a trust concept between nodes in MANETs that need IP address configuration in the event of MANET merging. Both traditional and other methods used to detect DoS attacks based on trust concepts are discussed in detail in (Alsumayt and Haggerty, 2014a); (Alsumayt and Haggerty, 2014b). The current section briefly presents a summary of studies that aim to configure IP addresses in the merging scenario and the methods used for misbehaviour detection.…”

Section: Related Workmentioning

confidence: 99%

Using trust to detect denial of service attacks in the internet of things over MANETs

Alsumayt¹,

Haggerty²,

Lotfi³

2017

IJSSC

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The rapid growth of employing devices as tools in daily life and the technological revolution have led to the invention of a novel paradigm; the Internet of Things (IoT). It includes a group of ubiquitous devices that communicate and share data with each other. In order to communicate, these devices use protocols, and in particular, theInternet Protocol (IP) is used to manage network nodes through mobile ad hoc networks (MANET), which provide self-configuring and transient infrastructures. MANET enables each node to freely join or leave the network frequently. IoT is beneficial to MANET as the nodes are self-organising and the information reach can be expanded according to the network range. Due to the nature of MANET, such as dynamic topologies and no administrative point, a number of challenges are inherent, such as message fabrication, confidentiality violations, and Denial of Service (DoS) attacks. DoS attacks prohibit legitimate users from using or accessing their authorised services and is particularly pertinent in IoT systems which rely on persistent connections for the transfer, collection, and processing of data. In addition, because of the high mobility of MANET, the network is likely to merge with other networks. In this situation, two or more networks of untrusted nodes may join one another leaving each of the networks open to attack. This paper proposes a novel method to detect DoS attacks immediately prior to the merger of two MANETs. To demonstrate the applicability of the proposed approach, a Grayhole attack, a type of DoS attack, is used in this study to evaluate the performance of the proposed method in detecting attacks.

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Evaluation of Detection Method to Mitigate DoS Attacks in MANETs

Cited by 6 publications

References 9 publications

Smart detection and prevention procedure for DoS attack in MANET

Smart detection and prevention procedure for DoS attack in MANET

Ddos Attack Detection and Prevention using Aodv Routing Mechanism and FFBP Neural Network in a Manet

Using trust to detect denial of service attacks in the internet of things over MANETs

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