Dpraodv: A Dyanamic Learning System Against Blackhole Attack in Aodv Based Manet

Abstract: Security is an essential requirement in mobile ad hoc networks to provide protected communication between mobile nodes. Due to unique characteristics of MANETS, it creates a number of consequential challenges to its security design. To overcome the challenges, there is a need to build a multifence security solution that achieves both broad protection and desirable network performance. MANETs are vulnerable to various attacks, blackhole, is one of the possible attacks. Black hole is a type of routing attack where a malicious node advertise itself as having the shortest path to all nodes in the environment by sending fake route reply. By doing this, the malicious node can deprive the traffic from the source node. It can be used as a denial-of-service attack where it can drop the packets later. In this paper, we proposed a DPRAODV (Detection, Prevention and Reactive AODV) to prevent security threats of blackhole by notifying other nodes in the network of the incident. The simulation results in ns2 (ver- 2.33) demonstrate that our protocol not only prevents blackhole attack but consequently improves the overall performance of (normal) AODV in presence of black hole attack.

• The paper introduces DPRAODV as a dynamic defense mechanism that detects and isolates nodes involved in blackhole attacks in AODV-based MANETs.
• It employs dynamic threshold calculation and the dissemination of ALARM packets to proactively identify and isolate malicious nodes.
• Simulation results in ns2 show an 80-85% improvement in packet delivery ratio with minimal increases in routing overhead and delay.

An Examination of DPRAODV: Enhancing Security Against Blackhole Attacks in AODV-Based MANETs

The paper "DPRAODV: A Dynamic Learning System Against Blackhole Attack in AODV Based MANET," authored by Payal N. Raj and Prashant B. Swadas, addresses a core security issue in Mobile Ad Hoc Networks (MANETs) by introducing a strategic protocol, DPRAODV, aimed at mitigating threats posed by blackhole attacks within the Ad hoc On-Demand Distance Vector (AODV) routing protocol.

Security Concerns in MANETs

MANETs are distinct from traditional wired networks due to their dynamic topology, resource constraints, and shared wireless medium, factors that contribute to heightened security challenges. The openness and decentralized nature of MANETs render them susceptible to various security threats, notably the blackhole attack—a type of routing attack where a malicious node deceitfully advertises itself as the route with the shortest path to the destination node in order to intercept or discard packets.

DPRAODV Protocol: Design and Mechanism

DPRAODV stands for Detection, Prevention, and Reactive AODV, a newly proposed mechanism that introduces a robust approach to counter blackhole attacks in AODV-based MANETs. The protocol's core strategy involves:

1. Dynamic Threshold Calculation: The protocol dynamically updates the threshold used to detect anomalies in node behavior, allowing it to adapt to changes in the network environment. This is achieved by analyzing sequence number trends and their variances over specified intervals.
2. Isolation of Malicious Nodes: Upon detection of anomalous sequence numbers indicating a potential blackhole attack, DPRAODV marks the suspect node, disseminates ALARM packets to its neighbors, and isolates the malicious node from further data forwarding activities.
3. Reactive Measures: In addition to prevention, DPRAODV implements reactive strategies that reinforce network integrity by updating nodes about threats, ensuring nodes are precluded from processing data from identified malicious nodes.

Performance Evaluation

Through simulation experiments conducted in ns2 (ver-2.33), DPRAODV's performance in terms of packet delivery ratio (PDR), average end-to-end delay, and normalized routing overhead was assessed under varying network conditions. The findings suggest remarkable improvements in PDR while maintaining moderate increases in routing overhead and delay, signifying enhanced network reliability even amidst blackhole attacks.

• Packet Delivery Ratio: DPRAODV exhibits an enhancement of 80-85% in PDR compared to the AODV protocol under attack, ensuring higher data delivery success rates.
• Average End-to-End Delay: The protocol maintains, on average, a similar delay to that experienced in standard AODV, signifying no substantial latency introduced by the security measures.
• Routing Overhead: Despite the introduction of ALARM packets, the increase in routing overhead is marginal, demonstrating the protocol's efficiency in maintaining protocol operations without significant resource consumption.

Implications and Future Directions

The introduction of DPRAODV illustrates a meaningful advancement in securing MANETs against specific routing attacks, addressing both immediate threat detection and long-term network integrity. Its dynamic learning approach, which adjusts according to network topology changes, offers a promising direction for adapting to diverse network conditions.

The paper posits that future developments in node authentication and further integration of machine learning techniques could enhance detection accuracy and response time. Moreover, expanding the protocol's functionality to encompass broader attack vectors could potentially enrich its applicability across varied wireless network environments.

In conclusion, the proposed DPRAODV protocol offers a pragmatic solution for improving MANET security by effectively detecting and mitigating blackhole attacks with a combination of dynamic learning and proactive network management techniques. The proposed methodology signifies a valuable contribution to the ongoing development of robust MANET protocols.