Abstract The Internet of things (IoT) is an important technology that is highly beneficial in establishing smart items, connections and cities. However, there are worries regarding security and privacy vulnerabilities in IoT in which some emerge from numerous sources, including cyberattacks, unsecured networks, data, connections or communication. This paper provides an ensemble intrusion strategy based on Cyborg Intelligence (machine learning and biological intelligence) framework to boost security of IoT enabled networks utilized for network traffic of smart cities. To do this, multiple algorithms such Random Forest, Bayesian network (BN), C5.0, CART and Artificial Neural Network were investigated to determine their usefulness in identifying threats and attacks-botnets in IoT networks based on cyborg intelligence using the KDDcup99 dataset. The results reveal that the AdaBoost ensemble learning based on Cyborg Intelligence Intrusion Detection framework facilitates dissimilar network characteristics with the capacity to swiftly identify different botnet assaults efficiently. The suggested framework has obtained good accuracy, detection rate and a decreased false positive rate in comparison to other standard methodologies. The conclusion of this study would be a valuable complement to the efforts toward protecting IoT-powered networks and the accomplishment of safer smart cities. 

/pdf/design-of-intrusion-detection-system-based-on-cyborg-1um0l5gc.pdf

Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities

Industrial internet of things in intelligent manufacturing: a review, approaches, opportunities, open challenges, and future directions

Green manufacturing via machine learning enabled approaches

Weld quality monitoring via machine learning-enabled approaches

Moving target defense (MTD) and decoy strategies, measures of active defense, were introduced to secure both the proactive security and reactive adaptability of internet-of-things (IoT) networks that have been explosively applied to various industries without any strong security measures and to mitigate the side effects of threats. However, the existing MTD and decoy strategies are limited to avoiding the attacker’s reconnaissance and initial intrusion attempts through simple structural mutations or inducing the attackers to a static trap based on the deceptive path and lack approaches to adaptively optimize IoT in consideration of the unique characteristic information by the domain of IoT. Game theory-based and decoy strategies are other options; however, they do not consider the dynamicity and uncertainty of the decision-making stages by the organizational agent related to the IoT domains. Therefore, in this paper, we present a type of organizational deception modeling, namely IoT-based organizational deception modeling (IoDM), which considers both the dynamic topologies and organizational business fingerprints customized in the IoT domain and operational purpose. For this model, we considered the practical scalability of the existing IoT-enabled MTD and decoy concepts and formulated the partially incomplete deceptive decision-making modeling for the cyber-attack and defense competition for IoT in real-time based on the general-sum game. According to our experimental results, the efficiency of the deceptive defense of the IoT defender could be improved by 70% on average while deriving the optimal defense cost compared to the increased defense performance. The findings of this study will improve the deception performances of MTD and decoy strategies by IoT scenarios related to various operational domains such as smart home networks, industrial networks, and medical networks. To the best of our knowledge, this study has employed social-engineering IoT knowledge and general-sum game theory for the first time.

/pdf/iodm-a-study-on-a-iot-based-organizational-deception-g6q3c9yh.pdf

IoDM: A Study on a IoT-Based Organizational Deception Modeling with Adaptive General-Sum Game Competition

With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.

/pdf/leveraging-computational-intelligence-techniques-for-21uw90f5.pdf

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government-funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard-to-reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions. 

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically extracting relevant features for forecasting. The use of deep learning models in drought forecasting can provide more accurate and timely predictions, which are crucial for the mitigation of drought-related impacts such as crop failure, water shortages, and economic losses. This review provides information on the type of droughts and their information systems. A comparative analysis of deep learning models, related technology, and research tabulation is provided. The review has identified algorithms that are more pertinent than others in the current scenario, such as the Deep Neural Network, Multi-Layer Perceptron, Convolutional Neural Networks, and combination of hybrid models. The paper also discusses the common issues for deep learning models for drought forecasting and the current open challenges. In conclusion, deep learning models offer a powerful tool for drought forecasting, which can significantly improve our understanding of drought dynamics and our ability to predict and mitigate its impacts. However, it is important to note that the success of these models is highly dependent on the availability and quality of data, as well as the specific characteristics of the drought event.

/pdf/a-contemporary-review-on-deep-learning-models-for-drought-1lxz320e.pdf

A Contemporary Review on Deep Learning Models for Drought Prediction

Tomorrow is a technology for Microbial fuel cells (MFC). It has attracted numerous studies for the continuous development of cell efficiency since the problem of the coming era can be resolved. Implementing artificial learning and machine learning is a change that can effectively achieve the goals. A microbial fuel cell is a complex non-linear procedure that preferably requires a strategy that is not a linear control strategy for the most favorable outcome. The practical and feasible ways to tackle non-linearity existing in the Microbial Fuel Cell, instead of making a computationally tedious and heavy non-linear control strategy a superior single linear model or scheduling or multiple model-oriented control techniques. Machine learning and Artificial Intelligence help reduce computation and model costs. It saves time and is more efficient than previously used manual methods, which are now obsolete. In order to find the most accurate results, the study would compare all currently available research efforts and focus on implementing Artificial Intelligence and Machine learning concepts within the Microbial Fuel Cell and comparison with other fuel cells.

A Survey of Applications of MFC and Recent Progress of Artificial Intelligence and Machine Learning Techniques and Applications, with competing fuel cells

Amogh Gyaneshwar

Papers

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Green manufacturing via machine learning enabled approaches

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

A Contemporary Review on Deep Learning Models for Drought Prediction

A Survey of Applications of MFC and Recent Progress of Artificial Intelligence and Machine Learning Techniques and Applications, with competing fuel cells