Optimizing cybersecurity incident response decisions using deep reinforcement learning

The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis

Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this work has been based on shallow machine learning, with little attention given to deep learning approaches. Furthermore, the deep learning models used for ASA have been based on noncontextualized embedding schemes that negatively impact model performances. This article proposes a novel deep learning-based multilevel parallel attention neural (MPAN) model that uses a simple positioning binary embedding scheme (PBES) to simultaneously compute contextualized embeddings at the character, word, and sentence levels. The MPAN model then computes multilevel attention vectors and concatenates them at the output level to produce competitive accuracies. Specifically, the MPAN model produces state-of-the-art results that outperform all established ASA baselines using 34 publicly available ASA datasets. The proposed model is further shown to produce new state-of-the-art accuracies for two multidomain collections: 95.61% for a binary classification collection and 94.25% for a tertiary classification collection. Finally, the performance of the MPAN model is further validated using the public IMDB movie review dataset, on which it produces an accuracy of 96.13%, placing it in second position on the global IMDB leaderboard

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset

Optimization of Multidimensional Energy Security: An Index Based Assessment

This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run

Optimization of Multidimensional Energy Security: An Index Based Assessment

This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run

The prevalence of prostate cancer in Pakistan: A systematic review and meta-analysis

Background: Prostate cancer is a significant public health issue, ranking as the second most common cancer and the fifth leading cause of cancer-related deaths in men. In Pakistan, the prevalence of prostate cancer varies significantly across published articles. This study aimed to determine the pooled prevalence of prostate cancer and its associated risk factors in Pakistan. Methods: MEDLINE (via PubMed), Web of Science, Google Scholar, and local databases were searched from inception until March 2023, using key search terms related to the prevalence of prostate cancer. We considered a random-effects meta-analysis to derive the pooled prevalence and relative risks with 95% CIs. Two investigators independently screened articles and performed data extraction and risk of bias analysis. We also conducted meta-regression analysis and stratification to investigate heterogeneity. This study protocol was registered at PROSPERO, number CRD42022376061. Results: Our meta-analysis incorporated 11 articles with a total sample size of 184,384. The overall pooled prevalence of prostate cancer was 5.20% (95% CI: 3.72–6.90%), with substantial heterogeneity among estimates (I2 = 98.5%). The 95% prediction interval of prostate cancer was ranged from 1.74%–10.35%. Subgroup meta-analysis revealed that the highest pooled prevalence of prostate cancer was in Khyber Pakhtunkhwa (8.29%; 95% CI: 6.13–10.74%, n = 1), followed by Punjab (8.09%; 95% CI:7.36–8.86%, n = 3), while the lowest was found in Sindh (3.30%; 95% CI: 2.37–4.38%, n = 5). From 2000 to 2010 to 2011–2023, the prevalence of prostate cancer increased significantly from 3.88% (95% CI: 2.72–5.23%) to 5.80% (95% CI: 3.76–8.24%). Conclusions: Our meta-analysis provides essential insights into the prevalence of prostate cancer in Pakistan, highlighting the need for continued research and interventions to address this pressing health issue

An Empirical Analysis of Sustainable Energy Security for Energy Policy Recommendations

This study presents a framework for assessing Pakistan’s sustainable energy security (SES) between 1991 and 2020 by estimating its composite index, termed “SESi”, and three sub-indices. The SES has three dimensions: economic, social, and environmental. A total of 26 indicators were chosen and normalized using the Z-score approach before being weighted using principal component analysis (PCA) or equal weighting. The findings associated with the indices point to a declining tendency between 1991 and 2020. The highest degree of sustainable energy security (SES) was reported in 1991, with the lowest levels recorded in 2004 and 2007. Between 1991 and 2020, 9% of SESi regressed. Economic dimensions regressed among the dimension indices between 1991 and 2004, followed by steady performance, while the other two dimensions, social and environmental, fell by 30% and 26%, respectively, during the study period. Further analysis indicates that the objectives of the policies implemented throughout the study period were only partially achieved due to the country’s heavy import dependence, energy expenditures, falling reserves and forest area, and inefficiencies in the power sector

Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on 2×2, 3×3, and 4×4 MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on 3×3 and 4×4 MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time