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\"Take your Office 365 skills to the next level. Master PowerShell for Office 365 to stay competitive in today's world of highly sought after cloud management skills. With expert guidance, IT pros will learn how to leverage the muscle of PowerShell to automate many advanced administrative tasks not otherwise accessible in the Office 365 Admin Center. You will discover how to unlock configuration options and automate tasks in order to free up valuable time and resources.This book is your companion to administering Office 365 with PowerShell. You will learn time-saving techniques such as how to streamline administrative tasks, and how to manage users, licenses, and Office 365 services. Expert and MVP Vlad Catrinescu introduces each chapter with an overview and basic fundamentals, such as how to connect to your required service in Office 365, so that you have a solid foundation for success. Benefit from learning the theory behind PowerShell for Office 365 and put your knowledge to practice with numerous hands-on code examples.What You'll LearnManage users in bulkExport data such as user lists and groupsCreate and manage Office 365 groupsManage Exchange online distribution lists, mailboxes, and contactsConfigure Skype for Business settingsPerform compliance searches directly from PowerShellWho This Book Is For Any IT pro who needs to manage Office 365 or one of its services such as Exchange, SharePoint, or Skype for Business. Readers should have a basic knowledge of PowerShell and the Office 365 service they want to manage.\"--Publisher's description.

We present the probabilistic model checker Storm . Storm supports the analysis of discrete- and continuous-time variants of both Markov chains and Markov decision processes. Storm has three major distinguishing features. It supports multiple input languages for Markov models, including the Jani and Prism modeling languages, dynamic fault trees, generalized stochastic Petri nets, and the probabilistic guarded command language. It has a modular setup in which solvers and symbolic engines can easily be exchanged. Its Python API allows for rapid prototyping by encapsulating Storm ’s fast and scalable algorithms. This paper reports on the main features of Storm and explains how to effectively use them. A description is provided of the main distinguishing functionalities of Storm . Finally, an empirical evaluation of different configurations of Storm on the QComp 2019 benchmark set is presented.

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share applications related to data analysis and visualization.

In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing (NLP). Human beings are asked to paraphrase commands and questions for task identification for further execution of algorithms as skills. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. “Robot, can we have a conversation?”) and allows for better accessibility to AI by non-technical users.

The cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with detailed threat attack procedures and indicators of compromise (IOCs), e.g., malware hash or URL (uniform resource locator) of command and control server. The actionable CTI, integrated into intrusion detection systems, can not only prioritize the most urgent threats based on the campaign stages of attack vectors (i.e., IOCs) but also take appropriate mitigation measures based on contextual information of the alerts. However, the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence. In this paper, we propose a trigger-enhanced actionable CTI discovery system (TriCTI) to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing (NLP) technology. Specifically, we introduce the “campaign trigger” for an effective explanation of the campaign stages to improve the performance of the classification model. The campaign trigger phrases are the keywords in the sentence that imply the campaign stage. The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords. We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets. Compared with state-of-the-art text classification models, such as BERT, the trigger-enhanced classification model has better performance with accuracy (86.99%) and F1 score (87.02%). We run TriCTI on more than 29k cybersecurity reports, from which we automatically and efficiently collect 113,543 actionable CTI. In particular, we verify the actionability of discovered CTI by using large-scale field data from VirusTotal (VT). The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs, such as the Actions on Objectives campaign stage. As a comparison, our proposed method can completely identify the actionable CTI in all campaign stages. Accordingly, cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI.

In recent years, Living off the Land (LotL) attacks have been drawing attention due to their flexibility and difficulty in detection. These attacks exploit legitimate tools already in the system to conduct malicious activities, hiding their malicious intent behind normal benign programs. However, detection methods for such attacks largely rely on expert rules. While rule tags can effectively detect known attacks, this also leads to a high false positive rate, resulting in low detection accuracy for the models. To address these issues, we propose a detection system called LOTLDetector, which combines deep learning methods with expert rules to detect malicious command lines in LotL attacks from both data and knowledge perspectives. LOTLDetector learns the semantics of command line text through neural networks and combines rule tags from expert knowledge, enabling a more comprehensive detection of LotL attacks. We extensively evaluated our method, validated it on a Windows dataset containing 27,448 command lines and a Linux dataset containing 27,093 command lines, and compared it with existing methods. The results show that our method significantly outperforms existing methods in detecting malicious command lines. For the Linux dataset, the detection system achieved a detection performance with an accuracy of 0.9728; for the Windows dataset, the system’s detection accuracy also reached 0.9598, which is about 8% higher than the best existing method. In addition, our project has been open-sourced at https://github.com/csedikaf/LOTLDetector .

Command and control (C2) servers are used by attackers to operate communications. To perform attacks, attackers usually employee the Domain Generation Algorithm (DGA), with which to confirm rendezvous points to their C2 servers by generating various network locations. The detection of DGA domain names is one of the important technologies for command and control communication detection. Considering the randomness of the DGA domain names, recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names. However, these methods are insufficient to handle wordlist-based DGA threats, which generate domain names by randomly concatenating dictionary words according to a special set of rules. In this paper, we proposed a a deep learning framework ATT-CNN-BiLSTM for identifying and detecting DGA domains to alleviate the threat. Firstly, the Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (BiLSTM) neural network layer was used to extract the features of the domain sequences information; secondly, the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names. Finally, the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification. Our extensive experimental results demonstrate the effectiveness of the proposed model, both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones. To be precise,we got a F1 score of 98.79% for the detection and macro average precision and recall of 83% for the classification task of DGA domain names.

Linux has been one of the widely adopted and popular OS when it comes to leveraging scripting and automating common tasks. With this book, readers will get to grips with shell scripting, automating repetitive tasks, text processing, regular expressions, pattern matching, backup and restore, and much more. The end goal of this book is to get.

The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive speech recognition system. Our system aims to enhance the driving experience to make it comfortable and safe while assisting individuals with disabilities. We created a speech dataset comprising 20 commonly used car commands. It consists of 5600 instances collected from Moroccan contributors and recorded in clean and noisy environments to increase its representativity. We used MFCC, weighted MFCC, and Spectral Subband Centroids (SSC) for feature extraction, as they demonstrated promising results in noisy settings. For classifier construction, we proposed a hybrid architecture, consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN). Training our proposed model with WMFCC and SSC features achieved an accuracy of 98.48%, outperforming all baseline models we trained and outperforming the existing solutions in the state-of-the-art literature. Moreover, it shows promising results in a clean and noisy environment and maintains resilience to additive Gaussian noise while using few computational resources.

We explore the design and implementation of Frank, a strict functional programming language with a bidirectional effect type system designed from the ground up around a novel variant of Plotkin and Pretnar’s effect handler abstraction. Effect handlers provide an abstraction for modular effectful programming: a handler acts as an interpreter for a collection of commands whose interfaces are statically tracked by the type system. However, Frank eliminates the need for an additional effect handling construct by generalising the basic mechanism of functional abstraction itself. A function is but the special case of a Frank operator that interprets no commands. Moreover, Frank’s operators can be multihandlers which simultaneously interpret commands from several sources at once, without disturbing the direct style of functional programming with values. Effect typing in Frank employs a novel form of effect polymorphism which avoids mentioning effect variables in source code. This is achieved by propagating an ambient ability inwards, rather than accumulating unions of potential effects outwards. With the ambient ability describing the effects that are available at a certain point in the code, it can become necessary to reconfigure access to the ambient ability. A primary goal is to be able to encapsulate internal effects, eliminating a phenomenon we call effect pollution. Moreover, it is sometimes desirable to rewire the effect flow between effectful library components. We propose adaptors as a means for supporting both effect encapsulation and more general rewiring. Programming with effects and handlers is in its infancy. We contribute an exploration of future possibilities, particularly in combination with other forms of rich type systems.