Suchergebnisse

1. Optimizing Multi-class Classification of Binaries Based on Static Features -- 2.Detecting Abusive Comments Using Ensemble Deep Learning Algorithms -- 3. Deep Learning Techniques for Behavioural Malware Analysis in Cloud IaaS -- 4. Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges -- 5. A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysis -- 6. A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification -- 7. Word Embedding Techniques for Malware Evolution Detection -- 8. Reanimating Historic Malware Samples -- 9. DURLD: Malicious URL detection using Deep learning based Character-level representations -- 10. Sentiment Analysis for Troll Detection on Weibo -- 11. Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families -- 12. Review of the Malware Categorization in the Era of Changing Cybethreats Landscape: Common Approaches, Challenges and Future Needs -- 13. An Empirical Analysis of Image-Based Learning Techniques for Malware Classification -- 14. A Survey of Intelligent Techniques for Android Malware Detection -- 15. Malware Detection with Sequence-Based Machine Learning and Deep Learning -- 16. A Novel Study on Multinomial Classification of x86/x64 Linux ELF Malware Types and Families through Deep Neural Networks -- 17. Cluster Analysis of Malware Family Relationships -- 18. Log-Based Malicious Activity Detection using Machine and Deep Learning -- 19. Deep Learning in Malware Identification and Classification -- 20. Image Spam Classification with Deep Neural Networks -- 21. Fast and Straightforward Feature Selection Method -- 22. On Ensemble Learning -- 23. A Comparative Study of Adversarial Attacks to Malware Detectors Based on Deep Learning -- 24. Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivability -- 25. Universal Adversarial Perturbations and Image Spam Classifiers.

Intro -- Preface -- Contents -- About the Editors -- Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments -- 1 Introduction -- 2 Review of Related Works -- 2.1 High Utility Itemset Mining (HUIM) -- 2.2 High Average-Utility Itemset Mining -- 2.3 Multi-threshold Pattern Mining Works -- 3 Background of HAUIM and Problem Statement -- 4 Designed Model and Pruning Stratrgies -- 4.1 Developed Closure Property -- 4.2 Proposed Multi-HAUIM Model -- 4.3 Designed Strategy 1 -- 4.4 Designed Strategy 2 -- 5 Experimental Evaluation -- 5.1 Runtime Evaluation -- 5.2 Evaluation of Candidate Size -- 5.3 Evaluation of the Used Memory -- 5.4 Evaluation of Scalability -- 6 Conclusion and Future Work -- References -- Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities -- 1 Introduction -- 1.1 Smart City Applications -- 1.2 Technologies Used in Smart Cities and Integrated Technology in the Smart City-Edge/Cloud -- 1.3 Security Loophole in Smart Cities -- 1.4 AI/ML Based Counter Measures -- 1.5 Open Issues, Challenges and Recommendation -- 1.6 Conclusion and Future Scope -- References -- Smart Cities Ecosystem in the Modern Digital Age: An Introduction -- 1 Introduction -- 2 Smart Cities Concepts -- 3 Smart Cities Applications -- 4 Importance of Big Data for Smart Cities -- 5 Blockchain for Smart Cities -- 6 Machine Learning for Smart Cities -- 7 Discussion -- 7.1 Challenges on the Implementation of Smart City -- 8 Trends and Future Directions -- 9 Conclusions -- References -- A Reliable Cloud Assisted IoT Application in Smart Cities -- 1 Introduction -- 2 Literature Survey -- 3 Previous Work -- 4 Proposed Architecture -- 5 Analysis of the Contribution -- 6 Future Work -- 7 Conclusion -- References -- Lightweight Security Protocols for Securing IoT Devices in Smart Cities.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- About the Authors -- Symbols -- 1. Internet and Android OS -- 1.1. Android OS -- 1.1.1. Linux kernel -- 1.1.2. Native libraries -- 1.1.3. Android runtime -- 1.1.4. Application framework -- 1.1.5. Application layer -- 1.2. Android Application Development -- 1.3. Google Playstore -- 1.4. Intents and Intent Filters -- 1.5. Android Security -- 1.5.1. Permissions -- 1.5.2. Application sandbox -- 1.5.3. Application signature -- 1.5.4. Data encryption -- 1.6. Internet of Things -- 1.6.1. Architecture of IoT -- 1.6.1.1. Sensor layer -- 1.6.1.2. Gateways and networks -- 1.6.1.3. Management service layer -- 1.6.1.4. Application layer -- 1.7. Android Things -- 1.8. IoT Security -- 1.8.1. Malware Threats in IoT -- 1.9. Conclusion -- 2. Android Malware -- 2.1. PC Malware vs. Android Malware -- 2.2. Trends in Malware -- 2.2.1. Trends in Windows malware -- 2.2.2. Trends in Android malware -- 2.3. Types of Malware Detection Mechanisms -- 2.4. Malware Types -- 2.5. Malware Attacks in Android -- 2.5.1. Drive by download attack -- 2.5.2. Update attack -- 2.5.3. Repacking attack -- 2.6. History of Malware Attacks in Android -- 2.7. Conclusion -- 3. Static Malware Detection -- 3.1. Reverse Engineering and Static Analysis -- 3.1.1. Reverse engineering using Apktool and Dex2jar -- 3.1.2. Static malware analysis tools -- 3.2. Components of Android Application -- 3.3. API Call Analysis -- 3.3.1. API's used by malware applications -- 3.4. API Call-Based Static Detection -- 3.4.1. Mechanisms using the independent occurrence of API -- 3.4.2. Mechanisms Using API Call Graphs -- 3.5. Permission and Intent-Based Static Detection -- 3.5.1. Permission analysis -- 3.5.1.1. Permissions used by the malware applications.

The point of departure is the Swedish rebellion against the regime of King John (Hans) in 1501. Sten Sture the Elder was the rebellion's most prominent leader. The article moves from a discussion of Sten's character and motives for his policy to a more general discussion of the motives of the late medieval Scandinavian aristocracy's political agitation and conduct. The principal question is whether the aristocrats were motived by economic profit and personal career alone, or if other motives, like political and ideological ones, also mattered. Several examples of aristocrats' political choices that cannot have been motivated by economic gains are examined. Thereafter, the article presents the main features of the late medieval aristocracy in Scandinavia as an elite, including its political position, and then especially the balance of power between the aristocracy and the monarch. The conclusion is that the late medieval Scandinavian aristocracy's political behaviour was motivated by a set of motives, that could differ from one situation to another. ; acceptedVersion ; © 2019. This is the authors' accepted and refereed manuscript to the article. Locked until 11.11.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Intro -- Preface -- Contents -- About the Editors -- Data Quality Evaluation, Outlier Detection and Missing Data Imputation Methods for IoT in Smart Cities -- 1 Introduction -- 2 Related Work -- 3 IoT Testbed -- 4 Data Quality Evaluation and Pre-processing -- 5 Outlier Detection -- 6 Missing Data Imputation -- 7 Discussion and Conclusions -- References -- Comparison of the Bias and Weighting of Variables in Neural Networks (ANN) for the Selection of the Type of Housing in Spain and Mexico -- 1 Introduction -- 2 Methodology -- 2.1 Artificial Neural Networks -- 2.2 Multilayer Perceptron (MLP) -- 3 Model -- 3.1 Analysis of the Information and Compilation of the Databases -- 3.2 Database Set Up and Selection of Variables to Compare -- 3.3 Artificial Neural Network Design -- 3.4 Features ANNs -- 3.5 Evaluation of the Results Obtained -- 3.6 Discussion -- 4 Conclusions -- References -- Artificial Olfaction for Detection and Classification of Gases Using e-Nose and Machine Learning for Industrial Application -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 The Experimental Setup -- 4.1 The Electronic Nose and Threshold Criterion for Gas Sensors -- 4.2 The Actual Setup and Data Collection Task -- 5 Experiment Results and Discussion -- 5.1 Discussion -- 6 Conclusion -- References -- Role of Machine Learning in Weather Related Event Predictions for a Smart City -- 1 Introduction -- 2 Problem Statement -- 3 Related Work -- 4 Methodology -- 4.1 Dataset and Feature Selection -- 4.2 Selection of Algorithm -- 4.3 Implementation -- 5 Results and Discussion -- 6 Conclusion and Future Work -- References -- Intelligent Vehicle Communications Technology for the Development of Smart Cities -- 1 Introduction -- 2 A Brief History -- 3 IVCs Characteristics, Application Challenges and Constraints -- 4 Main Standardization Activities -- 4.1 DSRC.

Telephony technologies (mobile, VoIP, and fixed) have potentially improved the way we communicate in our daily life and have been widely adopted for business and personal communications. At the same time, scammers, criminals, and fraudsters have also find the telephony network an attractive and affordable medium to target end-users with the advertisement, marketing of legal and illegal products, and bombard them with the huge volume of unwanted calls. These calls would not only trick call recipients into disclosing their private information such as credit card numbers, PIN code which can be used for financial fraud but also causes a lot of displeasure because of continuous ringing. The fraudsters, political campaigners can also use telephony systems to spread malicious information (hate political or religious messages) in real-time through audio or text messages, which have serious political and social consequences if malicious callers are not mitigated in a quick time. In this context, the identification of malicious callers would not only minimize telephony fraud but would also bring peace to the lives of individuals. One way to classifies users as a spammer or legitimate is to get feedback from the call recipients about their recent interactions with the caller, but these systems not only bring inconvenience to callees but also require changes in the system design. The call detail records extensively log the activities of users and can be used to categorize them as the spammer and non-spammer. In this paper, we utilize the information from the call detailed records and proposed a spam detection framework for the telephone network that identifies malicious callers by utilizing the social behavioral features of users within the network. To this extent, we first model the behavior of the users as the directed social graph and then analyze different features of the social graph i.e. the Relationship Network and Call patterns of users towards their peers. We then used these features along with the decision tree to classify callers into three classes i.e. human, spammer and call center. We analyzed the call record data-set consisting of more than 2 million users. We have conducted a detailed evaluation of our framework which demonstrates its effectiveness by achieving acceptable detection accuracy and extremely low false-positive rate. The performance results show that the spammers and call center numbers not only have a large number of non-repetitive calls but also have a large number of short duration calls. Similarly, on the other hand, the legitimate callers have a good number of repetitive calls and most of them interacted for a relatively long duration.