Machine learning algorithms provide the ability to quickly adapt and find patterns in large diverse data sources and therefore are a potential asset to application developers in enterprise systems, networks, and security domains. They make analyzing the security implications of these tools a critical task for machine learning researchers and practitioners alike, spawning a new subfield of research into adversarial learning for security-sensitive domains. The work presented in this book advanced the state of the art in this field of study with five primary contributions: a taxonomy for qualifying the security vulnerabilities of a learner, two novel practical attack/defense scenarios for learning in real-world settings, learning algorithms with theoretical guarantees on training-data privacy preservation, and a generalization of a theoretical paradigm for evading detection of a classifier. However, research in adversarial machine learning has only begun to address the field's complex obstacles—many challenges remain. These challenges suggest several new directions for research within both fields of machine learning and computer security. In this chapter we review our contributions and list a number of open problems in the area. Above all, we investigated both the practical and theoretical aspects of applying machine learning in security domains. To understand potential threats, we analyzed the vulnerability of learning systems to adversarial malfeasance. We studied both attacks designed to optimally affect the learning system and attacks constrained by real-world limitations on the adversary's capabilities and information.We further designed defense strategies, which we showed significantly diminish the effect of these attacks. Our research focused on learning tasks in virus, spam, and network anomaly detection, but also is broadly applicable across many systems and security domains and has farreaching implications to any system that incorporates learning. Here is a summary of the contributions of each component of this book followed by a discussion of open problems and future directions for research. Framework for Secure Learning The first contribution discussed in this book was a framework for assessing risks to a learner within a particular security context (see Table 3.1). The basis for this work is a taxonomy of the characteristics of potential attacks. From this taxonomy (summarized in Table 9.1), we developed security games between an attacker and defender tailored to the particular type of threat posed by the attacker.

In this chapter we introduce a framework for qualitatively assessing the security of machine learning systems that captures a broad set of security characteristics common to a number of related adversarial learning settings. There has been a rich set of work that examines the security of machine learning systems; here we survey prior studies of learning in adversarial environments, attacks against learning systems, and proposals for making systems secure against attacks. We identify different classes of attacks on machine learning systems (Section 3.3), categorizing a threat in terms of three crucial properties. We also present secure learning as a game between an attacker and a defender — the taxonomy determines the structure of the game and its cost model. Further, this taxonomy provides a basis for evaluating the resilience of the systems described by analyzing threats against them to construct defenses. The development of defensive learning techniques is more tentative, but we also discuss a variety of techniques that show promise for defending against different types of attacks. The work we present not only provides a common language for thinking and writing about secure learning, but goes beyond that to show how the framework applies to both algorithm design and the evaluation of real-world systems. Not only does the framework elicit common themes in otherwise disparate domains but it has also motivated our study of practical machine learning systems as presented in Chapters 5, 6, and 8. These foundational principles for characterizing attacks against learning systems are an essential first step if secure machine learning is to reach its potential as a tool for use in real systems in security-sensitive domains. This chapter builds on earlier research (Barreno, Nelson, Sears, Joseph, & Tygar 2006; Barreno, Nelson, Joseph, & Tygar 2010; Barreno 2008). Analyzing the Phases of Learning Attacks can occur at each of the phases of the learning process that were outlined in Section 2.2. Figure 2.1(a) depicts how data flows through each phase of learning. We briefly outline how attacks against these phases differ. The Measuring Phase With knowledge of the measurement process, an adversary can design malicious instances to mimic the measurements of innocuous data. After a successful attack against the measurement mechanism, the system may require expensive reinstrumentation or redesign to accomplish its task.

Adversaries can also execute attacks designed to degrade the classifier's ability to distinguish between allowed and disallowed events. These Causative Availability attacks against learning algorithms cause the resulting classifiers to have unacceptably high false-positive rates; i.e., a successfully poisoned classifier will misclassify benign input as potential attacks, creating an unacceptable level of interruption in legitimate activity. This chapter provides a case study of one such attack on the SpamBayes spam detection system. We show that cleverly crafted attack messages—pernicious spam email that an uninformed human user would likely identify and label as spam—can exploit Spam- Bayes' learning algorithm, causing the learned classifier to have an unreasonably high false-positive rate. (Chapter 6 demonstrates Causative attacks that instead result in classifiers with an unreasonably high false-negative rate—these are Integrity attacks.) We also show effective defenses against these attacks and discuss the tradeoffs required to defend against them.

The ICDCIT 2020 proceedings are dealing with distributed computing and internet technology focusing on concurrent and distributed systems modelling and verification; cloud and grid computing; social networks, machine learning and mobile networks; data processing and blockchain technology.

In this chapter, we explore a theoretical model for quantifying the difficulty of Exploratory attacks against a trained classifier. Unlike the previous work, since the classifier has already been trained, the adversary can no longer exploit vulnerabilities in the learning algorithm to mistrain the classifier as we demonstrated in the first part of this book. Instead, the adversary must exploit vulnerabilities that the classifier accidentally acquired from training on benign data (or at least data not controlled by the adversary in question). Most nontrivial classification tasks will lead to some form of vulnerability in the classifier. All known detection techniques are susceptible to blind spots (i.e., classes of miscreant activity that fail to be detected), but simply knowing that they exist is insufficient. The principal question is how difficult it is for an adversary to discover a blind spot that is most advantageous for the adversary. In this chapter, we explore a framework for quantifying how difficult it is for the adversary to search for this type of vulnerability in a classifier.

This paper proposes a correct-by-construction method to build realizable choreographies described using conversation protocols (CPs). We define a new language consisting of an operators set for incremental construction of CPs. We suggest an asynchronous model described with the Event-B method and its refinement strategy, ensuring the scalability of our approach.

Asian rice is a staple food in Indonesia and worldwide, and its production is essential to food security. Cataloging and linking genetic variation in Asian rice to important traits, such as quality and yield, is needed in developing superior varieties of rice. We develop a bioinformatics workflow for quality control and data analysis of genetic and trait data for a diversity panel of 467 rice varieties found in Indonesia. The bioinformatics workflow operates using a back-end relational database for data storage and retrieval. Quality control and data analysis procedures are implemented and automated using the whole genome data analysis toolset, PLINK, and the [R] statistical computing language. The 467 rice varieties were genotyped using a custom array (717,312 genotypes total) and phenotyped for 12 traits in four locations in Indonesia across multiple seasons. We applied our bioinformatics workflow to these data and present prototype genome-wide association results for a continuous trait days to flowering. Two genetic variants, located on chromosome 4 and 12 of the rice genome, showed evidence for association in these data. We conclude by outlining extensions to the workflow and plans for more sophisticated statistical analyses.

The growing number of information security breaches in organisations presents a serious risk to the confidentiality of personal and commercially sensitive data. Current research studies indicate that humans are the weakest link in the information security chain and the root cause of numerous security incidents in organisations. Based on literature gaps, this study investigates how procedural security countermeasures tend to affect employee security behaviour. Data for this study was collected in organisations located in the United States and Ireland. Results suggest that procedural security countermeasures are inclined to promote security-cautious behaviour, while their absence tends to lead to non-compliant behaviour.

Purpose - This paper provides new insights about security behaviour in selected US and Irish organisations by investigating how organisational culture and procedural security countermeasures tend to influence employee security actions. An increasing number of information security breaches in organisations presents a serious threat to the confidentiality of personal and commercially sensitive data. While recent research shows that humans are the weakest link in the security chain and the root cause of a great portion of security breaches, the extant security literature tends to focus on technical issues. Design/methodology/approach - This paper builds on general deterrence theory and prior organisational culture literature. The methodology adapted for this study draws on the analytical grounded theory approach employing a constant comparative method. Findings - This paper demonstrates that procedural security countermeasures and organisational culture tend to affect security behaviour in organisational settings. Research limitations/implications - This paper fills the void in information security research and takes its place among the very few studies that focus on behavioural as opposed to technical issues. Practical implications - This paper highlights the important role of procedural security countermeasures, information security awareness and organisational culture in managing illicit behaviour of employees. Originality/value - This study extends general deterrence theory in a novel way by including information security awareness in the research model and by investigating both negative and positive behaviours.

Autopilot systems have been vital to flight control for several years and have been making flight easier and more effective. Nevertheless, these autopilot systems are complex devices that demand exact control and stability. In this paper, design PID controller to control pitch angle in longitudinal dynamic of the autopilot system for jet transport using MATLAB/SIMULINK. In this paper designed on automatic pilot with Multi Input Multi Output (MIMO) system. Also, we studied the effect of atmospheric disturbances when a PID controller has been applied the results shown how this controller with good tuning reducing the effect of atmospheric disturbance on the consistency of the autopilot.

The CARDIS 2017 proceedings focus on the latest results on security of smart cards and related applications and their security.

Space-based systems play an important role in our daily life and business. The trend is likely to rely on the use of space based systems in a growing number of services or applications that can be either safety-of-life critical or business and mission-critical. The security measures implemented in space-based systems may turn out to be insufficient to guarantee the information assurance properties, in particular confidentiality (if required by the data policy), availability and integrity of these services/applications. The various and possible cyber-attacks on space segments, ground stations and its control segments are meanwhile well known and experienced in many cases. This paper will first introduce ESA and its constituency, then address the security specific aspects of its space missions. Threats specific to them from the cyberspace will be introduced, and the possible countermeasures briefly addressed. A categorization of the different types of space missions will then lead to the creation of the different protections profiles to be implemented respectively for the different categories.

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system’s ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years and containing 1.1 million binaries with 778 GB of raw feature data. Without reviewer assistance, we achieve 72

Classifier evasion consists in finding for a given instance $x$ the nearest instance $x'$ such that the classifier predictions of $x$ and $x'$ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixed Integer Linear Program solver and finds the optimal evading instance under an expressive set of constraints. Our second algorithm trades off optimality for speed by using symbolic prediction, a novel algorithm for fast finite differences on tree ensembles. On a digit recognition task, we demonstrate that both gradient boosted trees and random forests are extremely susceptible to evasions. Finally, we harden a boosted tree model without loss of predictive accuracy by augmenting the training set of each boosting round with evading instances, a technique we call adversarial boosting.