Biblio

Smartphones have become ubiquitous in our everyday lives, providing diverse functionalities via millions of applications (apps) that are readily available. To achieve these functionalities, apps need to access and utilize potentially sensitive data, stored in the user's device. This can pose a serious threat to users' security and privacy, when considering malicious or underskilled developers. While application marketplaces, like Google Play store and Apple App store, provide factors like ratings, user reviews, and number of downloads to distinguish benign from risky apps, studies have shown that these metrics are not adequately effective. The security and privacy health of an application should also be considered to generate a more reliable and transparent trustworthiness score. In order to automate the trustworthiness assessment of mobile applications, we introduce the Trust4App framework, which not only considers the publicly available factors mentioned above, but also takes into account the Security and Privacy (S&P) health of an application. Additionally, it considers the S&P posture of a user, and provides an holistic personalized trustworthiness score. While existing automatic trustworthiness frameworks only consider trustworthiness indicators (e.g. permission usage, privacy leaks) individually, Trust4App is, to the best of our knowledge, the first framework to combine these indicators. We also implement a proof-of-concept realization of our framework and demonstrate that Trust4App provides a more comprehensive, intuitive and actionable trustworthiness assessment compared to existing approaches.

Cyber physical systems are the key innovation driver for many domains such as automotive, avionics, industrial process control, and factory automation. However, their interconnection potentially provides adversaries easy access to sensitive data, code, and configurations. If attackers gain control, material damage or even harm to people must be expected. To counteract data theft, system manipulation and cyber-attacks, security mechanisms must be embedded in the cyber physical system. Adding hardware security in the form of the standardized Trusted Platform Module (TPM) is a promising approach. At the same time, traditional dependability features such as safety, availability, and reliability have to be maintained. To determine the right balance between security and dependability it is essential to understand their interferences. This paper supports developers in identifying the implications of using TPMs on the dependability of their system.We highlight potential consequences of adding TPMs to cyber-physical systems by considering the resulting safety, reliability, and availability. Furthermore, we discuss the potential of enhancing the dependability of TPM services by applying traditional redundancy techniques.

The current era can be characterized by the massive reliance on computing platforms in almost all domains, such as manufacturing, defense, healthcare, government. However, with the increased productivity, flexibility, and effectiveness that computers provide, comes the vulnerability to cyber-attacks where software, or even firmware, gets subtly modified by a hacker. The integration of a Trusted Platform Module (TPM) opts to tackle this issue by aiding in the detection of unauthorized modifications so that devices get remediation as needed. Nonetheless, the use of a TPM is impractical for resource-constrained devices due to power, space and cost limitations. With the recent proliferation of miniaturized devices along with the push towards the Internet-of Things (IoT) there is a need for a lightweight and practical alternative to the TPM. This paper proposes a cost-effective solution that incorporates modest amounts of integrated roots-of-trust logic and supports attestation of the integrity of the device's boot-up state. Our solution leverages crypto-acceleration modules found on many microprocessor and microcontroller based IoT devices nowadays, and introduces little additional overhead. The basic concepts have been validated through implementation on an SoC with an FPGA and a hard microcontroller. We report the validation results and highlight the involved tradeoffs.

Internet of Things is attracting a lot of interest in the modern world and has become a part of daily life leading to a large scale of distribution of Low power and Lossy Networks (LLN). For such networks constrained by low power and storage, IETF has proposed RPL an open standard routing protocol. However RPL protocol is exposed to a number of attacks which may degrade the performance and resources of the network leading to incorrect output. In this paper, to address Wormhole and Grayhole attack we propose a light weight Trust based mechanism. The proposed method uses direct trust which is computed based on node properties and Indirect Trust which is based on opinion of the neighboring nodes. The proposed method is energy friendly and does not impose excessive overhead on network traffic.

The most popular features of Big Data revolve around the so-called "3V" criterion: Volume, Variety and Velocity. Big Data is based on the massive collection and in-depth analysis of personal data, with a view to profiling, or even marketing and commercialization, thus violating citizens' privacy and the security of their data. In this article we discuss security and privacy solutions in the context of Big Data. We then focus on access control and present our new model called Knowledge-based Access Control (KBAC); this strengthens the access control already deployed in the target company (e.g., based on "RBAC" role or "ABAC" attributes for example) by adding a semantic access control layer. KBAC offers thinner access control, tailored to Big Data, with effective protection against intrusion attempts and unauthorized data inferences.

Cloud computing is an emerging technology that can provide organizations, enterprises and governments with cheaper, more convenient and larger scale computing resources. However, cloud computing will bring potential risks and threats, especially on security and privacy. We make a survey on potential threats and risks and existing solutions on cloud security and privacy. We also put forward some problems to be addressed to provide a secure cloud computing environment.

The fifth generation of cellular networks (5G) will enable different use cases where security will be more critical than ever before (e.g. autonomous vehicles and critical IoT devices). Unfortunately, the new networks are being built on the certainty that security problems cannot be solved in the short term. Far from reinventing the wheel, one of our goals is to allow security software developers to implement and test their reactive solutions for the capillary network of 5G devices. Therefore, in this paper a solution for analysing proximity-based attacks in 5G environments is modelled and tested using OMNET++. The solution, named CRAT, is able to decouple the security analysis from the hardware of the device with the aim to extend the analysis of proximity-based attacks to different use-cases in 5G. We follow a high-level approach, in which the devices can take the role of victim, offender and guardian following the principles of the routine activity theory.

In recent years, there has been progress in applying information technology to industrial control systems (ICS), which is expected to make the development cost of control devices and systems lower. On the other hand, the security threats are becoming important problems. In 2017, a command injection issue on a data logger was reported. In this paper, we focus on the risk assessment in security design for data loggers used in industrial control systems. Our aim is to provide a risk assessment method optimized for control devices and systems in such a way that one can prioritize threats more preciously, that would lead work resource (time and budget) can be assigned for more important threats than others. We discuss problems with application of the automotive-security guideline of JASO TP15002 to ICS risk assessment. Consequently, we propose a three-phase risk assessment method with a novel Risk Scoring Systems (RSS) for quantitative risk assessment, RSS-CWSS. The idea behind this method is to apply CWSS scoring systems to RSS by fixing values for some of CWSS metrics, considering what the designers can evaluate during the concept phase. Our case study with ICS employing a data logger clarifies that RSS-CWSS can offer an interesting property that it has better risk-score dispersion than the TP15002-specified RSS.

The analysis of security-related event logs is an important step for the investigation of cyber-attacks. It allows tracing malicious activities and lets a security operator find out what has happened. However, since IT landscapes are growing in size and diversity, the amount of events and their highly different representations are becoming a Big Data challenge. Unfortunately, current solutions for the analysis of security-related events, so called Security Information and Event Management (SIEM) systems, are not able to keep up with the load. In this work, we propose a distributed SIEM platform that makes use of highly efficient distributed normalization and persists event data into an in-memory database. We implement the normalization on common distribution frameworks, i.e. Spark, Storm, Trident and Heron, and compare their performance with our custom-built distribution solution. Additionally, different tuning options are introduced and their speed advantage is presented. In the end, we show how the writing into an in-memory database can be tuned to achieve optimal persistence speed. Using the proposed approach, we are able to not only fully normalize, but also persist more than 20 billion events per day with relatively small client hardware. Therefore, we are confident that our approach can handle the load of events in even very large IT landscapes.

Cloud computing is the major paradigm in today's IT world with the capabilities of security management, high performance, flexibility, scalability. Customers valuing these features can better benefit if they use a cloud environment built using HPC fabric architecture. However, security is still a major concern, not only on the software side but also on the hardware side. There are multiple studies showing that the malicious users can affect the regular customers through the hardware if they are co-located on the same physical system. Therefore, solving possible security concerns on the HPC fabric architecture will clearly make the fabric industries leader in this area. In this paper, we propose an autonomic HPC fabric architecture that leverages both resilient computing capabilities and adaptive anomaly analysis for further security.

Aggregate Computing is a promising paradigm for coordinating large numbers of possibly situated devices, typical of scenarios related to the Internet of Things, smart cities, drone coordination, and mass urban events. Currently, little work has been devoted to study and improve security in aggregate programs, and existing works focus solely on application-level countermeasures. Those security systems work under the assumption that the underlying computational model is respected; however, so-called Byzantine behaviour violates such assumption. In this paper, we discuss how Byzantine behaviours can hinder an aggregate program, and exploit application-level protection for creating bigger disruption. We discuss how the blockchain technology can mitigate these attacks by enforcing behaviours consistent with the expected operational semantics, with no impact on the application logic.

Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today's conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a cost-efficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.

Self-Adaptive Systems (SAS) are revolutionizing many aspects of our society. From server clusters to autonomous vehicles, SAS are becoming more ubiquitous and essential to our world. Security is frequently a priority for these systems as many SAS conduct mission-critical operations, or work with sensitive information. Fortunately, security is being more recognized as an indispensable aspect of virtually all aspects of computing systems, in all phases of software development. Despite the growing prominence in security, from computing education to vulnerability detection systems, it is just another concern of creating good software. Despite how critical security is, it is a quality attribute like other aspects such as reliability, stability, or adaptability in a SAS.

Threat intelligence is very important in order to execute a well-informed Security Development Lifecycle (SDL). Although there are many readily available solutions supporting tactical threat intelligence focusing on enterprise Information Technology (IT) infrastructure, the lack of threat intelligence solutions focusing on SDL is a known gap which is acknowledged by the security community. To address this shortcoming, we present a solution to automate the process of mining open source threat information sources to deliver product specific threat indicators designed to strategically inform the SDL while continuously monitoring for disclosures of relevant potential vulnerabilities during product design, development, and beyond deployment.

Threat modeling is one of the best practices to secure software development. A primary challenge for using this practice is how to extract threats. Existing threat extraction methods to this purpose are mainly based on penetration tests or vulnerability databases. This imposes a non-automated timeconsuming process, which fully relies on the human knowledge and expertise. In this paper, a method is presented, which can extract the threats to a software system based on the existing description of the software behavior. We elaborately describe software behavior with sequence diagrams enriched by security relevant attributes. To enrich a sequence diagram, some attributes and their associated values are added to the diagram elements and the communication between them. We have also developed a threat knowledge base from reliable sources such as CWE and CAPEC lists. Every threat in the knowledge base is described according to its occurrence conditions in the software. To extract threats of a software system, the enriched sequence diagrams describing the software behavior are matched with the threat rules in our knowledge base using a simple inference process. Results in a set of potential threats for the software system. The proposed method is applied on a software application to extract its threats. Our case study indicates the effectiveness of the proposed method compared to other existing methods.

A popular form of software reuse is the use of open source software libraries hosted on centralized code repositories, such as Maven or npm. Developers only need to declare dependencies to external libraries, and automated tools make them available to the workspace of the project. Recent incidents, such as the Equifax data breach and the leftpad package removal, demonstrate the difficulty in assessing the severity, impact and spread of bugs in dependency networks. While dependency checkers are being adapted as a counter measure, they only provide indicative information. To remedy this situation, we propose a fine-grained dependency network that goes beyond packages and into call graphs. The result is a versioned ecosystem-level call graph. In this paper, we outline the process to construct the proposed graph and present a preliminary evaluation of a security issue from a core package to an affected client application.

DLR as research organization increasingly faces the task to share its self-developed software with partners or publish openly. Hence, it is very important to harden the softwares to avoid opening attack vectors. Especially since DLR software is typically not developed by software engineering or security experts. In this paper we describe the data-oriented approach of our new found secure software engineering group to improve the software development process towards more secure software. Therefore, we have a look at the automated security evaluation of software as well as the possibilities to capture information about the development process. Our aim is to use our information sources to improve software development processes to produce high quality secure software.

At a time when all it takes to open a Twitter account is a mobile phone, the act of authenticating information encountered on social media becomes very complex, especially when we lack measures to verify digital identities in the first place. Because the platform supports anonymity, fake news generated by dubious sources have been observed to travel much faster and farther than real news. Hence, we need valid measures to identify authors of misinformation to avert these consequences. Researchers propose different authorship attribution techniques to approach this kind of problem. However, because tweets are made up of only 280 characters, finding a suitable authorship attribution technique is a challenge. This research aims to classify authors of tweets by comparing machine learning methods like logistic regression and naive Bayes. The processes of this application are fetching of tweets, pre-processing, feature extraction, and developing a machine learning model for classification. This paper illustrates the text classification for authorship process using machine learning techniques. In total, there were 46,895 tweets used as both training and testing data, and unique features specific to Twitter were extracted. Several steps were done in the pre-processing phase, including removal of short texts, removal of stop-words and punctuations, tokenizing and stemming of texts as well. This approach transforms the pre-processed data into a set of feature vector in Python. Logistic regression and naive Bayes algorithms were applied to the set of feature vectors for the training and testing of the classifier. The logistic regression based classifier gave the highest accuracy of 91.1% compared to the naive Bayes classifier with 89.8%.

The IP address attribution information includes the geographical information, the network routing information, the agency information, Internet Content Provider (ICP) information, etc. Nowadays, the attribution information is important to the network traffic engineering, which needs to be obtained in real time in network traffic analysis system. The existing proposed methods for IP address attribution information lookup cannot be employed in actual systems efficiently due to their low scalability or bad performance. They cannot address the backbone network's requirements for real-time IP address attribution information lookup, and most lookup methods do not support custom IP address attribution lookup. In response to these challenges, we propose a novel high-speed approach for IP address attribution information lookup. We first devise a data structure of IP address attribution information search tree (PATIP-TREE) to store custom IP address attribution information. Based on the PATIP-TREE, an effective algorithm for IP information lookup is proposed, which can support custom IP addresses attribution information lookup in real time. The experimental results show that our method outperforms the existing methods in terms of higher efficiency. Our approach also provides high scalability, which is suitable for many kinds network address such as IPv4 address, IPv6 address, named data networking address, etc.

Parfait [1] is a static analysis tool originally developed to find implementation defects in C/C++ systems code. Parfait's focus is on proving both high precision (low false positives) as well as scaling to systems with millions of lines of code (typically requiring 10 minutes of analysis time per million lines). Parfait has since been extended to detect security vulnerabilities in applications code, supporting the Java EE and PL/SQL server stack. In this abstract we describe some of the challenges we encountered in this process including some of the differences seen between the applications code being analysed, our solutions that enable us to analyse a variety of applications, and a summary of the challenges that remain.

In traditional steganographic schemes, RGB three channels payloads are assigned equally in a true color image. In fact, the security of color image steganography relates not only to data-embedding algorithms but also to different payload partition. How to exploit inter-channel correlations to allocate payload for performance enhancement is still an open issue in color image steganography. In this paper, a novel channel-dependent payload partition strategy based on amplifying channel modification probabilities is proposed, so as to adaptively assign the embedding capacity among RGB channels. The modification probabilities of three corresponding pixels in RGB channels are simultaneously increased, and thus the embedding impacts could be clustered, in order to improve the empirical steganographic security against the channel co-occurrences detection. Experimental results show that the new color image steganographic schemes incorporated with the proposed strategy can effectively make the embedding changes concentrated mainly in textured regions, and achieve better performance on resisting the modern color image steganalysis.

This paper proposes a game-theoretic approach to analyze the interactions between an attacker and a defender in a cyber-physical system (CPS) and develops effective defense strategies. In a CPS, the attacker launches cyber attacks on a number of nodes in the cyber layer, trying to maximize the potential damage to the underlying physical system while the system operator seeks to defend several nodes in the cyber layer to minimize the physical damage. Given that CPS attacking and defending is often a continual process, a zero-sum Markov game is proposed in this paper to model these interactions subject to underlying uncertainties of real-world events and actions. A novel model is also proposed in this paper to characterize the interdependence between the cyber layer and the physical layer in a CPS and quantify the impact of the cyber attack on the physical damage in the proposed game. To find the Nash equilibrium of the Markov game, we design an efficient algorithm based on value iteration. The proposed general approach is then applied to study the wide-area monitoring and protection issue in smart grid. Extensive simulations are conducted based on real-world data, and results show the effectiveness of the defending strategies derived from the proposed approach.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.

This talk will cover two topics, namely, modeling and design of Moving Target Defense (MTD), and DIFT games for modeling Advanced Persistent Threats (APTs). We will first present a game-theoretic approach to characterizing the trade-off between resource efficiency and defense effectiveness in decoy- and randomization-based MTD. We will then address the game formulation for APTs. APTs are mounted by intelligent and resourceful adversaries who gain access to a targeted system and gather information over an extended period of time. APTs consist of multiple stages, including initial system compromise, privilege escalation, and data exfiltration, each of which involves strategic interaction between the APT and the targeted system. While this interaction can be viewed as a game, the stealthiness, adaptiveness, and unpredictability of APTs imply that the information structure of the game and the strategies of the APT are not readily available. Our approach to modeling APTs is based on the insight that the persistent nature of APTs creates information flows in the system that can be monitored. One monitoring mechanism is Dynamic Information Flow Tracking (DIFT), which taints and tracks malicious information flows through a system and inspects the flows at designated traps. Since tainting all flows in the system will incur significant memory and storage overhead, efficient tagging policies are needed to maximize the probability of detecting the APT while minimizing resource costs. In this work, we develop a multi-stage stochastic game framework for modeling the interaction between an APT and a DIFT, as well as designing an efficient DIFT-based defense. Our model is grounded on APT data gathered using the Refinable Attack Investigation (RAIN) flow-tracking framework. We present the current state of our formulation, insights that it provides on designing effective defenses against APTs, and directions for future work.