Implementation of Rank Attack and Its Mitigation in RPL-Based IoT

Networks

Madhu Yadav and Rajbir Kaur

Department of CSE, The LNMIIT, Jaipur, 302031, Rajasthan, India

Keywords:

IoT, LLN, RPL, Rank Attacks.

Abstract:

The burgeoning interest in the Internet of Things (IoT) has led to the widespread deployment of Low-power

and Lossy Networks (LLNs). The Routing Protocol for Low-Power and Lossy Networks (RPL) is a standard

protocol designed for networks with resource-constrained devices and high packet loss rates. However, RPL is

vulnerable to various attacks, particularly rank attacks, which can disrupt network performance and compro-

mise security. This paper addresses this gap by implementing rank attacks in RPL using the Cooja Simulator

in Contiki OS and analyzing their impact on network performance. While rank attacks are extensively dis-

cussed in the literature, practical implementations remain limited. To mitigate these attacks, we propose a

novel trust-based mitigation strategy that integrates seamlessly with resource-constrained IoT devices. Our

approach dynamically computes trust metrics to detect and isolate malicious nodes, thereby improving net-

work security, reducing power consumption, and ensuring reliable packet transmission. Comparative analysis

demonstrates the superiority of our approach over existing techniques, offering enhanced scalability and adapt-

ability for secure IoT deployments.

1 INTRODUCTION

IoT devices operate under signiﬁcant resource con-

straints, such as limited memory, power, and pro-

cessing capabilities. Routing protocols are essen-

tial for efﬁcient data transmission in IoT, with the

Routing Protocol for Low-Power and Lossy Networks

(RPL)—developed by the IETF RoLL working group

(Baccelli and Philipp, 2013)—serving as a fundamen-

tal solution. RPL, integrated into the IoT protocol

stack (Figure 1), works alongside IEEE 802.15.4 to

enable interoperability and efﬁcient communication

in low-power wireless networks.

However, IoT faces signiﬁcant security challenges

due to its constrained nature, exposing devices and

networks to various attacks that threaten data integrity

and system functionality. Addressing these chal-

lenges is crucial for the secure and widespread de-

ployment of IoT systems.

Figure 1: Overview of the IoT protocol stack.

The remainder of this paper is organized as fol-

lows: - Section 2 reviews related work on rank at-

tacks and mitigation. - Section 3 introduces RPL and

its role in IoT security. - Section 4 presents a taxon-

omy of RPL attacks with a focus on rank attacks. -

Section 5 analyzes the simulation and impact of rank

attacks. - Section 6 describes rank attack detection

using a trust-based energy metric.

- Finally, Section 7 concludes with key ﬁndings

and contributions.

2 RELATED WORK

RPL (Baccelli and Philipp, 2013) is vulnerable to rank

attacks such as increase, decrease, and worst parent

attacks, which degrade network performance by in-

creasing energy consumption and reducing reliability

(Mayzaud et al., 2016) (Raoof et al., 2018). Mitiga-

tion strategies include IDS (Simoglou et al., 2021),

machine learning (Said et al., 2020), and trust-based

methods (Liu, 2021). Approaches like SecTrust-

RPL, DCTM (Hashemi and Aliee, 2019), and MRTS

(Djedjig et al., 2020) improve energy efﬁciency and

throughput. Statistical methods (Iuchi et al., 2015)

and deep learning (Choukri et al., 2020) show promise

Yadav, M. and Kaur, R.

Implementation of Rank Attack and Its Mitigation in RPL-Based IoT Networks.

DOI: 10.5220/0013205100003944

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), pages 215-222

ISBN: 978-989-758-750-4; ISSN: 2184-4976

215

Figure 2: A network conﬁguration with two RPL instances,

and 3 DODAGs.

but face challenges. Our model focuses on energy ef-

ﬁciency and trust evaluation without relying on hard-

ware or historical data, offering a lightweight solution

for rank attack mitigation.

3 THE RPL PROTOCOL

The Routing Protocol for Low-Power and Lossy

Networks (RPL) (Baccelli and Philipp, 2013) is

a distance-vector protocol designed for resource-

constrained devices in environments with high packet

loss. It constructs Destination Oriented Directed

Acyclic Graphs (DODAGs) combining tree and mesh

structures, with multiple instances optimized for met-

rics like energy consumption. RPL uses four ICMPv6

control messages (DIS, DIO, DAO, DAO-ACK), and

the trickle algorithm updates DIOs. Security is pro-

vided in three modes: Unsecured, Pre-Installed, and

Authenticated. Despite these measures, security chal-

lenges persist, emphasizing the need for lightweight

monitoring solutions. Figure 2 illustrates a net-

work conﬁguration with two RPL instances and three

DODAGs.

4 RPL ATTACKS

The attacks are categorized into resource depletion,

topology disruption, and trafﬁc manipulation de-

pending on the attacks’ effect on the IoT network

(Mayzaud et al., 2016). This taxonomy provides a

concise overview of RPL attacks, aiding in under-

standing and mitigating potential threats to network

security.

4.1 Resource Depletion Attacks

Resource depletion attacks aim to exhaust network

resources such as bandwidth, memory, and energy.

These attacks can disrupt normal network operations.

Rank Increase Attack increases energy consumption

by inﬂuencing nodes to choose more distant nodes as

parents. This is shown in (Figure 3(I)). Here, a mali-

Figure 3: RPL attacks (I).Rank Increase Attack (II).Rank

Decrease Attack (III).Worst Parent Attack

cious node 4 advertises a rank higher than node 2 and

node 3, causing nodes 6, 7 and 8 to select a distant

node 4 as their parent (Raoof et al., 2018).

4.2 Trafﬁc Manipulation Attacks

Trafﬁc manipulation attacks in Low-Power and Lossy

Networks (LLNs) occur when malicious entities dis-

rupt data ﬂow by creating fake identities or injecting

malicious trafﬁc.

Decrease Rank Attack involves advertising lower

ranks to disrupt the normal trafﬁc ﬂow, causing sub-

optimal or incorrect routes. This manipulation can

isolate nodes, impair communication, and potentially

lead to network partitioning, resulting in trafﬁc dis-

ruptions and degraded overall network performance.

In (Figure 3(II)), a malicious node 3 advertises a

lower rank than node 2 and node 4, causing one-hop

neighbors of nodes 2 and 4 to choose the adversary as

their preferred parent (Raoof et al., 2018).

4.3 Topology Disruption Attacks

Topology disruption attacks target the RPL net-

work’s structure, affecting the routing paths and over-

all communication within the network. Malicious

nodes hinder optimal convergence and render nodes

communication-incapable.

In Worst Parent Attack, malicious nodes manipulate

parent selection, leading to suboptimal routing and

disrupting the network’s topology. In (Figure 3(III)),

a malicious node 3 advertises its unmodiﬁed rank or

a lower rank than its neighbors. The child nodes for-

ward the data trafﬁc to 3. Instead of forwarding the

trafﬁc to its legitimate parent (node 1), the malicious

node 3 forwards the trafﬁc to the adversary’s worst

parent 6 (assuming node 6 has the highest rank among

node 3’s neighbors) (Raoof et al., 2018).

5 RANK ATTACKS: SIMULATION

AND ANALYSIS

We implement rank attacks using the Cooja simulator

on Contiki OS, which supports TCP/IP, multithread-

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216

ing, and hardware abstraction. C programming and

protothreads are used, with Cooja enabling code

development. Java handles mote conﬁguration, log

analysis, and visualization, while XML conﬁgures

simulations.

To evaluate the impact of rank attacks on network

performance, we use several performance metrics that

provide a comprehensive assessment of resource con-

sumption within the Contiki OS and Cooja simulation

environment.

Packet Delivery Ratio (PDR): Measures net-

work reliability by calculating the ratio of received

data packets to the total generated packets.

PDR =



∑

packet received

∑

packet sent



× 100%

Power Consumption: Quantiﬁes the amount of

energy consumed by a device in performing various

operations in the network. We calculated power

consumption using the Powertrace tool in Contiki

OS, considering transmission, listening, CPU usage,

and low-power mode.

Energy(mJ) = (Transmit × 19.5mA + Listen ×

21.5mA + CPU × 1.8mA + LPM × 0.0545mA) × 3V

/ 32768

PowerConsumption(mW ) = Energy(mJ)/Time(sec)

Our analysis includes the implementation of rank

increase, rank decrease, and worst parent attacks.

The simulation parameters conﬁgured in InstantCon-

tiki3.0/Cooja are detailed in Table 1.

5.1 Rank Increase Attack

Speciﬁc modiﬁcations are needed in the source

code to execute a rank increase attack in Contiki’s

RPL implementation, focusing on ﬁles in the ”con-

tiki/core/net/rpl/” directory. In the ”rpl-private.h”

ﬁle (Figure 4), which contains declarations and con-

stants pertinent to the calculation of Directed Acyclic

Graph (DAG) ranks, modiﬁcations need to be made to

compromise the protective measures within the rank

computation algorithm.

Moreover, within ”rpl-timers.c” (Figure 5), which

oversees RPL timer functions, the segment of code re-

sponsible for managing node rank recalculations re-

quires modiﬁcations to deactivate this process, thus

maintaining the impact of the rank increase attack.

Figure 6a depicts the simulated network network

for a rank increase attack in the Contiki/COOJA sim-

ulator. A green circle represents the transmission

range of a node. Node 1 is designated as the sink

Table 1: Simulation Parameters.

Parameter Value

Coverage Area 110m x 110m

Number of Nodes 8 normal and 1 Malicious in

rank decrease attack, 12 nor-

mal and 1 malicious in rank

increase and worst parent at-

tack

Number of Nodes

mitigation simu-

lation

7 normal and 3 attacker,

3 malicious, 3 mitigation

nodes

Node Placement Random

Transmission

Range

50m

Interference

Range

60m

Transmission Ra-

tio

100

Reception Ratio 30-100 (variable)

Routing Protocol RPL

Network Protocol IPv6

Simulation Start

Delay

500 milliseconds

Radio Medium UGDM Distance Loss

Simulation Dura-

tion

60 minutes for rank increase

and rank decrease attack, 20

minutes for worst parent at-

tack, 1 hour 12 minutes for

rank attack mitigation

Figure 4: rpl private.h code modiﬁcation.

Figure 5: rpl timers.c code modiﬁcation.

node, while the remaining nodes act as normal sender

nodes. Node 3 is identiﬁed as a malicious node. The

blue lines connecting the nodes indicate potential ra-

dio communication. Double-outlined pink circles il-

lustrate nodes sensing their neighboring nodes within

the transmission range.

Implementation of Rank Attack and Its Mitigation in RPL-Based IoT Networks

217

(a) Rank Increase Attack Simulation.

(b) Rank Decrease Attack Simulation.

Figure 6: Simulation Network.

5.2 Decrease Rank Attack

Similar changes are required in the ﬁles as mentioned

above 5.1, to execute the rank decrease attack. The

process is identical, involving modiﬁcations to con-

stants declared in ”rpl-private.h” and disabling rank

recalculation in ”rpl-timers.c”.

Figure 7: Modiﬁcations in rpl-mrhof.c.

Figure 6b illustrates the simulated network for a

rank decrease attack in the Contiki/COOJA simulator.

The left side of the ﬁgure represents the normal sce-

nario with no malicious node. Node 1 is assigned as

the sink node, and the remaining nodes function as

normal sender nodes. Node 9 is identiﬁed as a mali-

cious node on the right side of the ﬁgure. It intention-

ally discards packets originating from nodes 4, 6, and

8, leading to the isolation of these nodes and disrup-

tion of the network topology.

5.3 Worst Parent Attack

To simulate rank attacks in Contiki OS, we mod-

iﬁed the metric evaluation function in the ”rpl-

mrhof.c” (Figure 7) ﬁle. Speciﬁcally, we modiﬁed

the ’best-parent’ function in the ﬁle. The parameters

of the function are two nodes that are candidates for

becoming the parent of a node. We modiﬁed the code

to select the weakest candidate as the preferred par-

ent. The ’rpl-select-parent()’ in ”rpl-dag.c” uses the

outcome of the ’best-parent()’ function to determine

the preferred parent in the DODAG structure.

Figure 6c presents the simulation network depict-

ing a worst-parent attack in the Contiki/COOJA sim-

ulator. Node 1 is designated as the sink node, and

the remaining nodes operate as normal sender nodes.

Node 3 is marked as a malicious node. Instead of

directing the packets toward the sink node, it inten-

tionally routes them through the worst path, leading

to delays and potential routing loops.

5.4 Analysis of the Effects of Rank

Attacks

In this section, we analyze the effects of rank at-

tacks on network performance parameters, such as the

packet delivered at the sink and power consumption.

1. In a rank increase attack, malicious nodes inten-

tionally manipulate the RPL (Routing Protocol

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218

(I) Rank Increase

(II) Rank Decrease

(III) Worst Parent

Figure 8: Power Consumption.

for Low-Power and Lossy Networks) by choosing

suboptimal parents based on the objective func-

tion. This strategy increases energy consump-

tion (Figure 8(I)) and overhead trafﬁc in the net-

work.(Figure 9 (I)) represents the packets deliv-

ered at the sink during the 60-minute simulation

period in both normal and attack scenarios.

2. The rank decrease attack overloads the network,

causing resource consumption attacks that drain

node batteries(Figure 8 (II)) and congest the RPL

network. The consequences include signiﬁcant

disruption in trafﬁc and the RPL’s DODAG (Des-

tination Oriented Directed Acyclic Graph), with

legitimate nodes connecting to the DODAG via

the attacker. This diverts much trafﬁc, disrupting

the network’s topology and reducing the packet

delivery ratio(Figure 9 (II)).

(I) Rank Increase

(II) Rank Decrease

(III) Worst Parent

Figure 9: Packet Delivered at Sink.

3. The attacker’s systematic selection of the least-

preferred parent results in suboptimal paths and

poor overall network performance, including high

power consumption(Figure 8 (III)) and a low

number of packets delivered at the sink(Figure 9

(III)). Furthermore, the attacker may use the de-

creased rank method to attract more nodes as

parents, resulting in increased delays or routing

loops. To summarize, the worst parent attack

leads to network sub-optimization and heightened

end-to-end delays.

Implementation of Rank Attack and Its Mitigation in RPL-Based IoT Networks

219

Table 2: Comparison of Rank Attack Analysis and Mitigation Approaches in RPL-Based IoT Networks.

Criteria Our Approach (Rouissat et al.,

2023)

(Algahtani et al.,

2021)

(A. Hkiri and

Machhout, 2024)

Attack Types

Covered

Three rank-based

attacks: rank in-

crease, decrease,

worst-parent.

Silent decreased rank

attack only.

Covers multiple RPL

attacks, no focus on

rank-based attacks.

Decreased rank at-

tack only.

Mitigation

Strategy

Trust-based real-time

mitigation to detect

and isolate malicious

nodes.

Lightweight counter-

measure for silent de-

creased rank attack.

Simulation frame-

work, no speciﬁc

mitigation strategies.

No mitigation or

real-time defense

proposed.

Performance

Metrics

Evaluated

Energy, PDR, la-

tency, overhead, and

other metrics.

Energy, latency, and

overhead only.

Focuses on generat-

ing data, not defense

analysis.

PDR, throughput,

and power consump-

tion.

Attack Anal-

ysis and Mit-

igation Inte-

gration

Comprehensive anal-

ysis and integrated

mitigation in real-

world IoT scenarios.

Focus on single at-

tack and countermea-

sure.

Attack simulation

tools only, no inte-

gration of mitigation.

Focus on attack im-

pact without practi-

cal defenses.

6 PROPOSED APPROACH: RANK

ATTACKS MITIGATION

To enhance the security of the Routing Protocol for

Low-Power and Lossy Networks (RPL) against rank

attacks, we propose a Trust Energy-based counter-

measure. We introduce a novel metric called ”Trust

Energy” to assess the trustworthiness of nodes within

the RPL network. The Trust Energy metric is cal-

culated based on behavioral, communication, and

security-related parameters. The block diagram in

Figure 10 shows the steps of our proposed approach.

The following description outlines the steps:

1. Trust Energy Estimation:

The Trust Energy (Trust

) of a node is calculated

as follows (Zhou and Gong, 2014):

Trust

= (1 − α) · Energy

+ β · PDR

+ (1)

α · Behavior

+ (1 − β) · Trustneighbor

avg

Here, Energy

represents the energy level associ-

ated with node ’i’.

Trustneighbor

avg

is average trust value recom-

mended by neighboring nodes. This introduces

a collaborative aspect where a node’s trust is in-

ﬂuenced by the collective trustworthiness of its

neighbors.

The behavior of a node (Behavior

) is calculated

using the following parameters.

• Successful Packet Delivery (PDRi): Reﬂects a

node’s reliability in delivering packets. Higher

success rates contribute positively to trust en-

ergy.

• Adherence to Protocols (Behaviori): Nodes

conforming to communication protocols and

standards are deemed more trustworthy, em-

phasizing expected protocol behavior.

• Reliability in Task Execution: Nodes consis-

tently fulﬁlling responsibilities enhance trust

energy.

• Communication Analysis:

• Communication Responsiveness: Swift re-

sponses to communication requests positively

impact trust energy (Dahal and Shrestha, 2020).

• Consistency in Communication Patterns: Mon-

itoring and analyzing consistent communica-

tion patterns contribute to trust energy.

• Communication Security Measures: Robust se-

curity measures, such as encryption and au-

thentication, positively inﬂuence trust energy.

• Security Credential Assessment:

• Authentication and Authorization: Nodes with

valid credentials and a clean security history

boost trust energy.

• History of Security Incidents: A node’s secu-

rity incident history affects trust energy. The

parameters α and β serve as weighting factors,

allowing the adjustment of each factor’s inﬂu-

ence on the overall trust computation. This

comprehensive approach aims to create a nu-

anced evaluation of node trustworthiness, con-

sidering both individual and collective perfor-

mance metrics.

2. Trust-based Route Selection: Nodes prioritize

routes with higher trust energy to avoid compro-

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220

Figure 10: Steps of Proposed Rank Attack Mitigation.

mised nodes, integrating trust metrics with stan-

dard Objective Function metrics.

3. Trust Energy Propagation: Nodes exchange trust

energy values with neighbors (one-hop or two-

hop), using secure protocols to prevent malicious

activity.

4. Rank Calculation with Trust Energy: The Trust

Energy Factor (TEF) is integrated into the RPL

rank calculation (Santos-Boada and Hesselbach,

2019):

R = (DODAGRank × mHRI) + minRankIncrease + α · T EF

5. Monitor Trust Energy Changes: Rapid drops

in trust energy indicate attacks, and nodes

with suspicious changes are isolated or rerouted

(Airehrour et al., 2019).

6. Trust Energy Exchange: Nodes exchange trust

energy values securely with neighbors (Gelogo

et al., 2011).

7. Threshold Based Response: Nodes below a trust

energy threshold trigger isolation and rerouting

actions.

8. Collaborative Detection: Nodes detect and share

rapid trust energy drops to improve attack detec-

tion (Airehrour et al., 2019).

9. Secure Communication: Secure protocols, in-

cluding AES encryption and authentication, pro-

tect trust energy data (Ioulianou et al., 2022)

(Raghavendra et al., 2022).

While Trust Energy-based countermeasures enhance

security in RPL networks, a multi-layered strategy

combining cryptographic methods, intrusion detec-

tion, and anomaly detection is vital for comprehensive

protection (Zhang and Chen, 2018).

6.1 Simulation of Rank Attack

Mitigation

• Figure 11 (I) depicts the simulated node network

designed for detecting rank attacks. The network

comprises four types of nodes: green nodes num-

bered from 1 to 7 serve as result nodes, acting as

UDP sender nodes during bootstrapping. Follow-

ing them are the rank attacker nodes (numbered 7

to 10) and malicious nodes (numbered 11 to 13),

Figure 11: I.Simulation Network II.PDR III.Power Con-

sumption.

which attempt to manipulate rank values. How-

ever, mitigation nodes (numbered 14 to 16) isolate

these attackers and redirect trafﬁc through normal

nodes. .

• The proposed work aims to address attacker ac-

cess and isolate them, leading to improved net-

work packet transmissions (Figure 11 II). A col-

lect view is initiated for node ID 1, and the snap-

shot (Figure 11 III) illustrates the power consump-

tion of nodes, including CPU power, LPM power,

Listen power, and Transmit power. This mitiga-

tion method boasts low power consumption and

minimal computation time, thereby enhancing the

security of the RPL network and ensuring proper

packet transmission.

6.2 Comparative Analysis of Rank

Attacks and Mitigation

This section compares various approaches to address-

ing rank-based attacks in RPL-based IoT networks,

including Our Approach, (Rouissat et al., 2023),

(Algahtani et al., 2021), and (A. Hkiri and Machhout,

2024).Our Approach provides a comprehensive so-

lution for securing IoT networks by analyzing three

rank-based attacks (increase, decrease, and worst-

parent). It features a real-time dynamic trust-based

mitigation strategy that ensures network security dur-

ing attacks. Additionally, it evaluates multiple per-

Implementation of Rank Attack and Its Mitigation in RPL-Based IoT Networks

221

formance metrics, such as energy, PDR, and latency,

to demonstrate how mitigation improves network per-

formance. Furthermore, our Approach integrates both

attack analysis and mitigation into a cohesive frame-

work, offering a holistic solution for securing IoT net-

works. Table 2 provides a comparison of the dis-

cussed approaches.

7 CONCLUSION

This work analyzes the vulnerabilities of the Routing

Protocol for Low-Power and Lossy Networks (RPL),

focusing on rank-based attacks like rank increase,

rank decrease, and worst-parent attacks. We ﬁnd that

rank-increase attacks raise energy consumption and

overhead, leading to network overload, while rank-

decrease attacks disrupt trafﬁc and the RPL’s Directed

Acyclic Graph (DODAG), affecting packet delivery.

To improve security in low-power networks like

IoT, we propose a trust energy-based mitigation for

RPL, using trust energy for routing, anomaly detec-

tion, and defense. This approach enhances network

resilience against rank-based attacks, providing dy-

namic security that adapts to evolving threats. Fu-

ture work is necessary to reﬁne and address emerging

challenges.

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