In recent years, unethical behavior in the cyber-environment has been revealed. The presence of offensive language on social media platforms and automatic detection of such language is becoming a major challenge in modern society. The complexity of natural language constructs makes this task even more challenging. Until now, most of the research has focused on resource-rich languages like English. Roman Urdu and Urdu are two scripts of writing the Urdu language on social media. The Roman script uses the English language characters while the Urdu script uses Urdu language characters. Urdu and Hindi languages are similar with the only difference in their writing script but the Roman scripts of both languages are similar. This study is about the detection of offensive language from the user's comments presented in a resourcepoor language Urdu. We propose the first offensive dataset of Urdu containing user-generated comments from social media. We use individual and combined n-grams techniques to extract features at character-level and word-level. We apply seventeen classifiers from seven machine learning techniques to detect offensive language from both Urdu and Roman Urdu text comments. Experiments show that the regression-based models using character n-grams show superior performance to process the Urdu language. Character-level tri-gram outperforms the other word and character n-grams. LogitBoost and SimpleLogistic outperform the other models and achieve 99.2% and 95.9% values of F-measure on Roman Urdu and Urdu datasets respectively. Our designed dataset is publically available on GitHub for future research.

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An Intrusion Detection System (IDS) is an essential part of the network as it contributes towards securing the network against various vulnerabilities and threats. Over the past decades, there has been a comprehensive study in the field of IDS, and various approaches have been developed to design intrusion detection and classification system. With the proliferation in the usage of Deep Learning (DL) techniques and their ability to learn data extensively, we aim to design Deep Neural Network (DNN) based IDS. In this study, we aim to focus on enhancing the performance of DNN-based IDS in Cyber-Physical Systems (CPS). CPS combine physical processes, networking, and computation. The integration of CPS components could seriously jeopardise the security of CPS settings because of the physical limitations. The vulnerability of CPS to cyberattacks has grown with the development of IoT and other physical systems. As cyber-physical systems refer to the intersection of your organization's technology and IT infrastructure and its physical assets, ensuring access and data security through advanced methods prevents any cyber-attack from damaging your physical assets and thereby disrupting your business flow. Conventional cyber and network security procedures fail to guarantee data privacy and security in CPS contexts. This research aims to provide a cutting-edge attack detection method based on learning for CPS environments. The paper suggests using MLP-based smart attack control systems to increase the CPSs' security. Performance analysis is presented in terms of different evaluation metrics such as accuracy, precision, recall, f-score, and False Positive Rate (FPR), and the results are compared with existing feature selection techniques. The effectiveness of the suggested model was confirmed by comparing the outcomes with those of other successful deep learning-based algorithms, including the Gaussian Naive Bayes algorithm, SVM, and logistic regression. Comparative results demonstrate that the suggested method outperforms existing learning models with an exceptional accuracy of 99.52%.

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