Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels.

Routing protocol for low power and lossy networks (RPL) has been proposed for power, memory, and processing constrained devices. Owing to their constrained, RPL-based networks are exposed to a wide range of security attacks that mainly include control message tampering. In this paper we propose and study a modified version number attack, based on flooding the network by falsified incremented version numbers. The obtained results show that the modified attack led to an immense increase in the overhead, 1426%, compared with the attack-free case, and an increase of 182 % in the total energy consumption. When it comes to PDR a degradation to 4.7% has been recorded, affecting the reliability of the network. On the other hand, the latency also showed an increase from 0.24 s in the attack-free case to 0.89 s, which is mainly due to the high congestion created by the attack.

The growing deployment of Internet of Things (IoT) devices in diverse daily-life smart applications makes the security of IoT networks a critical requirement. However, efficient support of network security remains challenging even for a standardized IoT network protocol such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). It incorporates limited protection from external security attacks but stays considerably vulnerable to internal routing attacks. The inherent design of RPL, particularly its topology establishment and maintenance mechanism, makes it easy to initiate more damaging attacks such as flooding attacks. Given the constrained characteristics of IoT devices, flooding IoT networks can easily lead to resource exhaustion and network performance degradation. This paper introduces a new variant of the flooding attack namely the Destination Advertisement Object Flooding (DAOF) attack. It is based on using routing communications, particularly the DAO messages, to disseminate unnecessary routing traffic which instigate excessive transmissions of control messages across the network. As demonstrated by the experimental results of this study, the DAOF attack can increase network overhead by more than 65% even in a relatively small-scale setup. Additionally, it can notably lead to high energy consumption with an average increase of 36% and low QoS performance with an average latency increase of 150%. For effective mitigation of the DAOF attack, a new lightweight solution based on a simple collaboration among RPL nodes is presented in this paper. It is referred to as DAOF-Secure RPL (DAOF-SRPL). It is based on simple in-protocol modifications to provide RPL with effective security support against the DAOF attack. In contrast to RPL in the attack scenarios, DAOF-SRPL achieved a reduction of over 80% in the total transmission rates of control messages. Meanwhile, it was able to maintain energy consumption and latency at minimal levels while preserving the same PDR results.

The present study aims to assess the impact of alfa and date palm natural fibers as reinforcement for epoxy, vinyl ester, and polyimide polymer (Alfa-Date palm/epoxy, Alfa-Date palm/vinyl ester, and Alfa-date palm/polyimide) through a genetic approach. The study employs a genetic simulation rooted in the Weibull formalism, employing genetic operators such as selection, crossing, and mutation for a nuanced evaluation of the damage at the fiber-matrix interface across the studied materials. The genetic algorithm results authentically capture the intrinsic behaviour of these materials, emphasising key mechanical properties like Young’s Modulus, deformation, and stress at break. The obtained results highlight that the matrix showing the highest Young’s modulus value at its fiber- matrix interface exhibits superior strength. These conclusions align with contemporary advancements in natural fiber-reinforced composite materials tailored for diverse industries and eco-friendly applications.

Despite the widespread adoption of the Routing Protocol for Low-power and Lossy Networks (RPL) in IoT environments, its inherent limitations in addressing security vulnerabilities have left IoT networks vulnerable to ongoing attacks. This paper introduces a novel intrusion detection system tailored specifically for IoT networks, with a focus on mitigating attacks at the network’s edge. The study presents the Hybrid Rank Attack (HRA), a sophisticated threat exploiting RPL vulnerabilities by alternately advertising decreased and increased rank values in control messages. Extensive experimentation evaluates the detrimental effects of HRA on critical network metrics including exchanged messages, energy consumption, PDR, latency, and memory footprint. Additionally, a lightweight and distributed countermeasure algorithm is proposed to effectively mitigate the impact of HRA. Simulation-based evaluations demonstrate significant reductions in control overhead (68.7%) and energy consumption (61.83%), with minimal additional RAM utilization (1.05%). This lightweight solution enhances the resilience of RPL-based IoT networks against HRA threats.

The potential worldwide deployment of Internet of Things (IoT) networks for a broad scope of critical applications has led to significant security concerns. With no effective support for IoT security, this rising trend would expose IoT deployments to numerous security threats. Due to their inherent resource constraints, IoT networks are critically susceptible to a wide range of adverse attacks. In particular, internal routing attacks pose serious challenges to network stability and lifetime. This is evident even for an IETF-standardized routing protocol like the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). It lacks adequate provisioning of network security, making an internal routing attack such as the common Version Number (VN) attack easy to initiate and effective to target performance degradation and resource exhaustion. It is also possible for further variants of such an adverse attack to emerge and incur a different and significant impact on RPL networks. In this paper, a novel variant of the VN attack, referred to as the Decreased VN (DVN) attack, in experimentally investigated. It differently operates by decreasing the propagated VN value in DIO messages to cause disruptive subnet partitioning and topological re-establishment. Experimental evaluation results revealed the severe effects of DVN attacks on overall network performance. Network overhead and energy consumption were increased by more than 300% and 220%, respectively. Additionally, a reduction of over 28% and an increase of over 400% in PDR and latency, respectively, were experienced during the attack. To effectively mitigate the DVN attack, a novel lightweight solution called DVN-Secure RPL (DVN-SRPL) is introduced in this paper. It is based on an effective and simple distributed security scheme with limited modifications to certain operational aspects of RPL design. Experimental testing of DVN-SRPL demonstrated its efficiency in detecting and containing DVN attacks. It succeeded in mitigating the adverse effects of the attacks with considerable reductions of 179% and 142% in energy consumption and network overhead, respectively. Comparable QoS performance to the attack-free RPL was also maintained during the attack.