OAuth 2.0 provides an open secure protocol for authorizing users across the web. However, many modalities of this standard allow these protections to be implemented optionally. Thus, its use does not guarantee security by itself and some of the deployment options in the OAuth 2.0 specification can lead to incorrect settings. FIWARE is an open platform for developing Internet applications of the future. It is the result of the international entity Future Internet Public-Private Partnership. [1,2] FIWARE was designed to provide a broad set of API to stimulate the development of new businesses in the context of the European Union. This platform can be understood as a modular structure to reach a broad spectrum of applications such as IoT, big data, smart device management, security, open data, and virtualization, among others. Regarding security, the exchange of messages between its components is done through the OAuth 2.0 protocol. The objective of the present work is to create a system that allows the detection and analysis of vulnerabilities of OAuth 2.0, executed on HTTP/HTTPS in an on-premise development environment focused on the management of IoT devices and to help developers to implement them ensuring security for these environments. Through the system proposed by this paper, it was possible to find vulnerabilities in FIWARE components in HTTP/HTTPS environments. With this evidence, mitigations were proposed based on the mandatory recommendations by the IETF.

With attackers discovering more inventive ways to take advantage of network weaknesses, the pace of attacks has drastically increased in recent years. As a result, network security has never been more important, and many network intrusion detection systems (NIDS) rely on old, out-of-date attack signatures. This necessitates the deployment of reliable and modern Network Intrusion Detection Systems that are educated on the most recent data and employ deep learning techniques to detect malicious activities. However, it has been found that the most recent datasets readily available contain a large quantity of benign data, enabling conventional deep learning systems to train on the imbalance data. A high false detection rate result from this. To overcome the aforementioned issues, we suggest a Synthetic Minority Over-Sampling Technique (SMOTE) integrated convolution neural network and bi-directional long short-term memory SCNN-BIDLSTM solution for creating intrusion detection systems. By employing the SMOTE, which integrates a convolution neural network to extract spatial features and a bi-directional long short-term memory to extract temporal information; difficulties are reduced by increasing the minority samples in our dataset. In order to train and evaluate our model, we used open benchmark datasets as CIC-IDS2017, NSL-KDD, and UNSW-NB15 and compared the results with other state of the art models.

The persistent threat of malicious applications targeting Android devices has been growing in numbers and severity. Numerous techniques have been utilized to defend against this thread, including heuristic-based ones, which are able to detect unknown malware. Among the many features that this technique uses are system calls. Researchers have used several representation methods to capture system calls, such as histograms. However, some information may be lost if the system calls as a feature is only represented as a 1-dimensional vector. Graphs can represent the interaction of different system calls in an unusual or suspicious way, which can indicate malicious behavior. This study uses machine learning algorithms to recognize malicious behavior represented in a graph. The system call graph was fed into machine learning algorithms such as AdaBoost, Decision Table, Naïve Bayes, Random Forest, IBk, J48, and Logistic regression. We further employ a series feature selection method to improve detection accuracy and eliminate computational complexity. Our experiment results show that the proposed method has reduced feature dimension to 91.95% and provides 95.32% detection accuracy.

The Internet of Things (IoT) technology has changed the contemporary digital world. Devices connected to the IoT have sensors embedded within them. All these devices are purposely connected to share data among them through the Internet. Data sharing among IoT devices needs some security protocols to maintain the privacy and confidentiality of information. IoT devices have less computing power to perform various operations of a cryptographic process. So, there is a need of cryptographic approach to reduce the computational complexity for resource-constrained devices and provide data security. However, storing data over the cloud server also reduces storage overhead, but data transmission via the cloud is not always secure. Data integrity and authentication can be compromised because the end user can only access the data with the help of a cloud server. To ensure the security and integrity of the data, various cryptographic techniques are used. Therefore, in this paper, we propose a secure and optimized hybrid cryptographic scheme for the secure sharing of data by combining Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC). To ensure authentication and data integrity, the proposed scheme primarily uses the Message Authentication Code (MAC). The encrypted messages are stored on a cloud server to reduce storage overhead. The experimental findings demonstrate that the proposed scheme is effective and produces superior results as compared to existing approaches.

With the increasing number of co-residence attacks, the security of the multi-tenant public IaaS cloud environment has become a growing concern. The co-residence attacker creates a side channel to retrieve the secured data. These attacks help the adversary to leak out the sensitive information of the user with whom it is co-located. This paper discusses a secured VM placement technique, Previous Server and Co-resident users First (PSCF), which focuses on facilitating security against the co-residence attack by minimizing the probability of co-locating the malicious user with the authentic user. Co-location resistance and core utilization metrics are utilized to evaluate the algorithm’s performance. The proposed method is simulated, and the result is analysed and compared with existing approaches like Best Fit, Worst Fit, PSSF, and SC-PSSF. It is observed that the proposed approach furnished maximum co-location resistance of 74.32% and a core utilization of 82.63%. Further, the algorithm has shown significant performance in balancing the load and energy consumption. The result has reduced the probability that malicious users co-located with the authentic one, thus reducing the security breach of confidential information. 

In this manuscript, an Individual Updating Strategies-based Elephant Herding Optimization Algorithm are proposed to facilitate the effective load balancing (LB) process in cloud computing. Primary goal of proposed Individual Updating Strategies-based Elephant Herding Optimization Algorithm focus on issuing the workloads pertaining to network links by the purpose of preventing over-utilization and under-utilization of the resources. Here, NIUS-EHOA-LB-CE is proposed to exploit the merits of traditional Elephant Herd Optimization algorithm to achieve superior results in all dimensions of cloud computing. In this NIUS-EHOA-LB-CE achieves the allocation of Virtual Machines for the incoming tasks of cloud, when the number of currently processing tasks of a specific VM is less than the cumulative number of tasks. Also, it  attains potential load balancing process differences with the help of each individual virtual machine’s processing time and the mean processing time (MPT) incurred by complete virtual machine. Efficacy of the proposed technique activates the Cloudsim platform. Experimental results of the proposed method shows lower Mean Response time 11.6%, 18.4%, 20.34%and 28.1%, lower Mean Execution Time 78.2%, 65.4%, 40.32% and 52.6% compared with existing methods, like Improved Artificial Bee Colony utilizing Monarchy Butterfly Optimization approach for Load Balancing in Cloud Environments (IABC-MBOA-LB-CE), An improved Hybrid Fuzzy-Ant Colony Algorithm Applied to Load Balancing in Cloud Computing Environment (FACOA-LB-CE), Hybrid firefly and Improved Multi-Objective Particle Swarm Optimization for energy efficient LB in Cloud environments (FF-IMOPSO-LB-CE) and A hybrid gray wolf optimization and  Particle Swarm Optimization algorithm for load balancing in cloud computing environment (GWO-PSO-LB-CE).

In fog computing, computing resources are deployed at the network edge, which can include routers, switches, gateways, and even end-user devices. Fog computing focuses on running computations and storing data directly on or near the fog devices themselves. The data processing occurs locally on the device, reducing the reliance on network connectivity and allowing for faster response times. However, the conventional intrusion detection system (IDS) failed to provide security during the data transfer between fog nodes to cloud, fog data centres. So, this work implemented the optimized IDS in fog computing environment (OIDS-FCE) using advanced naturally inspired optimization algorithms with extreme learning. Initially, the data preprocessing operation maintains the uniform characteristics in the dataset by normalizing the columns. Then, comprehensive learning particle swarm based effective seeker optimization (CLPS-ESO) algorithm extracts the intrusion specific features by analyzing the internal patterns of all rows, columns. In addition, automatic termination-based whale optimization algorithm (ATWOA) selects the best intrusion features from CLPS-ESO resultant features using correlation analysis. Finally, the hybrid extreme learning machine (HELM) classifies the varies instruction types from ATWOA optimal features. The simulation results show that the proposed OIDS-FCE achieved 98.52% accuracy, 96.38% precision, 95.50% of recall, and 95.90% of F1-score using UNSW-NB dataset, which are higher than other artificial intelligence IDS models. 

Blockchain technology is a decentralized ledger system that finds applications in various domains such as banking, e-governance, and supply chain management. The consensus algorithm plays a crucial role in any blockchain network as it directly impacts the network's performance and security. There have been several proposed consensus mechanisms in the literature, including Proof of Work (PoW), Proof of Stake (PoS), Robust Proof of Stake (RPoS), and Delegated Proof of Stake (DPoS). Both Ethereum and Bitcoin utilize the PoW consensus mechanism, where nodes compete to solve puzzles in order to generate blocks, consuming significant processing power. On the other hand, the PoS consensus mechanism selects miners based on the stakes they hold, making it more energy efficient. However, PoS has drawbacks such as vulnerability to coin age accumulation attacks and the potential for partial centralization. In this work, we present a consensus mechanism known as Delegated Proof of Stake with Downgrading Mechanism using Game Theory (DDPoS (GT)). This mechanism employs a two-step game strategy to divide nodes into strong and weak nodes, as well as attack and non-attack nodes. Later, the results of the two games are combined to enhance protocol efficiency and security. Experimental results using a private Ethereum-based network demonstrate that DDPoS (GT) performs better than PoS and DPoS in terms of transaction latency, average block waiting time, and fairness.

Spiking Neural Network (SNN) use spiking neurons that transmit information through discrete spikes, similar to the way biological neurons communicate through action potentials. This unique property of SNNs makes them suitable for applications that require real-time processing and low power consumption. This paper proposes a new method for detecting DDoS attacks using a spiking neural network (SNN) with a distance-based rate coding mechanism and optimizing the SNN using a genetic algorithm (GA). The proposed GA-SNN approach achieved a remarkable accuracy rate of 99.98% in detecting DDoS attacks, outperforming existing state-of-the-art methods. The GA optimization approach helps to overcome the challenges of setting the initial weights and biases in the SNN, and the distance-based rate coding mechanism enhances the accuracy of the SNN in detecting DDoS attacks. Additionally, the proposed approach is designed to be computationally efficient, which is essential for practical implementation in real-time systems. Overall, the proposed GA-SNN approach is a promising solution for accurate and efficient detection of DDoS attacks in network security applications.

This paper proposes an intrusion detection system to prevent malicious node attacks that may result in failure links in wireless body area networks. The system utilizes a combination of Optimized Convolutional Neural Networks and Support Vector Machine techniques to classify nodes as malicious or not, and links as failure or not. In case of detection, the system employs a trust-based routing strategy to isolate malicious nodes or failure links and ensure a secure path. Furthermore, sensitive data is encrypted using a modified RSA encryption algorithm. The experimental results demonstrate the improved network performance in terms of data rate, delay, packet delivery ratio, energy consumption, and network security, by providing effective protection against malicious node attacks and failure links. The proposed system achieves the highest classification rate and sensitivity, surpassing similar methods in all evaluation metrics.