In the contemporary era of rapid technological advancement, the Industrial Internet of Things (IIoT) has become a pivotal element in revolutionizing industrial operations. This paper delves into the escalating cybersecurity challenges posed by the sprawling networks of IIoT, accentuating the inadequacy of traditional cybersecurity methods in the face of sophisticated cyber threats. We introduce machine learning (ML) as a transformative approach to fortify the cybersecurity landscape of IIoT systems. Our research primarily focuses on the application of machine learning algorithms to detect, analyze, and counteract diverse cyber threats in IIoT environments. These algorithms are trained to recognize and respond to a spectrum of cyber threats, thereby enhancing the resilience of IIoT networks. We present a novel Convolutional-GRU autoencoder model, which demonstrates superior performance over traditional machine learning models in terms of accuracy, precision, recall, and F1score. This model is adept at learning and adapting from complex data patterns, ensuring robust defense against cyber intrusions. We also address the challenges in applying ML to IIoT cybersecurity, considering the varied nature of IIoT devices and the dynamic landscape of cyber threats. This study is an important stride towards enhancing IIoT cybersecurity, highlighting the symbiotic relationship between ML and IIoT. It serves as a foundation for future research and a guide for current implementations, aiming to create more secure, reliable, and efficient IIoT environments. By exploring the potential of ML in cybersecurity, we pave the way for a new era in industrial digital protection, one that is adaptable, forward-thinking, and resilient against the ever-evolving digital threats.

This research paper presents a comprehensive investigation into the development of an innovative and novel custom neural network model for intrusion detection systems (IDS). In the current era of rapid data transfer facilitated by the internet and advancements in communication technologies, the security of sensitive information is of paramount concern. As attackers continuously devise new methodologies to steal or tamper with data, IDSs face significant challenges in effectively detecting and mitigating intrusions. While extensive research has been conducted to enhance IDS capabilities, the need for improved detection accuracy and reduced false alarm rates remains a pressing issue. Moreover, the identification of zeroday attacks continues to pose a formidable obstacle. In contrast to conventional IDS approaches that heavily rely on statistical methodologies and rule-based expert systems, this study embraces data mining techniques, specifically Neural Networks (NNs), to overcome the limitations associated with large datasets. This research paper proposes a meticulously designed custom neural network model that leverages machine learning (ML) algorithms to analyze contemporary host activity and cloud service data. The paper extensively discusses the utilized dataset, meticulously evaluates the performance of various classifiers, and introduces our innovative neural network model. Emphasizing the significance of our model in anomaly detection, the findings underscore the importance of robust ML models to ensure the efficacy and longevity of deployed defensive systems. By capitalizing on its innovative design and leveraging the power of ML algorithms, our model not only addresses the limitations of traditional IDS approaches but also paves the way for enhanced accuracy, reduced false alarms, and improved resilience against zero-day attacks. This research contributes to the advancement of the field, shedding light on the novel possibilities and remarkable innovation offered by our custom neural network model in safeguarding critical information in an increasingly hostile digital landscape.

Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited from the proprietary interpretation of the dataset. Addressing comparability and reproducibility issues, we introduce Payload-Byte, an open-source tool for extracting and labeling network packets in this work. Payload-Byte utilizes metadata information and labels raw traffic captures of modern intrusion detection datasets in a generalized manner. Moreover, we transformed the labeled data into a byte-wise feature vector that can be utilized for training machine learning models. The whole cycle of processing and labeling is explicitly stated in this work. Furthermore, source code and processed data are made publicly available so that it may act as a standardized baseline for future research work. Lastly, we present a brief comparative analysis of machine learning models trained on packet-based and flow-based data. UNSW-NB15 and CIC-IDS2017.

Microgrids specialized for tactical operations  have been subjected to several challenges. These tactical power  networks are islanded and have a relatively low power  generation capacity. Meeting power requirements of military  equipment, having intermittent and highly inductive nature,  exposes microgrids to severe stresses. Existing methodologies to  monitor and control the impact of load variations require  sophisticated equipment and trained personnel. The objective of  this research paper is to present an open-source edge energy  monitoring system (EEMS) for efficient demand management of  tactical networks. The proposed system is capable of capturing all minute operational artifacts, including harmonic distortions  and power quality of these networks. A variable gain amplifier  circuit enables the proposed EMS to sense all the signals in a  wide range of power with higher resolution. The proposed  system utilizes raspberry pi as an edge device to meet the low  power requirements of tactical networks. The novel concurrent  programming approach adopted in the proposed EMS,  effectively handles the large amount of data acquired from the  network. This parallel processing of acquired data speeds up the  execution process. All electrical parameters obtained during this  process are stored in an encrypted local database that can be  utilized for fault analysis and load prediction. Further  integration of machine learning tools in proposed EMS assists in  automated power network reconfiguration and tuning under  harsh battlefield situations

Emerging Cyber threats with an increased dependency on vulnerable cyber-networks have jeopardized all stakeholders, making Intrusion Detection Systems (IDS) the essential network security requirement. Several IDS have been proposed in the past decade for preventing systems from cyber-attacks. Machine learning (ML) based IDS have shown remarkable performance on conventional cyber threats. However, the introduction of adversarial attacks in the cyber domain highlights the need to upgrade these IDS because conventional ML-based approaches are vulnerable to adversarial attacks. Therefore, the proposed IDS framework leverages the performance of conventional ML-based IDS and integrates it with Explainable AI (XAI) to deal with adversarial attacks. Global Explanation of AI model, extracted by SHAP (Shapley additive explanation) during the training phase of Primary Random Forest Classifier (RFC), is used to reassess the credibility of predicted outcomes. In other words, an outcome with low credibility is reassessed by secondary classifiers. This SHAP-based approach helps in filtering out all disguised malicious network traffic and can also enhance user trust by adding transparency to the decision-making process. Adversarial robustness of the proposed IDS was assessed by Hop Skip Jump Attack and CICIDS dataset, where IDS showed 98.5% and 100% accuracy, respectively. Furthermore, the performance of the proposed IDS is compared with conventional algorithms using recall, precision, accuracy, and F1-score as evaluation metrics. This comparative analysis and series of experiments endorse the credibility of the proposed scheme, depicting that the integration of XAI with conventional IDS can ensure credibility, integrity, and availability of cyber-networks.