Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers. Falsifying the information collected by the adversary post penetration may significantly slower lateral movement and increase the amount of noise generated within the victim's network. We investigate the effect of fake vulnerabilities within a real enterprise network on the attacker performance. We use the attack graphs to model the path of an attacker making its way towards a target in a given network. We use combinatorial optimization in order to find the optimal assignments of fake vulnerabilities. We demonstrate the feasibility of our deception-based defense by presenting results of experiments with a large scale real network. We show that adding fake vulnerabilities forces the adversary to invest a significant amount of effort, in terms of time and exploitability cost.
In cyber security, deception is often used to create a controlled path for attackers to follow starting from the the initial reconnaissance phase. For example, Cohen and Koike @cite_19 @cite_51 showed how deception can control the path of an attack using red teams in experiments attacking a computer network. Repik @cite_22 makes a strong argument in favor of planned actions taken to mislead hackers. If the deception is obvious to the adversary, even unintentionally, the attacker can avoid, bypass, and even overcome the deceptive traps. We aim to avoid this situation and make the deception hard to identify by an adversary. RP Move to intro
@cite_19 @cite_51 @cite_22
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This paper describes a series of experiments in which specific deceptions were created in order to induce red teams attacking computer networks to attack network elements in sequence. It demonstrates the ability to control the path of an attacker through the use of deceptions and allows us to associate metrics with paths and their traversal. Our physical and digital worlds are converging at a rapid pace, putting a lot of our valuable information in digital formats. Currently, most computer systems’ predictable responses provide attackers with valuable information on how to infiltrate them. In this chapter, we discuss how the use of deception can play a prominent role in enhancing the security of current computer systems. We show how deceptive techniques have been used in many successful computer breaches. Phishing, social engineering, and drive-by-downloads are some prime examples. We discuss why deception has only been used haphazardly in computer security. Additionally, we discuss some of the unique advantages deception-based security mechanisms bring to computer security. Finally, we present a framework where deception can be planned and integrated into computer defenses. An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interactions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender's exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and effective greedy algorithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our extensive experimental analysis demonstrates the effectiveness of our approaches. Deceptive techniques played a prominent role in many human conflicts throughout history. Digital conflicts are no different as the use of deception has found its way to computing since at least the 1980s. However, many computer defenses that use deception were ad-hoc attempts to incorporate deceptive elements. In this paper, we present a model that can be used to plan and integrate deception in computer security defenses. We present an overview of fundamental reasons why deception works and the essential principles involved in using such techniques. We investigate the unique advantages deception-based mechanisms bring to traditional computer security defenses. Furthermore, we show how our model can be used to incorporate deception in many part of computer systems and discuss how we can use such techniques effectively. A successful deception should present plausible alternative(s) to the truth and these should be designed to exploit specific adversaries' biases. We investigate these biases and discuss how can they be used by presenting a number of examples. Current techniques towards information security have limited capabilities to detect and counter attacks that involve different kinds of masquerade and spread of misinformation executed over long time periods to achieve malicious goals. Detection of such deceptive information obtained during online interactions (emails, chat room conversations) is the first step before counter strategies can be developed. With the large-scale use of information technologies as a general communication medium, facilitating deception detection is a key enabler to utilizing information systems to their fullest potential. This article presents a framework for computer-aided deception detection building on the interpersonal deception theory (IDT) of human interpersonal communication research and text-processing techniques for facilitating deception analysis of text-oriented communication. A state-transition diagram based framework is proposed to model the dynamic evolution of an interpersonal conversation between a sender and receiver based on the IDT-based process schemata. The framework is then utilized to develop a deception detection agent to process textual information. Deception detection is defined as a process of model verification. Architecture of a prototype under development and open problems for further research in this area are outlined. Security of Cyber-Physical Systems (CPS) against cyber attacks is an important yet challenging problem. Since most cyber attacks happen in erratic ways, it is difficult to describe them systematically. In this paper, instead of identifying a specific cyber attack model, we are focused on analyzing the system's response during cyber attacks. Deception attacks (or false data injection attacks), which are performed by tampering with system components or data, are not of particular concern if they can be easily detected by the system's monitoring system. However, intelligent cyber attackers can avoid being detected by the monitoring system by carefully design cyber attacks. Our main objective is to investigate the performance of such stealthy deception attacks from the system's perspective. We investigate three kinds of stealthy deception attacks according to the attacker's ability to compromise the system. Based on the information about the dynamics of the system and existing hypothesis testing algorithms, we derive the necessary and sufficient conditions under which the attacker could perform each kind of attack without being detected. In the end, we illustrate the threat of these cyber attacks using an Unmanned Aerial Vehicle (UAV) navigation example. In modern days, breakthroughs in information and communications technologies lead to more and more devices of every imaginable type being connected to the Internet. This also strengthens the need for protection against cyber-attacks, as virtually any devices with a wireless connection could be vulnerable to malicious hacking attempts. Meanwhile, honeypot-based deception mechanism has been considered as one of the methods to ensure security for modern networks in the Internet of Things (IoT). In this paper, we address the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results show that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his her strategy while keeping the attacker’s success rate low. A growing field in computer applications is the use of algorithms to spot the lie. The most promising area within this field is the analysis of the language of the liar since speakers effectively control only the meaning they wish to convey, but not the linguistic style of the communication. With the advent of computational means to analyze language, we now have the ability to recognize differences in the way speakers phrase their lies as opposed to their truths. The main goal of this book is to cover the advances of the last 10 years in automatically discriminating truths from lies. To give the reader a grounding in deception studies, it describes a range of behaviors (physiological, gestural as well as verbal) that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and real-world data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, fraud, and false product reviews. Having established the background, the book concentrates on computational analyses of deceptive verbal behavior, which have enabled the field of deception studies to move from individual cues to overall differences in behavior. The book concludes with a set of open questions that the computational work has generated. Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. In this paper, we introduce a formal reasoning system called the InCA (Intelligent Cyber Attribution) framework that is designed to aid an analyst in the attribution of a cyber-operation even when the available information is conflicting and or uncertain. Our approach combines argumentation-based reasoning, logic programming, and probabilistic models to not only attribute an operation but also explain to the analyst why the system reaches its conclusions. In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security. In particular, MagNet consisting of an adversary detector and a data reformer is by far one of the strongest defenses in the black-box oblivious attack setting, where the attacker aims to craft transferable adversarial examples from an undefended DNN model to bypass an unknown defense module deployed on the same DNN model. Under this setting, MagNet can successfully defend a variety of attacks in DNNs, including the high-confidence adversarial examples generated by the Carlini and Wagner's attack based on the @math distortion metric. However, in this paper, under the same attack setting we show that adversarial examples crafted based on the @math distortion metric can easily bypass MagNet and mislead the target DNN image classifiers on MNIST and CIFAR-10. We also provide explanations on why the considered approach can yield adversarial examples with superior attack performance and conduct extensive experiments on variants of MagNet to verify its lack of robustness to @math distortion based attacks. Notably, our results substantially weaken the assumption of effective threat models on MagNet that require knowing the deployed defense technique when attacking DNNs (i.e., the gray-box attack setting). Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attacks generalize to the physical domain, to create perturbations on physical objects that fool image classifiers under a variety of real-world conditions. Such attacks pose a risk to deep learning models used in safety-critical cyber-physical systems. In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene. Improving upon a previous physical attack on image classifiers, we create perturbed physical objects that are either ignored or mislabeled by object detection models. We implement a Disappearance Attack, in which we cause a Stop sign to "disappear" according to the detector-either by covering thesign with an adversarial Stop sign poster, or by adding adversarial stickers onto the sign. In a video recorded in a controlled lab environment, the state-of-the-art YOLOv2 detector failed to recognize these adversarial Stop signs in over 85 of the video frames. In an outdoor experiment, YOLO was fooled by the poster and sticker attacks in 72.5 and 63.5 of the video frames respectively. We also use Faster R-CNN, a different object detection model, to demonstrate the transferability of our adversarial perturbations. The created poster perturbation is able to fool Faster R-CNN in 85.9 of the video frames in a controlled lab environment, and 40.2 of the video frames in an outdoor environment. Finally, we present preliminary results with a new Creation Attack, where in innocuous physical stickers fool a model into detecting nonexistent objects. Hardware security techniques are validated using fixed in-house methods. However, the effectiveness of such techniques in the field cannot be the same as the attacks are dynamic. A red team blue team approach mimics dynamic attack scenarios and thus can be used to validate such techniques by determining the effectiveness of a defense and identifying vulnerabilities in it. By following a red team blue team approach, we validated two trojan detection techniques namely, path delay measurement and ring oscillator frequency monitoring, in the Embedded Systems Challenge (ESC) 2010. In ESC, one team performed the blue team activities and eight other teams performed red team activities. The path delay measurement technique detected all the trojans. The ESC exposed a vulnerability in the RO-based technique which was exploited by the red teams causing some trojans to be undetected. Post ESC, we developed a technique to fix this vulnerability. Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24 of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19 and 88.94 . We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder. Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24 of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19 and 88.94 . We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder. Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In this paper, we explore the use of multimodal real-life data for the task of deception detection. We develop a new deception dataset consisting of videos from reallife scenarios, and build deception tools relying on verbal and nonverbal features. We achieve classification accuracies in the range of 77-82 when using a model that extracts and fuses features from the linguistic and visual modalities. We show that these results outperform the human capability of identifying deceit. Adversarial attacks on machine learning models have seen increasing interest in the past years. By making only subtle changes to the input of a convolutional neural network, the output of the network can be swayed to output a completely different result. The first attacks did this by changing pixel values of an input image slightly to fool a classifier to output the wrong class. Other approaches have tried to learn "patches" that can be applied to an object to fool detectors and classifiers. Some of these approaches have also shown that these attacks are feasible in the real-world, i.e. by modifying an object and filming it with a video camera. However, all of these approaches target classes that contain almost no intra-class variety (e.g. stop signs). The known structure of the object is then used to generate an adversarial patch on top of it. In this paper, we present an approach to generate adversarial patches to targets with lots of intra-class variety, namely persons. The goal is to generate a patch that is able successfully hide a person from a person detector. An attack that could for instance be used maliciously to circumvent surveillance systems, intruders can sneak around undetected by holding a small cardboard plate in front of their body aimed towards the surveillance camera. From our results we can see that our system is able significantly lower the accuracy of a person detector. Our approach also functions well in real-life scenarios where the patch is filmed by a camera. To the best of our knowledge we are the first to attempt this kind of attack on targets with a high level of intra-class variety like persons. Cyber-secure networked control is modeled, analyzed, and experimentally illustrated in this paper. An attack space defined by the adversary's system knowledge, disclosure, and disruption resources is introduced. Adversaries constrained by these resources are modeled for a networked control system architecture. It is shown that attack scenarios corresponding to replay, zero dynamics, and bias injection attacks can be analyzed using this framework. An experimental setup based on a quadruple-tank process controlled over a wireless network is used to illustrate the attack scenarios, their consequences, and potential counter-measures. We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack. In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode. Limiting the attack activity to this subset helps prevent detection of the attack by the agent. We propose a novel method to determine when an adversarial example should be crafted and applied. In the enchanting attack, the adversary aims at luring the agent to a designated target state. This is achieved by combining a generative model and a planning algorithm: while the generative model predicts the future states, the planning algorithm generates a preferred sequence of actions for luring the agent. A sequence of adversarial examples is then crafted to lure the agent to take the preferred sequence of actions. We apply the two tactics to the agents trained by the state-of-the-art deep reinforcement learning algorithm including DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much reward as the uniform attack (i.e., attacking at every time step) does by attacking the agent 4 times less often. Our enchanting attack lures the agent toward designated target states with a more than 70 success rate. Videos are available at this http URL Modern cyber attacks consist of a series of steps and are generally part of larger campaigns. Large-scale field data provides a quantitative measurement of these campaigns. On the other hand, security practitioners extract and report qualitative campaign characteristics manually. Linking the two sources provides new insights about attacker strategies from measurements. However, this is a time-consuming task because qualitative measurements are generally reported in natural language and are not machine-readable. We propose an approach to bridge measurement data with manual analysis. We borrow the idea from threat intelligence: we define campaigns using a 4-stage model, and describe each stage using IOCs (indicators of compromise), e.g. URLs and IP addresses. We train a multi-class classifier to extract IOCs and further categorize them into different stages. We implement these ideas in a system called ChainSmith. Our system can achieve 91.9 precision and 97.8 recall in extracting IOCs, and can determine the campaign roles for 86.2 of IOCs with 78.2 precision and 80.7 recall. We run ChainSmith on 14,155 online security articles, from which we collect 24,653 IOCs. The semantic roles allow us to link manual attack analysis with large scale field measurements. In particular, we study the effectiveness of different persuasion techniques used on enticing user to download the payloads. We find that the campaign usually starts from social engineering and "missing codec" ruse is a common persuasion technique that generates the most suspicious downloads each day. Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we tackle the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. We show that the Expectation over Transformation technique, which was originally proposed to enhance the robustness of adversarial perturbations in image classification, can be successfully adapted to the object detection setting. Our approach can generate adversarially perturbed stop signs that are consistently mis-detected by Faster R-CNN as other objects, posing a potential threat to autonomous vehicles and other safety-critical computer vision systems.