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The Internet of Things (IoT) is rapidly expanding, connecting an increasing number of devices daily. Having diverse and extensive networking and resource-constrained devices creates vulnerabilities to various cyber-attacks. The IoT with the supervision of Software Defined Network (SDN) enhances the network performance through its flexibility and adaptability. Different methods have been employed for detecting security attacks; however, they are often computationally efficient and unsuitable for such resource-constraint environments. Consequently, there is a significant requirement to develop efficient security measures against a range of attacks. Recent advancements in deep learning (DL) models have paved the way for designing effective attack detection methods. In this study, we leverage Genetic Algorithm (GA) with a correlation coefficient as a fitness function for feature selection. Additionally, mutual information (MI) is applied for feature ranking to measure their dependency on the target variable. The selected optimal features were used to train a hybrid DNN model to uncover attacks in IoT networks. The hybrid DNN integrates Convolutional Neural Network, Bi-Gated Recurrent Units (Bi-GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM) for training the input data. The performance of our proposed model is evaluated against several other baseline DL models, and an ablation study is provided. Three key datasets InSDN, UNSW-NB15, and CICIoT 2023 datasets, containing various types of attacks, were used to assess the performance of the model. The proposed model demonstrates an impressive accuracy and detection time over the existing model with lower resource consumption.

The development of the Internet of Things (IoT) technology is leading to a new era of smart applications such as smart transportation, buildings, and smart homes. Moreover, these applications act as the building blocks of IoT-enabled smart cities. The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing. However, there is a high computation latency due to the presence of a remote cloud server. Edge computing, which brings the computation close to the data source is introduced to overcome this problem. In an IoT-enabled smart city environment, one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint. An efficient resource allocation at the edge is helpful to address this issue. In this paper, an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem. First, we presented a three-layer network architecture for IoT-enabled smart cities. Then, we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem. Learning Automata (LA) is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping. An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

Smart farming is essential for a nation whose economy largely depends on agro products. In the last few years, rapid urbanization and deforestation have impacted farmers. Due to the lack of rainwater harvesting and changing weather patterns, many crop failure cases have been registered in the last few years. To prevent loss of annual crop production, many researchers propose the technology-driven smart farming method. Smart farming is a technology-driven control environment for monitoring and maintaining the crop. Smart farming increases crop production and provides an alternative source of income to small farmers. To promote smart farming in India, the government initiated many pilot projects for integrated aquaculture farming. However, the lack of technological intervention and skill-oriented process makes it difficult for most farmers to succeed in this business. In this paper, we have proposed an intelligent IoT-based freshwater recirculating aquaculture system. The proposed system has integrated sensors and actuators. The sensor system monitors the water parameters, and actuators maintain the aquaculture environment. An intelligent data analytics algorithm played a significant role in monitoring and maintaining the freshwater aquaculture environment. The analytics derived the relationship between the water parameters and identified the relative change. From the experimental evaluation, we have identified that the M5 model tree algorithm has the highest accuracy for monitoring the relative change in water parameters.

The vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service-based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open-source MongoDB database, and the operations on the database are performed using the tool Node-Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service-based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end-to-end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end-to-end latency is carried out using T score.

In Cloud-based computing, job scheduling and load balancing are vital to ensure on-demand dynamic resource provisioning. However, reducing the scheduling parameters may affect datacenter performance due to the fluctuating on-demand requests. To deal with the aforementioned challenges, this research proposes a job scheduling algorithm, which is an improved version of a swarm intelligence algorithm. Two approaches, namely linear weight JAYA (LWJAYA) and chaotic JAYA (CJAYA), are implemented to improve the convergence speed for optimal results. Besides, a load-balancing technique is incorporated in line with job scheduling. Dynamically independent and non-pre-emptive jobs were considered for the simulations, which were simulated on two disparate test cases with homogeneous and heterogeneous VMs. The efficiency of the proposed technique was validated against a synthetic and real-world dataset from NASA, and evaluated against several top-of-the-line intelligent optimization techniques, based on the Holms test and Friedman test. Findings of the experiment show that the suggested approach performs better than the alternative approaches.

With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.

Internet of things (IoT) facilitates a variety of heterogeneous devices to be enabled with network connectivity via various network architectures to gather and exchange real-time information. On the other hand, the rise of IoT creates Distributed Denial of Services (DDoS) like security threats. The recent advancement of Software Defined-Internet of Things (SDIoT) architecture can provide better security solutions compared to the conventional networking approaches. Moreover, limited computing resources and heterogeneous network protocols are major challenges in the SDIoT ecosystem. Given these circumstances, it is essential to design a low-cost DDoS attack classifier. The current study aims to employ an improved feature selection (FS) technique which determines the most relevant features that can improve the detection rate and reduce the training time. At first, to overcome the data imbalance problem, Edited Nearest Neighbor-based Synthetic Minority Oversampling (SMOTE-ENN) was exploited. The study proposes SFMI, an FS method that combines Sequential Feature Selection (SFE) and Mutual Information (MI) techniques. The top k common features were extracted from the nominated features based on SFE and MI. Further, Principal component analysis (PCA) is employed to address multicollinearity issues in the dataset. Comprehensive experiments have been conducted on two benchmark datasets such as the KDDCup99, CIC IoT-2023 datasets. For classification purposes, Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Random Forest (RF), and Multilayer Perceptron classifiers were employed. The experimental results quantitatively demonstrate that the proposed SMOTE-ENN+SFMI+PCA with RF classifier achieves 99.97% accuracy and 99.39% precision with 10 features.

SDN has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table’s storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. When suspicious activity is identified, FloRa promptly activates the machine-learning based detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49% which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN.

Cloud computing is widely being used by researchers’ academia and industry for its abundant opportunities. Different technologies such as Internet of Things, Edge Computing, and Fog Computing are gradually integrating with the cloud platform due to its scalability and availability. The number of cloud users is also increasing exponentially. The requests generated from wide range of users are random. Executions of request and providing the quality of service are one of the promising issues in cloud environment. Optimization of response time and commutation cost is the major concern in cloud environment. Researchers have proposed many heuristics, meta-heuristic approaches for solving the load balancing issues in cloud platform. In this paper, authors have proposed a hybrid approach for load balancing in cloud computing using genetic algorithm with gravitational search algorithm. Simulations are carried out using cloud Sim Simulator and comparisons are made with other competitive approaches to evaluate the performance of the system. It is observed that the hybrid approach outperforms in various measures.

Logic locking has become a robust method for reducing the risk of intellectual property (IP) piracy, overbuilding, and hardware Trojan threats throughout the lifespan of integrated circuits (ICs). Nevertheless, the majority of reported logic locking approaches are susceptible to satisfiability (SAT)-based attacks. The existing SAT-resistant logic locking methods provide a tradeoff between security and effectiveness and require a significant design overhead. In this article, a novel gate replacement-based input-dependent key-based logic locking (IDKLL) technique is proposed. We first introduce the concept of IDKLL, and how the IDKLL can mitigate the SAT attacks completely. Unlike conventional logic locking, the IDKLL approach uses multiple key sequences (KSs) (instead of a single KS) as the correct key to lock/unlock the design functionality for all inputs. Based on this IDKLL concept, we developed several locked gates. Further, we propose a lightweight gate replacement-based IDKLL called GateLock that locks the design by replacing exciting gates with their respective IDKLL-based locked gates. The security analysis of the proposed method shows that it prevents the SAT attack completely and forces the attacker to apply a significantly large number of brute-force attempts to decipher the key. The experimental evaluation on International Symposium on Circuits and Systems (ISCAS) and International Test Conference (ITC) benchmarks shows that the proposed GateLock method completely prevents the SAT-based attacks and requires an average of 56.7%, 72.7%, and 87.8% reduced area, power, and delay compared to cascaded locking (CAS-Lock) and strong Anti-SAT (SAS) approaches.