Privacy-preserving Social Media Forensic Analysis for Preventive Policing of Online Activities

Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse given multimedia contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be tracked while the privacy of innocent citizens can be preserved. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used speech recognition, face and object detection besides audio event detection to extract information from multimedia files. We have applied anonymization techniques to ensure the privacy of citizens using social media. We have discussed the challenges and prospects of this work especially the need of using digital forensic techniques while respecting European and national data protection laws notably GDPR

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Towards Fully Integrated Real-time Detection Framework for Online Contents Analysis - RED-Alert Approach

Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse online contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be ethically tracked. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used social network analysis, speech recognition, face and object detection besides audio event detection to extract information from online sources that are fed in a complex event processor. We have discussed the challenges and prospects of this work especially the need of analysing online contents while respecting European and national data protection laws notably GDPR

Challenges in Cybersecurity and Privacy - the European Research Landscape

Cybersecurity and Privacy issues are becoming an important barrier for a trusted and dependable global digital society development. Cyber-criminals are continuously shifting their cyber-attacks specially against cyber-physical systems and IoT, since they present additional vulnerabilities due to their constrained capabilities, their unattended nature and the usage of potential untrustworthiness components. Likewise, identity-theft, fraud, personal data leakages, and other related cyber-crimes are continuously evolving, causing important damages and privacy problems for European citizens in both virtual and physical scenarios. In this context, new holistic approaches, methodologies, techniques and tools are needed to cope with those issues, and mitigate cyberattacks, by employing novel cyber-situational awareness frameworks, risk analysis and modeling, threat intelligent systems, cyber-threat information sharing methods, advanced big-data analysis techniques as well as exploiting the benefits from latest technologies such as SDN/NFV and Cloud systems. In addition, novel privacy-preserving techniques, and crypto-privacy mechanisms, identity and eID management systems, trust services, and recommendations are needed to protect citizens’ privacy while keeping usability levels. The European Commission is addressing the challenge through different means, including the Horizon 2020 Research and Innovation program, thereby financing innovative projects that can cope with the increasing cyberthreat landscape. This book introduces several cybersecurity and privacy research challenges and how they are being addressed in the scope of 15 European research projects. Each chapter is dedicated to a different funded European Research project, which aims to cope with digital security and privacy aspects, risks, threats and cybersecurity issues from a different perspective. Each chapter includes the project’s overviews and objectives, the particular challenges they are covering, research achievements on security and privacy, as well as the techniques, outcomes, and evaluations accomplished in the scope of the EU project. The book is the result of a collaborative effort among relative ongoing European Research projects in the field of privacy and security as well as related cybersecurity fields, and it is intended to explain how these projects meet the main cybersecurity and privacy challenges faced in Europe. Namely, the EU projects analyzed in the book are: ANASTACIA, SAINT, YAKSHA, FORTIKA, CYBECO, SISSDEN, CIPSEC, CS-AWARE. RED-Alert, Truessec.eu. ARIES, LIGHTest, CREDENTIAL, FutureTrust, LEPS. Challenges in Cybersecurity and Privacy - the European Research Landscape is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science and communications networks interested in learning about cyber-security and privacy aspects

Challenges in Cybersecurity and Privacy - the European Research Landscape

Cybersecurity and Privacy issues are becoming an important barrier for a trusted and dependable global digital society development. Cyber-criminals are continuously shifting their cyber-attacks specially against cyber-physical systems and IoT, since they present additional vulnerabilities due to their constrained capabilities, their unattended nature and the usage of potential untrustworthiness components. Likewise, identity-theft, fraud, personal data leakages, and other related cyber-crimes are continuously evolving, causing important damages and privacy problems for European citizens in both virtual and physical scenarios. In this context, new holistic approaches, methodologies, techniques and tools are needed to cope with those issues, and mitigate cyberattacks, by employing novel cyber-situational awareness frameworks, risk analysis and modeling, threat intelligent systems, cyber-threat information sharing methods, advanced big-data analysis techniques as well as exploiting the benefits from latest technologies such as SDN/NFV and Cloud systems. In addition, novel privacy-preserving techniques, and crypto-privacy mechanisms, identity and eID management systems, trust services, and recommendations are needed to protect citizens’ privacy while keeping usability levels. The European Commission is addressing the challenge through different means, including the Horizon 2020 Research and Innovation program, thereby financing innovative projects that can cope with the increasing cyberthreat landscape. This book introduces several cybersecurity and privacy research challenges and how they are being addressed in the scope of 15 European research projects. Each chapter is dedicated to a different funded European Research project, which aims to cope with digital security and privacy aspects, risks, threats and cybersecurity issues from a different perspective. Each chapter includes the project’s overviews and objectives, the particular challenges they are covering, research achievements on security and privacy, as well as the techniques, outcomes, and evaluations accomplished in the scope of the EU project. The book is the result of a collaborative effort among relative ongoing European Research projects in the field of privacy and security as well as related cybersecurity fields, and it is intended to explain how these projects meet the main cybersecurity and privacy challenges faced in Europe. Namely, the EU projects analyzed in the book are: ANASTACIA, SAINT, YAKSHA, FORTIKA, CYBECO, SISSDEN, CIPSEC, CS-AWARE. RED-Alert, Truessec.eu. ARIES, LIGHTest, CREDENTIAL, FutureTrust, LEPS. Challenges in Cybersecurity and Privacy - the European Research Landscape is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science and communications networks interested in learning about cyber-security and privacy aspects

Data ethics : building trust : how digital technologies can serve humanity

Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

Machine Learning and Security of Non-Executable Files

Computer malware is a well-known threat in security which, despite the enormous time and effort invested in fighting it, is today more prevalent than ever. Recent years have brought a surge in one particular type: malware embedded in non-executable file formats, e.g., PDF, SWF and various office file formats. The result has been a massive number of infections, owed primarily to the trust that ordinary computer users have in these file formats. In addition, their feature-richness and implementation complexity have created enormous attack surfaces in widely deployed client software, resulting in regular discoveries of new vulnerabilities. The traditional approach to malware detection – signature matching, heuristics and behavioral profiling – has from its inception been a labor-intensive manual task, always lagging one step behind the attacker. With the exponential growth of computers and networks, malware has become more diverse, wide-spread and adaptive than ever, scaling much faster than the available talent pool of human malware analysts. An automated and scalable approach is needed to fill the gap between automated malware adaptation and manual malware detection, and machine learning is emerging as a viable solution. Its branch called adversarial machine learning studies the security of machine learning algorithms and the special conditions that arise when machine learning is applied for security. This thesis is a study of adversarial machine learning in the context of static detection of malware in non-executable file formats. It evaluates the effectiveness, efficiency and security of machine learning applications in this context. To this end, it introduces 3 data-driven detection methods developed using very large, high quality datasets. PJScan detects malicious PDF files based on lexical properties of embedded JavaScript code and is the fastest method published to date. SL2013 extends its coverage to all PDF files, regardless of JavaScript presence, by analyzing the hierarchical structure of PDF logical building blocks and demonstrates excellent performance in a novel long-term realistic experiment. Finally, Hidost generalizes the hierarchical-structure-based feature set to become the first machine-learning-based malware detector operating on multiple file formats. In a comprehensive experimental evaluation on PDF and SWF, it outperforms other academic methods and commercial antivirus systems in detection effectiveness. Furthermore, the thesis presents a framework for security evaluation of machine learning classifiers in a case study performed on an independent PDF malware detector. The results show that the ability to manipulate a part of the classifier’s feature set allows a malicious adversary to disguise malware so that it appears benign to the classifier with a high success rate. The presented methods are released as open-source software.Schadsoftware ist eine gut bekannte Sicherheitsbedrohung. Trotz der enormen Zeit und des Aufwands die investiert werden, um sie zu beseitigen, ist sie heute weiter verbreitet als je zuvor. In den letzten Jahren kam es zu einem starken Anstieg von Schadsoftware, welche in nicht-ausführbaren Dateiformaten, wie PDF, SWF und diversen Office-Formaten, eingebettet ist. Die Folge war eine massive Anzahl von Infektionen, ermöglicht durch das Vertrauen, das normale Rechnerbenutzer in diese Dateiformate haben. Außerdem hat die Komplexität und Vielseitigkeit dieser Dateiformate große Angriffsflächen in weitverbreiteter Klient-Software verursacht, und neue Sicherheitslücken werden regelmäßig entdeckt. Der traditionelle Ansatz zur Erkennung von Schadsoftware – Mustererkennung, Heuristiken und Verhaltensanalyse – war vom Anfang an eine äußerst mühevolle Handarbeit, immer einen Schritt hinter den Angreifern zurück. Mit dem exponentiellen Wachstum von Rechenleistung und Netzwerkgeschwindigkeit ist Schadsoftware diverser, zahlreicher und schneller-anpassend geworden als je zuvor, doch die Verfügbarkeit von menschlichen Schadsoftware-Analysten kann nicht so schnell skalieren. Ein automatischer und skalierbarer Ansatz ist gefragt, und maschinelles Lernen tritt als eine brauchbare Lösung hervor. Ein Bereich davon, Adversarial Machine Learning, untersucht die Sicherheit von maschinellen Lernverfahren und die besonderen Verhältnisse, die bei der Anwendung von machinellem Lernen für Sicherheit entstehen. Diese Arbeit ist eine Studie von Adversarial Machine Learning im Kontext statischer Schadsoftware-Erkennung in nicht-ausführbaren Dateiformaten. Sie evaluiert die Wirksamkeit, Leistungsfähigkeit und Sicherheit von maschinellem Lernen in diesem Kontext. Zu diesem Zweck stellt sie 3 datengesteuerte Erkennungsmethoden vor, die alle auf sehr großen und diversen Datensätzen entwickelt wurden. PJScan erkennt bösartige PDF-Dateien anhand lexikalischer Eigenschaften von eingebettetem JavaScript-Code und ist die schnellste bisher veröffentliche Methode. SL2013 erweitert die Erkennung auf alle PDF-Dateien, unabhängig davon, ob sie JavaScript enthalten, indem es die hierarchische Struktur von logischen PDF-Bausteinen analysiert. Es zeigt hervorragende Leistung in einem neuen, langfristigen und realistischen Experiment. Schließlich generalisiert Hidost den auf hierarchischen Strukturen basierten Merkmalsraum und wurde zum ersten auf maschinellem Lernen basierten Schadsoftware-Erkennungssystem, das auf mehreren Dateiformaten anwendbar ist. In einer umfassenden experimentellen Evaulierung auf PDF- und SWF-Formaten schlägt es andere akademische Methoden und kommerzielle Antiviren-Lösungen bezüglich Erkennungswirksamkeit. Überdies stellt diese Doktorarbeit ein Framework für Sicherheits-Evaluierung von auf machinellem Lernen basierten Klassifikatoren vor und wendet es in einer Fallstudie auf eine unabhängige akademische Schadsoftware-Erkennungsmethode an. Die Ergebnisse zeigen, dass die Fähigkeit, nur einen Teil von Features, die ein Klasifikator verwendet, zu manipulieren, einem Angreifer ermöglicht, Schadsoftware in Dateien so einzubetten, dass sie von der Erkennungsmethode mit hoher Erfolgsrate als gutartig fehlklassifiziert wird. Die vorgestellten Methoden wurden als Open-Source-Software veröffentlicht

Security Analysis of System Behaviour - From "Security by Design" to "Security at Runtime" -

The Internet today provides the environment for novel applications and processes which may evolve way beyond pre-planned scope and purpose. Security analysis is growing in complexity with the increase in functionality, connectivity, and dynamics of current electronic business processes. Technical processes within critical infrastructures also have to cope with these developments. To tackle the complexity of the security analysis, the application of models is becoming standard practice. However, model-based support for security analysis is not only needed in pre-operational phases but also during process execution, in order to provide situational security awareness at runtime. This cumulative thesis provides three major contributions to modelling methodology. Firstly, this thesis provides an approach for model-based analysis and verification of security and safety properties in order to support fault prevention and fault removal in system design or redesign. Furthermore, some construction principles for the design of well-behaved scalable systems are given. The second topic is the analysis of the exposition of vulnerabilities in the software components of networked systems to exploitation by internal or external threats. This kind of fault forecasting allows the security assessment of alternative system configurations and security policies. Validation and deployment of security policies that minimise the attack surface can now improve fault tolerance and mitigate the impact of successful attacks. Thirdly, the approach is extended to runtime applicability. An observing system monitors an event stream from the observed system with the aim to detect faults - deviations from the specified behaviour or security compliance violations - at runtime. Furthermore, knowledge about the expected behaviour given by an operational model is used to predict faults in the near future. Building on this, a holistic security management strategy is proposed. The architecture of the observing system is described and the applicability of model-based security analysis at runtime is demonstrated utilising processes from several industrial scenarios. The results of this cumulative thesis are provided by 19 selected peer-reviewed papers