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The Internet of Things (IoT) is becoming more prevalent in our daily lives. A recent industry report projected the global IoT market to be worth more than USD 4 trillion by 2032. To cope with the ever-increasing IoT devices in use, identifying and securing IoT devices has become highly crucial for network administrators. In that regard, network traffic classification offers a promising solution by precisely identifying IoT devices to enhance network visibility, allowing better network security. Currently, most IoT device identification solutions revolve around machine learning, outperforming prior solutions like port and behavioural-based. Although performant, these solutions often experience performance degradation over time due to statistical changes in the data. As a result, they require frequent retraining, which is computationally expensive. Therefore, this article aims to improve the model performance through a robust alternative feature set. The improved feature set leverages payload lengths to model the unique characteristics of IoT devices and remains stable over time. Besides that, this article utilizes the proposed feature set with Random Forest and OneVSRest to optimize the learning process, particularly concerning the easier addition of new IoT devices. On the other hand, this article introduces weekly dataset segmentation to ensure fair evaluation over different time frames. Evaluation on two datasets, a public dataset, IoT Traffic Traces, and a self-collected dataset, IoT-FSCIT, show that the proposed feature set maintained above 80% accuracy throughout all weeks on the IoT Traffic Traces dataset, outperforming selected benchmark studies while improving accuracy over time by +10.13% on the IoT-FSCIT dataset. [ABSTRACT FROM AUTHOR]

The strength of Vehicular Ad hoc Networks (VANETs) and the rapid deployment capability, can be used in many situations where the network should be arranged in a short time and there is a need to collect sensitive information. We consider cluster-based attack detection in data compilation wherever the neighbor nodes give the important information to the cluster head. Moreover, evidence is obtainable in the cluster head may possibly be accumulated by some vehicular nodes and executes numerous responsibilities such as decision making about delivering information. The existence of malicious nodes threatens determination making through transmitting malevolent information, which is not appropriate to the VANET categorized data and might send a substantial number of packets to the vehicles or Road Side Unit (RSU). To overcome this issue, we have proposed a Stream Position Performance Analysis (SPPA) approach. This approach monitors the position of any field station in sending the information to perform a Distributed Denial of Service (DDoS) attack. The method computes various factors like Conflict field, Conflict data and Attack signature sample rate (CCA). Using all these factors, the method identifies the trustworthiness of the packet and includes it in decision making. The proposed approach increases the performance of a Distributed Denial of Service (DDoS) attack detection in a VANET environment. [ABSTRACT FROM AUTHOR]

ABSTRACT An essential problem in mobile ad hoc networks is finding an efficient and secure route from a source to an intended destination. In this paper, we have proposed a new model of routing protocol named ARANz, which is an extension of the original Authenticated Routing for Ad Hoc Networks (ARAN). ARANz adopts the authentication methods used with ARAN and aims to increase security, achieve robustness and solve the single point of failure and attack problems by introducing multiple local certificate authority servers. Additionally, via dealing with the network as zones and using restricted directional flooding, our new model exhibits better scalability and performance. Through simulation, we evaluated ARANz and compared it with the original ARAN as well as Ad Hoc On-demand Distance Vector. Simulation results show that ARANz is able to effectively discover secure routes within relatively large networks with large number of mobile nodes, while maintaining the minimum packet routing load. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mobile Ad-Hoc Networks (MANETs) are becoming increasingly popular as more and more mobile devices find their way to the public. A crucial problem in Ad-Hoc networks is finding an efficient route between a source and a destination. Due to MANET's inherent characteristics, secure routing may be one of the most difficult areas to tackle because opponents can add themselves to a MANET using the existing common routing protocols. Hence, this paper proposed a new model of routing protocol called ARANz, which is an extension of the original Authenticated Routing for Ad-Hoc Networks (ARAN). Apart from the authentication methods adopted from ARAN, ARANz aims to increase security, achieve robustness and solve the single point of failure and attack problems by introducing multiple Local Certificate Authority servers. Moreover, by dealing with the network as zones and using restricted directional flooding, our new model will exhibit better scalability and performance. An overview and a qualitative comparison between ARANz and some existing Ad-Hoc routing protocols is presented in this paper. [ABSTRACT FROM AUTHOR]