Data Mining - Concepts and Techniques.

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

/pdf/cybersecurity-data-science-an-overview-from-machine-learning-2g5zdq7ti3.pdf

Cybersecurity data science: an overview from machine learning perspective

Threats that have been primarily targeting nation states and their associated entities have expanded the target zone to include the private and corporate sectors. This class of threats, well known as advanced persistent threats (APTs), are those that every nation and well-established organization fears and wants to protect itself against. While nation-sponsored APT attacks will always be marked by their sophistication, APT attacks that have become prominent in corporate sectors do not make it any less challenging for the organizations. The rate at which the attack tools and techniques are evolving is making any existing security measures inadequate. As defenders strive to secure every endpoint and every link within their networks, attackers are finding new ways to penetrate into their target systems. With each day bringing new forms of malware, having new signatures and behavior that is close to normal, a single threat detection system would not suffice. While it requires time and patience to perform APT, solutions that adapt to the changing behavior of APT attacker(s) are required. Several works have been published on detecting an APT attack at one or two of its stages, but very limited research exists in detecting APT as a whole from reconnaissance to cleanup, as such a solution demands complex correlation and fine-grained behavior analysis of users and systems within and across networks. Through this survey paper, we intend to bring all those methods and techniques that could be used to detect different stages of APT attacks, learning methods that need to be applied and where to make your threat detection framework smart and undecipherable for those adapting APT attackers. We also present different case studies of APT attacks, different monitoring methods, and mitigation methods to be employed for fine-grained control of security of a networked system. We conclude this paper with different challenges in defending against APT and opportunities for further research, ending with a note on what we learned during our writing of this paper.

A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

The Internet of Things (IoT) aims to transform the human society toward becoming intelligent, convenient, and efficient with potentially enormous economic and environmental benefits. Reliability is one of the main challenges that must be addressed to enable this revolutionized transformation. Based on the layered IoT architecture, this article first identifies reliability challenges posed by specific enabling technologies of each layer. This article then presents a systematic synthesis and review of IoT reliability-related literature. Reliability models and solutions at four layers (perception, communication, support, and application) are reflected and classified. Despite the rich body of works performed, the IoT reliability research is still in its early stage. Challenging research problems and opportunities are then discussed in relation to current underexplored behaviors and future new aspects of evolving IoT system complexity and dynamics.

Reliability in Internet of Things: Current Status and Future Perspectives

Data exfiltration

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.

Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges.

A review of edge computing: Features and resource virtualization

Context: Big Data Cybersecurity Analytics is aimed at protecting networks, computers, and data from unauthorized access by analysing security event data using big data tools and technologies. Whilst a plethora of Big Data Cybersecurity Analytic Systems have been reported in the literature, there is a lack of a systematic and comprehensive review of the literature from an architectural perspective. Objective: This paper reports a systematic review aimed at identifying the most frequently reported quality attributes and architectural tactics for Big Data Cybersecurity Analytic Systems. Method: We used Systematic Literature Review (SLR) method for reviewing 74 primary studies selected using well-defined criteria. Results: Our findings are twofold: (i) identification of 12 most frequently reported quality attributes and the justification for their significance for Big Data Cybersecurity Analytic Systems; and (ii) identification and codification of 17 architectural tactics for addressing the quality attributes that are commonly associated with Big Data Cybersecurity Analytic systems. The identified tactics include six performance tactics, four accuracy tactics, two scalability tactics, three reliability tactics, and one security and usability tactic each. Conclusion: Our findings have revealed that (a) despite the significance of interoperability, modifiability, adaptability, generality, stealthiness, and privacy assurance, these quality attributes lack explicit architectural support in the literature (b) empirical investigation is required to evaluate the impact of codified architectural tactics (c) a good deal of research effort should be invested to explore the trade-offs and dependencies among the identified tactics and (d) there is a general lack of effective collaboration between academia and industry for supporting the field of Big Data Cybersecurity Analytic Systems.

Architectural Tactics for Big Data Cybersecurity Analytic Systems: A Review

An increasing number of large-scale distributed systems are being built by incorporating Cloud, Fog, and Edge computing. There is an important need of understanding how to ensure the resilience of systems built using Cloud, Fog, and Edge computing. This survey reports the state-of-the-art of architectural approaches that have been reported for ensuring the resilience of Cloud-, Fog- and Edge-based systems. This work reports a flexible taxonomy for reviewing architectural resilience approaches for distributed systems. In addition, this work also presents a capability-based cyber-foraging framework intended to improve the overall system resilience in the context of a physical node’s capabilities. This survey also highlights the trust-related issues and solutions in the context of system resilience and reliability. This survey will help improve the understanding of the current state of system resilience solutions and raise awareness about the issues related to physical capabilities and trust management in the context of distributed systems resilience.

/pdf/architectural-resilience-in-cloud-fog-and-edge-systems-a-an2i0qnesv.pdf

M. Ali Babar

Papers

Data exfiltration

Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges.

A review of edge computing: Features and resource virtualization

Architectural Tactics for Big Data Cybersecurity Analytic Systems: A Review

Architectural Resilience in Cloud, Fog and Edge Systems: A Survey