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Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. However, IoT devices are highly prone to botnet attacks. To mitigate this threat, a lightweight and anomaly-based detection mechanism that can create profiles for malicious and normal actions on IoT networks could be developed. Additionally, the massive volume of data generated by IoT gadgets could be analyzed by machine learning (ML) methods. Recently, several deep learning (DL)-related mechanisms have been modeled to detect attacks on the IoT. This article designs a botnet detection model using the barnacles mating optimizer with machine learning (BND-BMOML) for the IoT environment. The presented BND-BMOML model focuses on the identification and recognition of botnets in the IoT environment. To accomplish this, the BND-BMOML model initially follows a data standardization approach. In the presented BND-BMOML model, the BMO algorithm is employed to select a useful set of features. For botnet detection, the BND-BMOML model in this study employs an Elman neural network (ENN) model. Finally, the presented BND-BMOML model uses a chicken swarm optimization (CSO) algorithm for the parameter tuning process, demonstrating the novelty of the work. The BND-BMOML method was experimentally validated using a benchmark dataset and the outcomes indicated significant improvements in performance over existing methods. [ABSTRACT FROM AUTHOR]

The internet of things (ransomware refers to a type of malware) is the concept of connecting devices and objects of all types on the internet. IoT cybersecurity is the task of protecting ecosystems and IoT gadgets from cyber threats. Currently, ransomware is a serious threat challenging the computing environment, which needs instant attention to avoid moral and financial blackmail. Thus, there comes a real need for a novel technique that can identify and stop this kind of attack. Several earlier detection techniques followed a dynamic analysis method including a complex process. However, this analysis takes a long period of time for processing and analysis, during which the malicious payload is often sent. This study presents a new model of dwarf mongoose optimization with machine-learning-driven ransomware detection (DWOML-RWD). The presented DWOML-RWD model was mainly developed for the recognition and classification of goodware/ransomware. In the presented DWOML-RWD technique, the feature selection process is initially carried out using an enhanced krill herd optimization (EKHO) algorithm by the use of dynamic oppositional-based learning (QOBL). For ransomware detection, DWO with an extreme learning machine (ELM) classifier can be utilized. The design of the DWO algorithm aids in the optimal parameter selection of the ELM model. The experimental validation of the DWOML-RWD method can be examined on a benchmark dataset. The experimental results highlight the superiority of the DWOML-RWD model over other approaches. [ABSTRACT FROM AUTHOR]

Cyber security is becoming a challenging issue, because of the growth of the Internet of Things (IoT), in which an immense quantity of tiny smart gadgets push trillions of bytes of data over the Internet. Such gadgets have several security flaws, due to a lack of hardware security support and defense mechanisms, thus, making them prone to cyber-attacks. Moreover, IoT gateways present limited security features for identifying such threats, particularly the absence of intrusion detection techniques powered by deep learning (DL). Certainly, DL methods need higher computational power that exceeds the capability of such gateways. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies a DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process. The experimental validation of the PODCNN-LWID model is carried out on a benchmark dataset, and the results are assessed using varying measures. The comparison study reports the enhancements of the PODCNN-LWID model over other approaches. [ABSTRACT FROM AUTHOR]