ASPG

An Effective FOG Computing Based Distributed Forecasting of Cyber-Attacks in Internet of Things

Vandana Roy

Existing cloud based security procedures are insufficient to manage the ever-increasing assaults in IoT due to the volume of data generated and the processing latency. IoT applications are vulnerable to cyberattacks, and some of these assaults might have catastrophic results if not stopped or mitigated quickly enough. As a result, IoT calls for self-protect security systems that can automatically interpret attacks in IoT traffic and efficiently handle the attack situation by activating the proper response quickly. Fog computing satisfies this need because it can embed the intelligent self-protection mechanism in the distributed fog nodes, allowing them to swiftly deal with the assault scenario and safeguard the IoT application with little in the way of human interaction. At the fog nodes, the forecasting method employs distributed Gaussian process regression. The cyber-attack may be predicted more quickly and with less mistake for both low- and high-rate attacks thanks to the local forecasting about the IoT traffic characteristics at fog node. One of the fundamental necessities of an IoT security mechanism is the ability to forecast attacks in a timely manner with a high degree of accuracy, and the simulation results highlight this fact.

Vol. 12 Issue. 2 PP. 08-17, (2023)

Improved Method for Enhanced Quality of Service in IoHT Task Dependency Optimization

Rumi iqbal doewes , Preeti Saini

Keeping a proper level of task dependency throughout the scheduling process is critical to achieving the aim of decreasing the make-span rate in Internet of Health Things (IoHT) projects. We provide a smart model strategy for effective task scheduling in the IoHT environment for e-healthcare systems by merging hybrid moth flame optimisation (HMFO) with cloud computing. The HMFO algorithm guarantees that all available resources are distributed evenly, resulting in improved quality of service (QoS). We study the Google cluster dataset to learn about the scheduling behaviours of cloud-based jobs in order to train our model. After training, an HMFO model may be used to plan activities in real time. To assess the success of our strategy, we run simulations in the CloudSim environment, taking into account crucial parameters such as resource utilisation, reaction time, and energy consumption. According to a comparative analysis, our hybrid HMFO system surpasses the alternatives in terms of reaction time, average run duration, and cost savings. Our method has proven to be effective due to the favourable effects it has had on response rates, prices, and run times. Combining IoT and cloud computing has the potential to improve healthcare delivery in a variety of ways. One unique strategy we offer for scheduling IOHT jobs is to combine a deep neural network (DNN) algorithm with the MFO technique. Job scheduling in electronic healthcare systems can be optimised with the help of our hybrid MFO-DNN algorithm by taking into account a variety of different objectives, the most important of which are lowering response times while improving resource utilisation and maintaining consistent load balances. The MFO approach searches the search space and provides early solutions, while the DNN algorithm refines and improves those first findings. In comprehensive simulations conducted in a real-world hospital setting, the hybrid MFO-DNN technique outperformed existing scheduling algorithms in terms of reaction time, resource utilisation, and load balancing. The simulated healthcare environments were as true to life as was feasible. The suggested technique has been demonstrated to be both dependable and scalable, making it appropriate for use in large-scale IOHT deployments. This study considerably enhances the state of the art in IOHT task scheduling in E healthcare systems by developing a hybrid optimisation technique that takes advantage of the strengths of both MFO and DNN. The findings indicate that this strategy has the potential to improve the quality and efficiency of healthcare delivery, which helps patients receive care that is both effective and timely.

Vol. 12 Issue. 2 PP. 18-35, (2023)

Maximizing Anomaly Detection Performance in Next-Generation Networks

Pallavi Goel , Sarika Chaudhary

The paper discusses major components of the proposed intrusion detection system as well as associated ideas. Dimensionality reduction solutions are highly valued for their potential to improve the efficiency of anomaly detection. Furthermore, feature selection and fusion methods are applied to optimise the system's capabilities. The following summary of network control, management, and cloud-based network processing aspects highlights operations managers, cloud resources, network function virtualization (NFV), and hardware and software components. We discuss prospective Deep Autoencoders (DAEs) applications, such as their use in the dimensionality reduction module, training methodologies, and benefits. Data transformation utilising coded representations is also graphically displayed and described in the text using an encoder and decoder system. The role of the anomaly detection via virtual network function in the suggested technique is also investigated. This component leverages a deep neural network (DNN) to identify anomalies in the 5G network's peripherals. DNN design issues, optimisation methodologies, and the trade-off between model complexity and detection efficacy are also discussed. Overall, the passage provides an overview of the proposed intrusion detection scheme, its components, and the techniques employed, underscoring their contributions to improving efficiency, accuracy, and security in Next Generation Networks.

Vol. 12 Issue. 2 PP. 36-51`, (2023)

Transforming Healthcare Infrastructure for Enhanced Energy Efficiency and Privacy

Sudeshna chakraborty , Akanksha Singh

The Internet of Medical Things (IoMT) is a revolutionary technique for integrating the IT infrastructure of healthcare organisations with medical apps and equipment. Rapid advancements in this approach in recent years have resulted in game-changing improvements in the healthcare system, illness management, and patient care standards. Both achievements have been made possible by the Internet of Medical Things. People can use the IoMT to access a variety of cloud-based services, including file sharing, patient monitoring, data collection, information gathering, and hospital cleaning. Wireless sensor networks (WSNs), which collect and transmit data, are critical to system operation. In the healthcare system, patients’ privacy and security must be preserved at all costs. Wireless data transmission from these cutting-edge devices may have been intercepted and manipulated without consent. The hybrid and improved (Elliptic Curve Cryptography ECC) Energy-Efficient Routing Protocol (EERP) method, which is based on the elliptic curve encryption protocol, may provide enough protection for sensitive information. ECC-EERP uses pairs of public and private keys known only to each other to decode and encrypt data delivered across a network. As a result, the energy needed to sustain WSNs has dropped. To assess the efficacy of the recommended plan, we did an extensive study and compared our findings to the many other viable courses of action. We did the analysis while taking a variety of aspects into account. The study's findings and conclusion all point to the strategy's ability to significantly increase energy efficiency and security. ECC-EERP is a novel encryption method that increases data security while consuming less energy. Because of its efficacy in improving the whole healthcare system, this strategy has a lot of potential for the future of patient care, illness management, and healthcare delivery in general.

Vol. 12 Issue. 2 PP. 52-68, (2023)

Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security

Rajit Nair

The growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projections, we integrate deep learning models with the Shapley Additive Reasons (SHAP) approach. By adding weight to distinct elements of the input data needed to develop the model, we increase the readability of so-called "black box" processes. We use the ToN_IoT dataset, which provides statistics on the volume of network traffic created by IoT-enabled transport systems, to assess the success of our strategy. We use a tool called CICFlowMeter to create network flows and collect data. The regularity of the flows, as well as their correlation with specific assaults, has been documented, allowing us to train and evaluate the IDS model. The experiment findings show that our explainable deep learning system is extremely accurate at detecting and categorising intrusions in IoT-enabled transportation networks. By examining data using the SHAP approach, cybersecurity specialists may learn more about the IDS's decision-making process. This enables the development of robust solutions, which improves the overall security of the Internet of Things. Aside from simplifying IDS predictions, the proposed technique provides useful recommendations for strengthening the resilience of IoT-enabled transportation systems against cyberattacks. The usefulness of IDS in defending mission critical IoT infrastructure has been questioned by security experts in the Internet of Vehicles (IoV) industry. The IoV is the primary research object in this case. Deep learning algorithms' versatility in processing many forms of data has contributed to their growing prominence in the field of anomaly detection in intrusion detection systems. Although machine learning models may be highly useful, they frequently yield false positives, and the path they follow to their conclusions is not always obvious to humans. Cybersecurity experts who want to evaluate the performance of a system or design more secure solutions need to understand the thinking process behind an IDS's results. The SHAP approach is employed in our proposed framework to give greater insight into the decisions made by IDSs that depend on deep learning. As a result, IoT network security is strengthened, and more cyber-resilient systems are developed. We demonstrate the effectiveness of our technique by comparing it to other credible methods and utilising the ToN_IoT dataset. Our framework has the best success rate when compared to other frameworks, as evidenced by testing results showing an F1 score of 98.83 percent and an accuracy of 99.15 percent. These findings demonstrate that the architecture successfully resists a variety of destructive assaults on IoT networks. By integrating deep learning and methodologies with an emphasis on explainability, our approach significantly enhances network security in IoT use scenarios. The ability to assess and grasp IDS options provides the path for cybersecurity experts to design and construct more secure IoT systems.

Vol. 12 Issue. 2 PP. 69-82, (2023)

Protecting Smart Home from Cybersecurity Threats Strategies for Homeowners

Miguel Botto-Tobar , Sumaiya Rehan , Ravi Prakash Verma

Cyberthreat proliferation parallels the rapid surge in smart home usage. While having everything in one place is convenient, it also increases your home's vulnerability to cyber threats. Such an attack could result in bodily harm, the theft of sensitive information, or both. To mitigate the effects of these threats, owners of smart homes can make efforts to prevent cybercriminals from breaking into their premises starting by updating their firmware to the most recent version, creating secure passwords, and enabling two-factor authentication. Second, people should safeguard their gadgets by creating unique user IDs, disabling unneeded functions, and always keeping a tight eye on them. Finally, they must safeguard the facility where they conduct business by installing surveillance equipment, employing electronic locks, and restricting network access. Individuals must take these safeguards, but they must also stay informed about the most recent threats to home cybersecurity and the best strategies to combat them. Smart home device owners should become acquainted with the risks to which their devices are prone and ensure that their devices are updated to the most recent versions of all available software and security upgrades. Collaboration between homeowners, connected device manufacturers, and internet service providers is required to ensure the security of a smart home. Homeowners should research the security features available in smart home devices and only buy from reputable businesses that value consumer privacy and security. As the Internet of Things (IoT) expands and develops, a data privacy standard that meets the criteria of Data protection is in great demand. Safeguarding smart family apps necessitates a community agreement and specific permission from users to store their personal information in the product's database.

Vol. 12 Issue. 2 PP. 83-98, (2023)

Journal of Cybersecurity and Information Management

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