A Review: Black Hole and Gray Hole Attack in MANET

Dixit, Sweta; Joshi, Krishna Kumar; Joshi, Neelam

doi:10.14257/ijfgcn.2015.8.4.28

Cited by 6 publications

(3 citation statements)

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“…The malicious nodes can perform packet modification attacks [26], black hole and grey hole attacks [27] with all or some packet ejection, and energy depletion [28,29]. When simulation starts, all nodes in the WSN consider each other a legitimate one.…”

Section: Resultsmentioning

confidence: 99%

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

2022

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This work addresses the functional approach to ensuring cyber resiliency as a kind of adaptive security management. For this purpose, we propose a learning automaton model capable of self-learning and adapting to changes while interacting with the external environment. Each node in the under-controlled system has a set of probable actions with respect to neighboring nodes. The same actions are represented in the graph of the learning automaton, but the probabilities of actions in the graph model are permanently updated based on the received reinforcement signals. Due to the adaptive reconfiguration of the nodes, the system is able to counteract the cyberattacks, preserving resiliency. The experimental study results for the emulated wireless sensor network (WSN) are presented and discussed. The packets loss rate stays below 20% when the number of malicious nodes is 20% of the total number of nodes, while the common system loses more than 70% of packets. The network uptime with the proposed solution is 30% longer; the legitimate nodes detect malicious nodes and rebuild their interaction with them, thereby saving their energy. The proposed mechanism allows ensuring the security and functional sustainability of the protected system regardless of its complexity and mission.

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Section: Resultsmentioning

confidence: 99%

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

2022

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“…Di dalam serangan black hole pada jaringan MANET, protokol routing digunakan menyatakan dirinya ke node perantara lainnya sebagai node yang memiliki rute terpendek ke node tujuan [9]. Setelah menerima pesan RREQ dari node, node black hole dapat memalsukan pesan RREP seolah-olah memiliki rute yang cukup fresh ke node tujuan.…”

Section: Serangan Black Holeunclassified

Implementasi Metode Idsaodv Untuk Mendeteksi Serangan Black Hole Pada Jaringan Manet

Pakpahan

2021

Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informat

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MANET adalah sekelompok node yang membentuk kesatuan dan mengirim paket kepada node yang lain. Namun dari segi keamanan, MANET sangat rentan terhadap berbagai macam serangan. Serangan black hole merupakan jenis serangan pada jaringan yang dapat menyerap network traffic dan menjatuhkannya. Cara kerja dari serangan black hole adalah dengan berpura-pura memiliki rute yang cukup fresh ke semua tujuan yang diminta oleh semua node dan menyerap semua network traffic. Saat node sumber melakukan broadcast pesan RREQ untuk node tujuan, node black hole akan segera merespon dengan pesan RREP yang berisi informasi sequence number tertinggi dan pesan ini dianggap seolah-olah datang dari node tujuan atau dari node yang memiliki rute yang cukup fresh ke node tujuan. IDSAODV merupakan suatu routing protokol yang digunakan untuk meminimalisir efek dari black hole. Protokol IDSAODV akan memeriksa paket RREP dari node black hole untuk jalur minimum ke tujuan dan maksimum sequence number tujuan. Protokol IDSAODV akan membuang paket RREP pertama yang datang dari node black hole dan memilih paket RREP kedua yang datang dari node tujuan. Pada penelitian ini akan dilakukan uji coba serangan black hole pada lingkungan MANET dengan jumlah node sebanyak 100, 110, 120, 130, 140, dan 150 dengan menggunakan routing protokol AODV dan IDSAODV. Dari percobaan tersebut didapatkan hasil peningkatan Packet Delivery Ratio (PDR) paling tinggi sebesar 47,36%, peningkatan Throughput sebesar 156,78 Kbps dan penurunan End-to-End Delay sebesar 27,62 ms.

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“…The black-and gray-hole attacks are also known as sequence number attacks. Many approaches have been designed and suggested to identify and mitigate these kinds of attacks in MANETs [11]. Though these approaches mitigate malicious activities, still the routing security has less efficiency.…”

Section: Introductionmentioning

confidence: 99%

Protecting MANETs from Black and Gray Hole Attacks Through a Detailed Detection System

2022

IJIES

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In mobile ad-hoc network (MANET), identification and mitigation of black and gray-hole attacks is a challenging task compared to the detection of other attacks. To solve this issue, a secure route discovery ad-hoc ondemand distance vector (SRD-AODV) protocol has been suggested, which verifies the nodes only during the path discovery. But, it is necessary to authenticate the nodes during data transmission since the gray-hole nodes broadcast an accurate target sequence number (TSN) during the route discovery, whereas it becomes malicious and drops the packets during the data forwarding. Hence in this article, a secure route maintenance and attack detection AODV (SRMAD-AODV) protocol is proposed for identifying and defending the black and gray-hole attacks in the data transfer stage. Initially, an attack discovery system (ADS) node is decided from the connected dominating set (CDS) method based on energy and confidence score. The CDS is a robust, distinct and localized method to identify nearby linked dominating sets of nodes in a limited range in MANETs. The selected ADS nodes forward a status packet within the size of the dominating set to retrieve the entire behavioral data. ADS nodes examine gathered behavioral data and create a blacklist in which the suspected black and gray-hole nodes are added. Then, the blacklist is forwarded to the origin node to confirm the susceptibility of nodes present in the blacklist. Once the origin node authenticates the blacklist, it broadcasts a block message to all other nodes in a path for discarding blacklist nodes from the routing path. Further, this SRMAD-AODV protocol is simulated and the findings exhibit that it realizes 5.2sec of end-to-end delay (EED) and 86 % of packet delivery ratio (PDR) in contrast to the SRD-AODV protocol.

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A Review: Black Hole and Gray Hole Attack in MANET

Cited by 6 publications

References 0 publications

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

Implementasi Metode Idsaodv Untuk Mendeteksi Serangan Black Hole Pada Jaringan Manet

Protecting MANETs from Black and Gray Hole Attacks Through a Detailed Detection System

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