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The security of IoT networks has become a major concern with the ubiquity of Internet of Things (IoT) technology. The concept of intrusion detection system (IDS) is a complex system to discover an intruder in the IoT platform, where the intruder can be a host that tries to gain some other nodes without authorization. Due to the complexity and resource constraints, classical IDS have their limitations in the context of IoT networks. There has been significant research in integrating both IDS and blockchain (BC) to detect existing and emerging cyberattacks and improve data privacy, correspondingly. In these approaches, learning-based ensemble algorithms can concurrently ensure data privacy and facilitate the detection of sophisticated malicious events. This study introduces a BC with fractal chaotic oppositional barnacles mating optimizer-based deep learning (BCOBMO-DL) Model for Secure IoT environment. The BCOBMO-DL technique exploits BC technology with DL-based intrusion detection to protect the IoT environment. In the BCOBMO-DL technique, the linear scaling normalization (LSN) approach can be used to scale the input data into a uniform format. In addition, the BCOBMO-DL technique designs the COBMO algorithm for electing the optimal subset of features. For intrusion detection, the self-attention bidirectional gated recurrent unit (SA-BiGRU) model is applied with fractal theory. Finally, the reptile search algorithm (RSA)-based hyperparameter tuning process is utilized for tuning the parameters based on the SA-BiGRU model. Furthermore, the BC technology is useful for achieving security in the IoT network. The experimental evaluation of the BCOBMO-DL method takes place on the benchmark NSLKDD dataset. The widespread experimental outcomes highlighted the enhanced detection outcomes of the BCOBMO-DL algorithm over other models.

Distributed Denial of Service (DDoS) attacks remain a persistent and formidable threat in the ever-changing world of cyber security. These attacks have the potential to disrupt internet services and cause substantial financial and reputational concerns. The major challenge is developing an adaptable and real-time Intrusion Detection System (IDS) that can detect and neutralize DDoS attacks effectively and quickly, even when attackers use increasingly advanced ways to avoid detection. The problem concerns the development of a dynamic and real-time intrusion detection technique that combines the benefits of logistic regression for anomaly detection with GoogLeNet for deep learning-based network traffic analysis. This paper proposes a unique framework for intrusion detection that blends logistic regression-based anomaly detection with GoogLeNet deep learning capabilities. The combination of these technologies makes it easier to identify and mitigate DDoS attacks, hence improving the security of internet-based systems. The proposed IDS framework utility is proved through experimental evaluations, which highlight its capacity to effectively identify DDoS attacks while minimizing false positives. The use of this technology in real-time during security games demonstrates its potential to improve online service security infrastructure and reduce the impact of DDoS attacks on critical networks and data resources.

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

The capacity to identify breaches and malicious activity inside the Internet of Things (IoT) networks is important for network infrastructure resilience as the dependence on IoT devices and services grows. Intrusion detection systems (IDS) are basic components of network security. IDSs monitor and analyze the activity of a system in a network to identify intrusions. Existing intrusion detection systems (IDS) gather and utilize large amounts of data with irrelevant, unnecessary, and unsuitable characteristics, resulting in long detection times and low accuracy. In this paper, we present an IDS model based on a Random Forest (RF) classifier. NSL-KDD dataset is used to test the performance of the model and the satisfying performance is obtained in terms of accuracy, detection rate, and false alarm rate. The proposed model has attained an average accuracy of 99.3% and 98% for binary classification and multiclass classification, respectively. To demonstrate the efficacy of the suggested model, its accuracy was compared with some existing approaches that utilize other models such as AIDS, ELM and PCA, MapReduce-based hybrid architecture, and DRNN.

This article will look at the ways an organisation can prepare for, deal with and then recover from a cyber attack against it. There are multiple possible scenarios that can be attributed to a cyber attack. This paper looks at the creation of security policies, cyber incident response plans and organizational risk and cyber insurance.

In this paper we propose a new approach to reduce excessive duplicate alerts and high false positive rates in IDS. We used an improved quantum-behaved particle swarm optimization (IQPSO) algorithm byintroducing multiple segment processing and absorption wall. During Alert aggregation processing, wecalculate similarity of alert by fuzzy membership function firstly, then optimize attribute weights a similarity threshold by IQPSO. Experimental results show that the algorithm is effective in alert aggregation and gives better results in reducing false positive rate and duplicate alerts.

A number of neighbor-monitoring, trust-building, and cluster-based voting schemes have been proposed in the research to enable the detection and reporting of malicious activity in ad hoc networks. The resources consumed by ad hoc network member nodes to monitor, detect, report, and diagnose malicious activity, however, may be greater than simply rerouting packets through a different available path. In this paper we present a method for determining conditions under which critical nodes should be monitored, describes the details of a critical node test Simulation, presents experimental results, and offers a new approach for conserving the limited resources of an ad hoc network IDS.