Sulphate-reducing bacteria (SRB) are dominant species causing corrosion of various types of materials. However, they also play a beneficial role in bioremediation due to their tolerance of extreme pH conditions. The application of sulphate-reducing bacteria (SRB) in bioremediation and control methods for microbiologically influenced corrosion (MIC) in extreme pH environments requires an understanding of the microbial activities in these conditions. Recent studies have found that in order to survive and grow in high alkaline/acidic condition, SRB have developed several strategies to combat the environmental challenges. The strategies mainly include maintaining pH homeostasis in the cytoplasm and adjusting metabolic activities leading to changes in environmental pH. The change in pH of the environment and microbial activities in such conditions can have a significant impact on the microbial corrosion of materials. These bacteria strategies to combat extreme pH environments and their effect on microbial corrosion are presented and discussed.

Sulfate-reducing bacteria (SRB) can regulate environmental pH because of their metabolism. Because local acidification results in pitting corrosion, the potential capacity of pH regulation by SRB would have important consequences for electrochemical aspects of the bio-corrosion process. This study focused on identifying the effect of pH on the corrosion of duplex stainless steel 2205 in a nutrient-rich artificial seawater medium containing SRB species, Desulfovibrio vulgaris . Duplex stainless steel samples were exposed to the medium for 13 days at 37°C at pH ranging from 4.0 to 7.4. The open-circuit potential value, sulfide level, pH and number of bacteria in the medium were recorded daily. Electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization were used to study the properties of the biofilms at the end of the experiments and the corrosion behaviour of the material. Inductively coupled plasma mass spectrometry was used to measure the concentration of cations Fe, Ni, Mo, Mn, Cr in the experimental solution after 13 days. Scanning electron microscopy and energy-dispersive X-ray spectroscopy (EDX) were used for surface analysis. The results showed the pH changed from acidic values set at the beginning of the experiment to approximately pH 7.5 after 5 days owing to bacterial metabolism. After 13 days, the highest iron concentration was in the solution that was initially at pH 4 accompanied by pitting on the stainless steel. Sulfur was present on all specimens but with more sulfur at pH 4 in the EDX spectra. EIS showed the film resistance of the specimen at pH 4 was much lower than at pH 7.4 which suggests the corrosion resistance of the stainless steel was better at higher pH. The results of this study suggest that the corrosion process for the first few days exposure at low pH was driven by pH in solution rather than by bacteria. The increasing pH during the course of the experiment slowed down the corrosion process of materials originally at low pH. The nature and mechanism of SRB attack on duplex stainless steel at different acidic environments are discussed.

This comprehensive systematic review examines the integration of software-defined networking (SDN) with IoT security frameworks, analyzing recent advancements in encryption, authentication, access control techniques, and intrusion detection systems. Our analysis reveals that while SDN demonstrates promising capabilities in enhancing IoT security through centralized control and dynamic policy enforcement, several critical limitations persist, particularly in scalability and real-world validation. As intrusion detection represents an integral security requirement for robust IoT frameworks, we conduct an in-depth evaluation of Machine Learning (ML) and Deep Learning (DL) techniques that have emerged as predominant approaches for threat detection in SDN-enabled IoT environments. The review categorizes and analyzes these ML/DL implementations across various architectural paradigms, identifying patterns in their effectiveness for different security contexts. Furthermore, recognizing that the performance of these ML/DL models critically depends on training data quality, we evaluate existing IoT security datasets, identifying significant gaps in representing contemporary attack vectors and realistic IoT environments. A key finding indicates that hybrid architectures integrating cloud–edge–fog computing demonstrate superior performance in distributing security workloads compared to single-tier implementations. Based on this systematic analysis, we propose key future research directions, including adaptive zero-trust architectures, federated machine learning for distributed security, and comprehensive dataset creation methodologies, that address current limitations in IoT security research.

The increasing complexity and adversity of cyber-attacks have prompted discussions in the cyber scenario for a prognosticate approach, rather than a reactionary one. In this paper, a signature-based intrusion detection system has been built based on C5 classifiers, to classify packets into normal and attack categories. Next, an anomaly-based intrusion detection was built based on the LSTM (Long-Short Term Memory) algorithm to detect anomalies. These anomalies are then fed into the signature generator to extract attributes. These attributes get uploaded into the C5 training set, aiding the ensemble model in continual learning with expanding signatures of unknown attacks. By generating signatures of unknown attacks, the self-healing attribute of the ensemble model contributes to the early detection of attacks. For the C5 classifier, the proposed model is evaluated on the UNSW-NB15 dataset, while for the LSTM model, it is evaluated on the ADFA-LD dataset. Compared to conventional models, the experimental results show better detection rates for both known and unknown attacks. The C5 classifier achieved a True Positive Rate of 97% while maintaining a false positive rate of 8%. Also, the LSTM model achieved a detection rate of 90% while retaining a 17% False Alarm Rate. As the proposed model learns, its performance in real network traffic also improves and it also eliminates human intervention when updating training data.

Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is being applied to. There is no universally best clustering algorithm, and hence, there are numerous clustering algorithms available with different performance characteristics. This raises the problem of how to select an appropriate clustering algorithm for the given analytical purposes. We present and validate an analysis framework to address this problem. Unlike most current literature which focuses on characterizing the clustering algorithm itself, we present a wider holistic approach, with a focus on the user’s needs, the data’s characteristics and the characteristics of the clusters it may contain. In our analysis framework, we utilize a softer qualitative approach to identify appropriate characteristics for consideration when matching clustering algorithms to the intended application. These are used to generate a small subset of suitable clustering algorithms whose performance are then evaluated utilizing quantitative cluster validity indices. To validate our analysis framework for selecting clustering algorithms, we applied it to four different types of datasets: three datasets of homemade explosives spectroscopy, eight datasets of publicly available spectroscopy data covering food and biomedical applications, a gene expression cancer dataset, and three classic machine learning datasets. Each data type has discernible differences in the composition of the data and the context within which they are used. Our analysis framework, when applied to each of these challenges, recommended differing subsets of clustering algorithms for final quantitative performance evaluation. For each application, the recommended clustering algorithms were confirmed to contain the top performing algorithms through quantitative performance indices.