In the rapidly evolving landscape of Software-Defined Networking (SDN), the enhancement of security measures against sophisticated cyber threats is paramount. Among these threats, inference attacks pose a significant risk by allowing adversaries to deduce the configurations and policies of SDN switches, thereby undermining the integrity and confidentiality of the network infrastructure. To address this critical issue, we introduce a novel dynamic rule replacement policy for SDN switches, leveraging the capabilities of a Support Vector Machine (SVM) for its implementation. Our approach utilizes a comprehensive set of statistical features, including duration analysis of flow rules, dispersion of packet match fields, and frequency of packet arrivals to identify patterns indicative of potential inference attacks. By dynamically adjusting the rules within SDN switches based on the analysis of these features, our policy significantly enhances the resilience of the network against such attacks. To accelerate the innovation and development of network services, this study proposes an integrated SDN architecture deployed over a serverless framework. This work serves as a starting point to enable researchers to realize the concept of modular serverless functions over traditional SDN environments. We show during inference attacks how a serverless framework improves the latency and resource utilization of the network compared to a traditional SDN framework. This study demonstrates an improvement in preventing inference attacks without compromising the performance and efficiency of the SDN infrastructure.

Among other deep learning (DL) architectures, the convolutional neural network (CNN) has wide applications in speech recognition, face detection, natural language processing, and computer vision. Multiply and Accumulate (MAC) unit is a core part of CNN and requires large computations and memory resources. They result in more power dissipation for low-power embedded devices. Hence, the hardware implementation of CNN to produce high throughput is one of the challenges nowadays. Therefore, sparsity is introduced in weights by a non-linear method with a minor compromise in accuracy. Experimental results also show the enhancement of 52% sparsity with a 4% loss in accuracy. In addition, an indexing module is proposed to perform Single Instruction Multiple Data (SIMD) operations in the fully connected layer to perform only effective operations without multiplication. This module is used along with sparsity to offer better results as compared to SOTA methods. Cadence RTL compiler results show that the proposed indexing module saves 1.3 nJ of energy as compared to the existing methods.

Human activity recognition (HAR) constitutes an integral part of occupant-centric smart services, such as health monitoring and building energy management. In this article, a simple and cost-effective solution to HAR through passive sensing of WiFi channel state information (CSI), is proposed. WiFi CSI extracted from ESP32 was utilized to classify four different human activities, using ensemble machine learning models. A mean accuracy of 83.39% was achieved using gradient boosting classifier with Haar wavelet-based denoising, in spite of using a single transmission link and in the presence of coexisting wireless devices. The proposed model can be employed for the development of an IoT-enabled smart LED lighting system, with minimal infrastructure changes. In this strategy, the illuminance level of LED lighting fixtures is adjusted according to the predicted occupant activity, thereby reducing power consumption, without compromising visual comfort. This method offers a potential energy savings of up to 36.42% and 29.45% per month, for a typical office and home scenario, respectively. The possibility of supplementing activity sensing with daylight harvesting, is also explored. An additional energy savings of up to 18.26% may be obtained using this method, during daytime. An evaluation of the annual electricity cost shows an estimated reduction between 29.43% and 62.13% using activity recognition and daylight harvesting.

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.

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.

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.

Connected cars are equipped with a rich set of sensors, such as GPS, accelerometer, video cameras, and pollution detectors. The information generated by these sensors can be used to offer a wide range of on-demand services, such as congestion notification, parking lots, and video surveillance. These services need a reliable and low latency unicast communication scheme in order to efficiently deliver the information requested by drivers. In this paper, a local traffic aware unicast routing scheme is proposed. To overcome network fragmentation, the proposed scheme relies on base stations and virtual base stations to transmit information from source car to the destination car using backhaul link. In this model, as base stations are sparsely deployed in the junction areas, a car moving in the junction area acts as a virtual base station node to support the routing process in the absence of a base station. Moreover, it avoids the impact of unreliable channel on information delivery. In the proposed scheme, each base station and virtual base station uses the short status messages (beacons) exchanged by the cars to form a local database of car locations. The stored information is used to find a base station or virtual base station that offers a minimum delay path to the destination car. The simulation results show that the proposed scheme outperforms the existing routing schemes in terms of end-to-end delay and packet delivery ratio. The proposed scheme is also validated by a connected car prototype built in an indoor laboratory environment.

In recent years, Industrial Wireless Sensor Network (IWSN) is gaining more popularity due to many applications in industries like fire detection, hazardous gas leakage detection, temperature monitoring, localization of sensors, etc. However, faulty sensors in the network may degrade the performance of the applications. In this paper, a software defined network (SDN) based fault detection method is proposed for IWSN. In this method, SDN plays an important role for controlling the whole system by setting a fault detection algorithm at the cluster heads (CHs). The CH periodically receives the monitoring data from the sensors and follows the fault detection algorithm set by the SDN to detect the faulty sensors in the network. The fault detection algorithm uses a statistical trimean method to detect the faulty sensors. Simulation results show that our proposed method performs better than Ji's fault detection method in terms of detection accuracy (DA) and false alarm rate (FAR). A IWSN prototype is also designed to evaluate the performance of the proposed method.