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Über dieses Buch

This book constitutes the revised selected papers of the Third International Conference on Information Systems Security and Privacy, ICISSP 2017, held in Porto, Portugal, in February 2017.
The 13 full papers presented were carefully reviewed and selected from a total of 100 submissions. They are dealing with topics such as vulnerability analysis and countermeasures, attack patterns discovery and intrusion detection, malware classification and detection, cryptography applications, data privacy and anonymization, security policy analysis, enhanced access control, and socio-technical aspects of security.

Inhaltsverzeichnis

Frontmatter

Application Marketplace Malware Detection by User Feedback Analysis

Abstract
Smartphones are becoming increasingly ubiquitous. Like recommended best practices for personal computers, users are encouraged to install antivirus and intrusion detection software on their mobile devices. However, even with such software these devises are far from being fully protected. Given that application stores are the source of most applications, malware detection on these platforms is an important issue. Based on our intuition, which suggests that an application’s suspicious behavior will be noticed by some users and influence their feedback, we present an approach for analyzing user reviews in mobile application stores for the purpose of detecting malicious apps. The proposed method transfers an application’s text reviews to numerical features in two main steps: (1) extract domain-phrases based on external domain-specific textual corpus on computer and network security, and (2) compute three statistical features based on domain-phrases occurrences. We evaluated the proposed methods on 2,506 applications along with their 128,863 reviews collected from “Amazon AppStore”. The results show that proposed method yields an AUC of 86% in the detection of malicious applications.
Tal Hadad, Rami Puzis, Bronislav Sidik, Nir Ofek, Lior Rokach

A System for Detecting Targeted Cyber-Attacks Using Attack Patterns

Abstract
Detecting multi-stage cyber-attacks remains a challenge for any security analyst working in large corporate environments. Conventional security solutions such as intrusion detection systems tend to report huge amount of alerts that still need to be examined and cross-checked with other available data in order to eliminate false positives and identify any legitimate attacks. Attack patterns can be used as a means to describe causal relationships between the events detected at different stages of an attack. In this paper, we introduce an agent-based system that collects relevant event data from various sources in the network, and then correlates the events according to predefined attack patterns. The system allows security analysts to formulate the attack patterns based on their own knowledge and experience, and test them on available datasets. We present an example attack pattern for discovering suspicious activities in the network following a potential brute force attack on one of the servers. We discuss the results produced by our prototype implementation and show how a security analyst can drill down further into the data to identify the victim and obtain information about the attack methods.
Ian Herwono, Fadi Ali El-Moussa

A Better Understanding of Machine Learning Malware Misclassifcation

Abstract
Machine learning-based malware detection systems have been widely suggested and used as a replacement for signature-based detection methods. Such systems have shown that they can provide a high detection rate when recognising non-previously seen malware samples. However, when classifying malware based on their behavioural features, some new malware can go undetected, resulting in a misclassification. Our aim is to gain more understanding of the underlying causes of malware misclassification; this will help to develop more robust malware detection systems. Towards this objective, several questions have been addressed in this paper: Does misclassification increase over a period of time? Do changes that affect classification occur in malware at the level of families, where all instances that belong to certain families are hard to detect? Alternatively, can such changes be traced back to certain malware variants instead of families? Also, does misclassification increase when removing distinct API functions that have been used only by malware? As this technique could be used by malware writers to evade the detection. Our experiments showed that changes in malware behaviour are mostly due to behavioural changes at the level of variants across malware families, where variants did not behave as expected. It also showed that machine learning-based systems could maintain a high detection rate even in the case of trying to evade the detection by not using distinct API functions, which are uniquely used by malware.
Nada Alruhaily, Tom Chothia, Behzad Bordbar

Situation-Aware Access Control for Industrie 4.0

Abstract
In recent years, the Internet of Things emerges as a new paradigm that enables new applications, such as Smart Factories, Smart Homes, and Smart Cities. In these applications, privacy and security are important issues, especially regarding the access to sensors and actuators. Sometimes, this access should only be permitted if a certain situation occurs, e.g., access to a camera should only be allowed in an exceptional situation. In this paper, we enable situation-based access control for sensitive components in the Internet of Things, focusing on Industrie 4.0. To realize this, we combine an attribute-based access control system with a situation recognition system to create a highly flexible, well performing, and situation-aware access control system. This access control system is capable of automatically granting or prohibiting access depending on situation occurrences and other dynamic or static security attributes.
Marc Hüffmeyer, Pascal Hirmer, Bernhard Mitschang, Ulf Schreier, Matthias Wieland

How to Quantify Graph De-anonymization Risks

Abstract
An increasing amount of data are becoming publicly available over the Internet. These data are released after applying some anonymization techniques. Recently, researchers have paid significant attention to analyzing the risks of publishing privacy-sensitive data. Even if data anonymization techniques were applied to protect privacy-sensitive data, several de-anonymization attacks have been proposed to break their privacy. However, no theoretical quantification for relating the data vulnerability against de-anonymization attacks and the data utility that is preserved by the anonymization techniques exists.
In this paper, we first address several fundamental open problems in the structure-based de-anonymization research by establishing a formal model for privacy breaches on anonymized data and quantifying the conditions for successful de-anonymization under a general graph model. To the best of our knowledge, this is the first work on quantifying the relationship between anonymized utility and de-anonymization capability. Our quantification works under very general assumptions about the distribution from which the data are drawn, thus providing a theoretical guide for practical de-anonymization/anonymization techniques.
Furthermore, we use multiple real-world datasets including a Facebook dataset, a Collaboration dataset, and two Twitter datasets to show the limitations of the state-of-the-art de-anonymization attacks. From these experimental results, we demonstrate the ineffectiveness of previous de-anonymization attacks and the potential of more powerful de-anonymization attacks in the future, by comparing the theoretical de-anonymization capability proposed by us with the practical experimental results of the state-of-the-art de-anonymization methods.
Wei-Han Lee, Changchang Liu, Shouling Ji, Prateek Mittal, Ruby B. Lee

A Security Pattern Classification Based on Data Integration

Abstract
Security patterns are design patterns specialised to provide reusable and general solutions to recurring security problems. These patterns, which capture the strengths of different security approaches, are intended to make the design of maintainable and secure applications easier. The pattern community is continuously providing new security patterns (180 patterns are available at the moment). For a given problem, this growing pattern set along with their abstract presentations make the security pattern choice tedious, even for experts in software design. We contribute in this issue by presenting a method of security pattern classification based upon data extraction and integration. The pattern classification is semi-automatically inferred by means of a data-store integrating disparate publicly available security data. This classification exposes relationships among software attacks, weaknesses, security principles and security patterns. It expresses the pattern combinations that can counter a given attack. Besides the pattern classification, we show that the data-store can be used to generate Attack Defense Trees. In our context, these illustrate, for a given attack, its sub-attacks and the related defenses given under the form of security pattern combinations. Such trees make the pattern classification more readable even for beginners in security patterns. Finally, we evaluate on 25 human subjects the benefits of using Attack Defense Trees and a classification established for Web applications, which covers 215 attacks, 136 software weaknesses, 66 security principles and 26 security patterns.
Sébastien Salva, Loukmen Regainia

Forensic Analysis of Android Runtime (ART) Application Heap Objects in Emulated and Real Devices

Abstract
Each new release of a mobile device operating system represents a renewed challenge for the forensics analyst. Even a small modification or fault correction of such basic software requires the revision of forensic tools and methods, frequently yielding to the development of new investigation tools and the consequent adaptation of methods. Forensic analysts then need to preserve each tool set and related methods and associate these sets to the specific mobile operating system release. This paper describes a case of transition consequent to the Android Runtime (ART) operating system release. The introduction of this system in the market required the development of a new forensic technique for analyzing ART memory objects using a volatile memory data extraction. Considering the Android Open Source Project (AOSP) source code, a method and associated software tools were developed allowing the location, extraction and interpretation of arbitrary ART memory instances with the respective object classes and their data properties. The proposed technique and tools were validated both for emulated and real devices, illustrating the difficulties related to the forensics analysis for the target system due to its particular implementations by multiple manufacturers of mobile devices.
Alberto Magno Muniz Soares, Rafael Timoteo de Sousa Junior

Efficient Detection of Conflicts in Data Sharing Agreements

Abstract
This paper considers Data Sharing Agreements and their management as a key aspect for a secure, private and controlled access and usage of data. Starting from describing formats and languages for the agreements, we then focus on the design, development, and performance evaluation of an analysis tool, to spot potential conflicts within the data privacy policies constituting the agreement. The promising results achieved in terms of the execution time, by varying the number of rules in the agreements, and number of terms in the rules vocabulary, pave the way for the employment of the analyser in a real-use context.
Gianpiero Costantino, Fabio Martinelli, Ilaria Matteucci, Marinella Petrocchi

On Using Obligations for Usage Control in Joining of Datasets

Abstract
Legitimately collected and accessed data must also be used appropriately according to laws, guidelines, policies or the (current) preferences of data subjects. For example, inconsistency between the data collection purpose and the data usage purpose may conflict with some privacy principles. In this contribution we motivate adopting the usage control model when joining vertically-separated relational datasets and characterize it as obligations within the Usage Control (UCON) model. Such obligations are defined by the state of the object (i.e., a dataset) in the UCON model with respect to the state of another object/dataset. In case of the join operation, dependency on two UCON objects (i.e., two datasets) results in a new type of UCON obligations. We describe also a number of mechanisms to realize the identified concept in database management systems. To this end, we also provide some example methods for determining whether two given datasets can be joined.
Mortaza S. Bargh, Marco Vink, Sunil Choenni

Directional Distance-Bounding Identification

Abstract
Distance bounding (DB) protocols allow a prover to convince a verifier that they are within a distance bound. A public key distance bounding relies on the public key of the users to prove their identity and proximity claim. There has been a number of approaches in the literature to formalize security of public key distance bounding protocols. In this paper we extend an earlier work that formalizes security of public key DB protocols using an approach that is inspired by the security definition of identification protocols, and is referred to it as distance-bounding identification (\(\mathtt {DBID}\)). We first show that if protocol participants have access to a directional antenna, many existing protocols that have been proven secure, will become insecure, and then show to revise the previous model to include this new capability of the users. DBID approach provides a natural way of modelling man-in-the-middle attack in line with identification protocols, as well as other attacks that are commonly considered in distance bounding protocols. We compare the existing public key DB models, and prove the security of the scheme known as \(\mathtt {ProProx}\), in our model.
Ahmad Ahmadi, Reihaneh Safavi-Naini

An Information Security Management for Socio-Technical Analysis of System Security

Abstract
Concerned about the technical and social aspects at the root causes of security incidents and how they can hide security vulnerabilities we propose a methodology compatible with the Information Security Management life-cycle. Retrospectively, it supports analysts to reason about the socio-technical causes of observed incidents; prospectively, it helps designers account for human factors and remove potential socio-technical vulnerabilities from a system’s design. The methodology, called \(\text {S}{\cdot }\text {CREAM}\), stems from practices in safety, but because of key differences between the two disciplines migrating concepts, techniques, and tools from safety to security requires a complete re-thinking. \(\text {S}{\cdot }\text {CREAM}\) is supported by a tool, which we implemented. When available online it will assist security analysts and designers in their tasks. Using \(\text {S}{\cdot }\text {CREAM}\), we discuss potential socio-technical issues in the Yubikey’s two-factor authentication device.
Jean-Louis Huynen, Gabriele Lenzini

An Exploration of Some Security Issues Within the BACnet Protocol

Abstract
Building automation systems control a range of services, commonly heating, ventilation and air-conditioning. BACnet is a leading protocol used to transmit data across building automation system networks, for the purpose of reporting and control. Security is an issue in BACnet due to its initial design brief which appears to be centred around a centralised monolithic command and control architecture. With the advent of the Internet of Things, systems that were isolated are now interconnected. This interconnectivity is problematic because whilst security is included in the BACnet standard, it is not implemented by vendors of building automation systems. The lack of focus on security can lead to vulnerabilities in the protocol being exploited with the result that the systems and the buildings they control are open to attack. This paper describes two proof-of-concept protocol attacks on a BACnet system, proves one attack using experimentation and the other attack through simulation. The paper contextualises a range of identified attacks using a threat model based on the STRIDE threat taxonomy.
Matthew Peacock, Michael N. Johnstone, Craig Valli

Not So Greedy: Enhanced Subset Exploration for Nonrandomness Detectors

Abstract
Distinguishers and nonrandomness detectors are used to distinguish ciphertext from random data. In this paper, we focus on the construction of such devices using the maximum degree monomial test. This requires the selection of certain subsets of key and IV-bits of the cipher, and since this selection to a great extent affects the final outcome, it is important to make a good selection. We present a new, generic and tunable algorithm to find such subsets. Our algorithm works on any stream cipher, and can easily be tuned to the desired computational complexity. We test our algorithm with both different input parameters and different ciphers, namely Grain-128a, Kreyvium and Grain-128. Compared to a previous greedy approach, our algorithm consistently provides better results.
Linus Karlsson, Martin Hell, Paul Stankovski

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