Today’s smart city infrastructure is predominantly dependant on Internet of Things (IoT) technologies. IoT technology essentially facilitates a platform for service automation through connections of heterogeneous objects via the Internet backbone. However, the security issues associated with IoT networks make smart city infrastructure vulnerable to cyber-attacks. For example, Distributed Denial of Service (DDoS) attack violates the authorization conditions in smart city infrastructure; whereas replay attack violates the authentication conditions in smart city infrastructure. Both attacks lead to physical disruption to smart city infrastructure, which may even lead to financial loss and/or loss of human lives. In this paper, a hybrid deep learning model is developed for detecting replay and DDoS attacks in a real life smart city platform. The performance of the proposed hybrid model is evaluated using real life smart city datasets (environmental, smart river and smart soil), where DDoS and replay attacks were simulated. The proposed model reported high accuracy rates: 98.37% for the environmental dataset, 98.13% for the smart river dataset, and 99.51% for the smart soil dataset. The results demonstrated an improved performance of the proposed model over other machine learning and deep learning models from the literature.

Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research.

In the sixth-generation (6G) cellular networks, hybrid beamforming would be a real-time optimization problem that is becoming progressively more challenging. Although numerical computation-based iterative methods such as the minimal mean square error (MMSE) and the alternative manifold optimization (Alt-Min) can already attain near-optimal performance, their computational cost renders them unsuitable for real-time applications. However, recent studies have demonstrated that machine learning techniques like deep neural networks (DNN) can learn the mapping done by those algorithms between channel state information (CSI) and near-optimal resource allocation and then approximate this mapping in near real-time. In light of this, we investigate various DNN architectures for beamforming challenges in the terahertz (THz) band for ultra-massive multiple-input multiple-output (UM-MIMO) and explore their contextual mathematical modeling. Specifically, we design a sophisticated 1D convolutional neural network, and long short-term memory (1D CNN-LSTM) based fusion-separation scheme, which can approach the performance of the Alt-Min algorithm in terms of spectral efficiency (SE) for fully connected structures and, at the same time, use significantly less computational effort. Simulation results indicate that the proposed system can attain almost the same level of SE as that of the numerical iterative algorithms while incurring a substantial reduction in computational cost. Our DNN-based approach also exhibits exceptional adaptability to diverse network setups and high scalability. Although the current model only addresses the fully connected hybrid architecture, our approach can also be expanded to address a variety of other beamforming topologies.

Wireless body area networks (WBANs) are intelligent wireless monitoring systems, consisting of wearable, and implantable computing devices on or in the human body. They are used to support a variety of personalized, advanced, and integrated applications in the field of medical, fitness, sports, military, and consumer electronics. In a WBAN, network longevity is a major challenge due to the limitation of the availability of energy supply in body nodes. Therefore, routing protocols can play a key role towards making such networks energy efficient. In this work, we exhibit that a routing protocol together with an effective body node coordinator (BNC) deployment strategy can influence the network lifetime eminently. Our initial work shows that the variation in the placement of a BNC within a WBAN could significantly vary the overall network lifetime. This motivated us to work on an effective node placement strategy for a BNC, within a WBAN; and thus we propose three different BNC placement algorithms considering different features of available energy efficient routing protocols in a WBAN. Our simulation results show that these algorithms along with an appropriate routing protocol can prolong the network lifetime by up to 47.45%.

Over the past few years, billions of unsecured Internet of Things (IoT) devices have been produced and released, and that number will only grow as wireless technology advances. As a result of their susceptibility to malware, effective methods have become necessary for identifying IoT malware. However, the low generalizability and the nonindependently and identically distributed data (non-IID) still pose a major challenge to achieving this goal. In this work, a new federated malware detection paradigm, termed FED-MAL, is introduced to collaboratively train multiple distributed edge devices to detect malware. In FED-MAL, the malware binaries are transformed into an image format to lessen the impact on non-IID, and then a compact convolutional model, named AM-NET, is proposed to learn the malware patterns as an image recognition task. The compact nature of AM-NET makes it an appropriate choice for deployment on resource-constrained IoT devices. Following, a refined edge-based adversarial training is given in FED-MAL to empower generalizability and resistibility by generating adversarial samples from various participating clients. Experimental evaluation on publicly available malware data sets shows that the FED-MAL is efficacious, reliable, expandable, generalizable, and communication efficient.