Due to the complexity of modern web applications, security testing is a time-consuming process that heavily relies on manual interaction with various analysis tools. This process often needs to be repeated for newer versions of previously tested applications, as new functionalities frequently introduce security vulnerabilities. This paper introduces scrooge, a tool that automates change detection in web application functionality to enhance the efficiency and focus of the security testing process. We evaluate scrooge on various platforms, demonstrating its ability to reliably detect a range of changes. Scrooge successfully identifies different types of changes, showcasing its applicability across diverse scenarios with high accuracy.

Paper documents, where digital signatures are not directly applicable, are still widely utilized due to usability and legal reasons. We propose a novel approach to authenticating paper documents by taking short videos of them with smartphones. Our solution combines cryptographic and image comparison techniques to detect and highlight semantic-changing attacks on rich documents, containing text and graphics. We provide geometrical arguments for the security of our novel comparison algorithm, and prove that its combination with a cryptographic protocol is secure against strong adversaries capable of compromising different system components. We also measure its accuracy on a set of 128 videos of paper documents and a set of 960 synthetically generated warped documents, half containing subtle forgeries. Our algorithm finds all forgeries accurately with no false positives. The highlighted regions are large enough to be visible to users, but small enough to precisely locate forgeries.

Implementing access control policies is an error-prone task that can have severe consequences for the security of software applications. Model-driven approaches have been proposed in the literature and associated tools have been developed with the goal of reducing the complexity of this task and helping developers to produce secure software efficiently. Nevertheless, there is a lack of empirical data supporting the advantages of model-driven security approaches over code-centric approaches, which are the de-facto industry standard for software development.

Neural network implementations are known to be vulnerable to physical attack vectors such as fault injection attacks. As of now, these attacks were only utilized during the inference phase. In this work, we explore a novel attack paradigm by injecting faults during the training phase in a way that the resulting network can be attacked during deployment without the necessity of further faulting. We discuss attacks against ReLU activation functions that make it possible to generate a family of malicious inputs, which are called fooling inputs, to be used at inference time to induce controlled misclassifications. Such malicious inputs are obtained by mathematically solving a system of linear equations that would cause a particular behaviour on the attacked activation functions, similar to the one induced in training through faulting. We call such attacks fooling backdoors as the faults at training phase inject backdoors into the network that allow an attacker to produce fooling inputs. We evaluate our approach against multi-layer perceptron networks and convolutional networks on a popular image classification task obtaining high attack success rates (60% - 100%) and high classification confidence when as little as 25 neurons are attacked while preserving high accuracy on the original classification task.

Proximity attacks allow an adversary to uncover the location of a victim by repeatedly issuing queries with fake location data. These attacks have been mostly studied in scenarios where victims remain static and there are no constraints that limit the actions of the attacker. In such a setting, it is not difficult for the attacker to locate a particular victim and quantifying the effort for doing so is straightforward. However, it is far more realistic to consider scenarios where potential victims present a particular mobility pattern. In this article, we consider abstract (constrained and unconstrained) attacks on services that provide location information on other users in the proximity. We derive strategies for constrained and unconstrained attackers, and show that when unconstrained they can practically achieve success with theoretically optimal effort. We then propose a simple yet effective constraint that may be employed by a proximity service (for example, running in the cloud or using a suitable two-party protocol) as a countermeasure to increase the effort for the attacker several orders of magnitude both in simulated and real-world cases.

Programmable Logic Controllers (PLCs) are a core component of an Industrial Control System (ICS). However, if a PLC is compromised or the commands sent across a network from the PLCs are spoofed, consequences could be catastrophic. In this work, a novel technique to authenticate PLCs is proposed that aims at raising the bar against powerful attackers while being compatible with real-time systems. The proposed technique captures timing information for each controller in a non-invasive manner. It is argued that Scan Cycle is a unique feature of a PLC that can be approximated passively by observing network traffic. An attacker that spoofs commands issued by the PLCs would deviate from such fingerprints. To detect replay attacks a PLC Watermarking technique is proposed. PLC Watermarking models the relation between the scan cycle and the control logic by modeling the input/output as a function of request/response messages of a PLC. The proposed technique is validated on an operational water treatment plant (SWaT) and smart grid (EPIC) testbeds. Results from experiments indicate that PLCs can be distinguished based on their scan cycle timing characteristics.

To protect an Industrial Control System (ICS), defenders need to identify potential attacks on the system and then design mechanisms to prevent them. Unfortunately, identifying potential attack conditions is a time-consuming and error-prone process. In this work, we propose and evaluate a set of tools to symbolically analyse the software of Programmable Logic Controllers (PLCs) guided by an information flow analysis that takes into account PLC network communication (compositions). Our tools systematically analyse malicious network packets that may force the PLC to send specific control commands to actuators. We evaluate our approach in a real-world system controlling the dosing of chemicals for water treatment. Our tools are able to find 75 attack tactics (56 were novel attacks), and we confirm that 96% of these tactics cause the intended effect in our testbed.

Fingerprinting of various physical and logical devices has been proposed for uniquely identifying users or devices of mainstream IT systems such as PCs, laptops, and smart phones. However, the application of such techniques in Industrial Control Systems (ICS) is less explored for reasons such as a lack of direct access to such systems and the cost of faithfully reproducing realistic threat scenarios. This work addresses the feasibility of using fingerprinting techniques in the context of realistic ICS related to water treatment and distribution systems. A model-free sensor fingerprinting scheme ( NoiSense ) and a model-based sensor fingerprinting scheme ( NoisePrint ) are proposed. Using extensive experimentation with sensors, it is shown that noise patterns due to microscopic imperfections in hardware manufacturing can uniquely identify sensors with accuracy as high as 97%. The proposed technique can be used to detect physical attacks, such as the replacement of legitimate sensors by faulty or manipulated sensors. For NoisePrint , a combined fingerprint for sensor and process noise is created. The difference (called residual), between expected and observed values, i.e., noise, is used to derive a model of the system. It was found that in steady state the residual vector is a function of process and sensor noise. Data from experiments reveals that a multitude of sensors can be uniquely identified with a minimum accuracy of 90% based on NoisePrint . Also proposed is a novel challenge-response protocol that exposes more powerful cyber-attacks, including replay attacks.

Attacks on Internet of Things (IoT) devices, exploiting inherent vulnerabilities, have intensified over the last few years. Recent large-scale attacks, such as Persirai, Hakai, etc. corroborate concerns about the security of IoT devices. In this work, we propose an approach that allows easy integration of commercial off-the-shelf IoT devices into a general honeypot architecture. Our approach projects a small number of heterogeneous IoT devices (that are physically at one location) as many (geographically distributed) devices on the Internet, using connections to commercial and private VPN services. The goal is for those devices to be discovered and exploited by attacks on the Internet, thereby revealing unknown vulnerabilities. For detection and examination of potentially malicious traffic, we devise two analysis strategies: (1) given an outbound connection from honeypot, backtrack into network traffic to detect the corresponding attack command that caused the malicious connection and use it to download malware, (2) perform live detection of unseen URLs from HTTP requests using adaptive clustering. We show that our implementation and analysis strategies are able to detect recent large-scale attacks targeting IoT devices (IoT Reaper, Hakai, etc.) with overall low cost and maintenance effort.

Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional software layers. To illustrate our approach we implemented a secure monitor which collects user activities, such as keyboard and mouse events for insider attack detection. We show how our solution provides a strong anti-tampering guarantee with a low overhead: around 10 lines of code added to the entire application, an average execution time overhead of 5.7% and only 300KB of memory allocated for the trusted module.

Summary Android native applications, written in Java and distributed in APK format, are widely used in mobile devices. Their specific pattern of use lets the operating system control the creation and destruction of resources, such as activities and services (contexts). Programmers are not supposed to interfere with such life cycle events. Otherwise, contexts might be leaked, ie, they will never be deallocated from memory, or be deallocated late, leading to memory exhaustion and frozen applications. In practice, it is easy to write incorrect code, which hinders garbage collection of contexts and leads to context leakages. In this work, we present a novel static analysis method that finds context leaks in Android code. We apply this analysis to APKs translated into Java bytecode. We provide a formal analysis of our algorithms and suggest further research directions for improving precision by combining different approaches. We discuss the results of a large number of experiments with our analysis, which reveal context leaks in many widely used applications from the Android marketplace. This shows the practical usefulness of our technique and its superiority w.r.t. the well‐known Lint and Infer static analysis tools. We estimate the amount of memory saved by the collection of the leaks found and explain, experimentally, where programmers often go wrong and limitations of our tool. Such lessons could be used for designing of a sound or more powerful static analysis tool. This work can be considered as a practical application of software analysis techniques to solve practical problems.

Behavior-based detection approaches commonly address the threat of statically obfuscated malware. Such approaches often use graphs to represent process or system behavior and typically employ frequency-based graph mining techniques to extract characteristic patterns from collections of malware graphs. Recent studies in the molecule mining domain suggest that frequency-based graph mining algorithms often perform sub-optimally in finding highly discriminating patterns. We propose a novel malware detection approach that uses so-called compression-based mining on quantitative data flow graphs to derive highly accurate detection models. Our evaluation on a large and diverse malware set shows that our approach outperforms frequency-based detection models in terms of detection effectiveness by more than 600 percent.

In modern societies, critical services such as transportation, power supply, water treatment and distribution are strongly dependent on Industrial Control Systems (ICS). As technology moves along, new features improve services provided by such ICS. On the other hand, this progress also introduces new risks of cyber attacks due to the multiple direct and indirect dependencies between cyber and physical components of such systems. Performing rigorous security tests and risk analysis in these critical systems is thus a challenging task, because of the non-trivial interactions between digital and physical assets and the domain-specific knowledge necessary to analyse a particular system. In this work, we propose a methodology to model and analyse a System Under Test (SUT) as a data flow graph that highlights interactions among internal entities throughout the SUT. This model is automatically extracted from production code available in Programmable Logic Controllers (PLCs). We also propose a reachability algorithm and an attack diagram that will emphasize the dependencies between cyber and physical domains, thus enabling a human analyst to gauge various attack vectors that arise from subtle dependencies in data and information propagation. We test our methodology in a functional water treatment testbed and demonstrate how an analyst could make use of our designed attack diagrams to reason on possible threats to various targets of the SUT.

An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.

We present a privacy-assured multiplication protocol using which an arbitrary arithmetic formula with inputs from two parties over a finite field can be jointly computed on encrypted data using an additively homomorphic encryption scheme. Our protocol is secure against malicious adversaries. To motivate and illustrate applications of this technique, we demonstrate an attack on a class of known protocols showing how to compromise location privacy of honest users by manipulating messages in protocols with additively homomorphic encryption. We demonstrate how to apply the technique in order to solve different problems in geometric applications. We evaluate our approach using a prototypical implementation. The results show that the added overhead of our approach is small compared to insecure outsourced multiplication.

Despite numerous countermeasures proposed by practitioners and researchers, remote control-flow alteration of programs with memory-safety vulnerabilities continues to be a realistic threat. Guaranteeing that complex software is completely free of memory-safety vulnerabilities is extremely expensive. Probabilistic countermeasures that depend on random secret keys are interesting, because they are an inexpensive way to raise the bar for attackers who aim to exploit memory-safety vulnerabilities. Moreover, some countermeasures even support legacy systems. However, it is unclear how to quantify and compare the effectiveness of different probabilistic countermeasures or combinations of such countermeasures. In this paper we propose a methodology to rigorously derive security bounds for probabilistic countermeasures. We argue that by representing security notions in this setting as events in probabilistic games, similarly as done with cryptographic security definitions, concrete and asymptotic guarantees can be obtained against realistic attackers. These guarantees shed light on the effectiveness of single countermeasures and their composition and allow practitioners to more precisely gauge the risk of an attack.

Concolic (concrete+symbolic) execution has recently gained popularity as an effective means to uncover non-trivial vulnerabilities in software, such as subtle buffer overflows. However, symbolic execution tools that are designed to optimize statement coverage often fail to cover potentially vulnerable code because of complex system interactions and scalability issues of constraint solvers. In this paper, we present a tool (MACKE) that is based on the modular interactions inferred by static code analysis, which is combined with symbolic execution and directed inter-procedural path exploration. This provides an advantage in terms of statement coverage and ability to uncover more vulnerabilities. Our tool includes a novel feature in the form of interactive vulnerability report generation that helps developers prioritize bug fixing based on severity scores. A demo of our tool is available at https://youtu.be/icC3jc3mHEU.

Malware remains a major IT security threat and current detection approaches struggle to cope with a professionalized malware development industry. We propose the use of genetic programming to generate effective and robust malware detection models which we call FrankenMods. These are sets of graph metrics that capture characteristic malware behavior. Evolution of FrankenMods with good detection capabilities yields continuously improved detection effectiveness. FrankenMods are operationalized by evaluating them on quantitative data flow graphs that model malware behavior as data flows between system resources caused by issued system calls. We show that FrankenMods are substantially more robust and effective than a state-of-the-art graph metric-based detection approach.

Storing highly confidential data and carrying out security-related operations are crucial to many systems. Starting from an industrial use case we propose a generic architecture based on secret sharing which address critical operation authorization. By comparing and benchmarking different scheme from the literature we analyze the different trade-offs (security, functionality, performance) which can be achieved. Finally by providing an open source .NET implementation of several secret sharing schemes, this paper aims to rise awareness regarding the capabilities of such algorithms to increase security in industrial setting.

Location Based Services (LBS) are becoming increasingly popular. Users enjoy a wide range of services from tracking a lost phone to querying for nearby restaurants or nearby tweets. However, many users are concerned about sharing their location. A major challenge is achieving the privacy of LBS without hampering the utility. This paper focuses on the problem of location proximity, where principals are willing to reveal whether they are within a certain distance from each other. Yet the principals are privacy-sensitive, not willing to reveal any further information about their locations, nor the distance. We propose InnerCircle, a novel secure multi-party computation protocol for location privacy, based on partially homomorphic encryption. The protocol achieves precise fully privacy-preserving location proximity without a trusted third party in a single round trip. We prove that the protocol is secure in the semi-honest adversary model of Secure Multi-party Computation, and thus guarantees the desired privacy properties. We present the results of practical experiments of three instances of the protocol using different encryption schemes. We show that, thanks to its parallelizability, the protocol scales well to practical applications.

We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental construction of aggregated quantitative data flow graphs. These graphs represent communication between different system entities such as processes, sockets, files or system registries. We demonstrate the feasibility of our approach through a prototypical instantiation and implementation for the Windows operating system. Our experiments yield encouraging results: in our data set of samples from common malware families and popular non-malicious applications, our approach has a detection rate of 96% and a false positive rate of less than 1.6%. In comparison with closely related data flow based approaches, we achieve similar detection effectiveness with considerably better performance: an average full system analysis takes less than one second.

Host-based intrusion detection systems need to be complemented by analysis tools that help understand if malware or attackers have indeed intruded, what they have done, and what the consequences are. We present a tool that visualizes system activities as data flow graphs: nodes are operating system entities such as processes, files, and sockets; edges are data flows between the nodes. Pattern matching identifies structures that correspond to (suspected) malicious and (suspected) normal behaviors. Matches are highlighted in slices of the data flow graph. As a proof of concept, we show how email worm attacks, drive-by downloads, and data leakage are detected, visualized, and analyzed.

There exist an abundant number of tools for aiding developers and penetration testers to spot common software security vulnerabilities. However, testers are often confronted with situations where existing tools are of little help because a) they do not account for a particular configuration of the SUT and b) they do not include tests for certain vulnerabilities. To cope with this we propose a tool that allows users to define attacker models where the payloads and the behavior are cleanly separated and that abstract away from low-level implementation details such as HTTP requests.

Model-Based Testing (MBT) is a widely used methodology for generating tests aiming to ensure that the system behaviour conforms to its specification. Recently, it has been successfully applied for testing certain security properties. However, for the success of this approach, it is an important prerequisite to consider the correctness of test models with respect to the given security property. In this paper we present an approach for smart-card specific security properties that permits to validate the system with MBT from test schemas. We combine this MBT approach with UMLsec security verification technique, by using UMLsec stereotypes to verify the model w.r.t. given security properties and gain more confidence in the model. We then define an automatic procedure to generate security test from the UMLsec model via so-called "test schemas". We validate this approach on a fragment of the Global Platform specification and report on available tool support.