Active Adversary Research Articles

Pairing: Privately Balancing Multiparty Real-Time Supply and Demand on the Power Grid

Microgrids equipped with renewable energy resources have proven to be critical building blocks on the power grid that can greatly improve the grid performance. A promising application would be enabling microgrids to utilize their local energy for further balancing the regional supply and demand at different times – ensuring better system economics and reliability. However, due to the privacy concerns on continuously revealing each microgrid’s local data for deriving real-time optimal balancing decisions, the application of such promising cooperative technique is still limited. In this paper, we design an efficient cryptographic protocol for privately balancing the regional supply and demand, as well as each microgrid’s local supply and demand in real time. We prove the security of our protocol against both passive and active adversaries. Meanwhile, we implemented a prototype of the Pairing system that integrates cryptographic protocol and the power transmission network. We mount the real smart grid datasets into Pairing in real time for system evaluations. The experimental results demonstrate the practicality of our system by scaling to hundreds of microgrids with high accuracy and efficient system performance.

First steps toward CNN based source classification of document images shared over messaging app

Knowledge of source smartphone corresponding to a document image can be helpful in a variety of applications including copyright infringement, ownership attribution, leak identification, and usage restriction. In this work, we investigate a convolutional neural network-based approach to solve source smartphone identification problem for printed text documents which have been captured by smartphone cameras and shared over messaging platform. The proposed method comprises of a fusion technique which allows a single network to learn a model directly from two-channel images fused out of native letter images and their denoised versions. In the absence of any publicly available dataset addressing this problem, we introduce a new image dataset consisting of 770 images of documents printed in three different fonts, captured using 22 smartphones and shared over WhatsApp. A series of experiments are conducted on the newly captured dataset including an experiment in the presence of an active adversary who might re-scale the native images before sharing over WhatsApp. In all the experiments, for classification of WhatsApp-processed document images, the proposed method outperforms the baseline methods.

The Arbitrarily Varying Relay Channel.

We study the arbitrarily varying relay channel, which models communication with relaying in the presence of an active adversary. We establish the cutset bound and partial decode-forward bound on the random code capacity. We further determine the random code capacity for special cases. Then, we consider conditions under which the deterministic code capacity is determined as well. In addition, we consider the arbitrarily varying Gaussian relay channel with sender frequency division under input and state constraints. We determine the random code capacity, and establish lower and upper bounds on the deterministic code capacity. Furthermore, we show that as opposed to previous relay models, the primitive relay channel has a different behavior compared to the non-primitive relay channel in the arbitrarily varying scenario.

A Lightweight and Secure Data Collection Serverless Protocol Demonstrated in an Active RFIDs Scenario

In the growing Internet of Things context, thousands of computing devices with various functionalities are producing data (from environmental sensors or other sources). However, they are also collecting, storing, processing and transmitting data to eventually communicate them securely to third parties (e.g., owners of devices or cloud data storage). The deployed devices are often battery-powered mobile or static nodes equipped with sensors and/or actuators, and they communicate using wireless technologies. Examples include unmanned aerial vehicles, wireless sensor nodes, smart beacons, and wearable health objects. Such resource-constrained devices include Active Radio Frequency IDentification (RFID) nodes, and these are used to illustrate our proposal. In most scenarios, these nodes are unattended in an adverse environment, so data confidentiality must be ensured from the sensing phase through to delivery to authorized entities: in other words, data must be securely stored and transmitted to prevent attack by active adversaries even if the nodes are captured. However, due to the scarce resources available to nodes in terms of energy, storage, and/or computation, the proposed security solution has to be lightweight. In this article, we propose a serverless protocol to enable Mobile Data Collectors (MDCs), such as drones, to securely collect data from mobile and static Active RFID nodes and then deliver them later to an authorized third party. The whole solution ensures data confidentiality at each step (from the sensing phase, before data collection by the MDC, once data have been collected by MDC, and during final delivery), while fulfilling the lightweight requirements for the resource-limited entities involved. To assess the suitability of the protocol against the performance requirements, it was implemented on the most resource-constrained devices to get the worst possible results. In addition, to prove the protocol fulfills the security requirements, it was analyzed using security games and also formally verified using the AVISPA and ProVerif tools.

Sensor-Based Proximity Detection in the Face of Active Adversaries

Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a context-manipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniques: features-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases.

Detection Games under Fully Active Adversaries.

We study a binary hypothesis testing problem in which a defender must decide whether a test sequence has been drawn from a given memoryless source , while an attacker strives to impede the correct detection. With respect to previous works, the adversarial setup addressed in this paper considers an attacker who is active under both hypotheses, namely, a fully active attacker, as opposed to a partially active attacker who is active under one hypothesis only. In the fully active setup, the attacker distorts sequences drawn both from and from an alternative memoryless source , up to a certain distortion level, which is possibly different under the two hypotheses, to maximize the confusion in distinguishing between the two sources, i.e., to induce both false positive and false negative errors at the detector, also referred to as the defender. We model the defender–attacker interaction as a game and study two versions of this game, the Neyman–Pearson game and the Bayesian game. Our main result is in the characterization of an attack strategy that is asymptotically both dominant (i.e., optimal no matter what the defender’s strategy is) and universal, i.e., independent of and . From the analysis of the equilibrium payoff, we also derive the best achievable performance of the defender, by relaxing the requirement on the exponential decay rate of the false positive error probability in the Neyman–Pearson setup and the tradeoff between the error exponents in the Bayesian setup. Such analysis permits characterizing the conditions for the distinguishability of the two sources given the distortion levels.

Collusion-Resistant Processing of SQL Range Predicates

Prior solutions for securely handling SQL range predicates in outsourced Cloud-resident databases have primarily focused on passive attacks in the Honest-but-Curious adversarial model, where the server is only permitted to observe the encrypted query processing. We consider here a significantly more powerful adversary, wherein the server can launch an active attack by clandestinely issuing specific range queries via collusion with a few compromised clients. The security requirement in this environment is that data values from a plaintext domain of size N should not be leaked to within an interval of size H. Unfortunately, all prior encryption schemes for range predicate evaluation are easily breached with only O(log _2psi ) range queries, where psi = N{/}H. To address this lacuna, we present SPLIT, a new encryption scheme where the adversary requires exponentially more—{mathbf{O}}(psi )—range queries to breach the interval constraint and can therefore be easily detected by standard auditing mechanisms. The novel aspect of SPLIT is that each value appearing in a range-sensitive column is first segmented into two parts. These segmented parts are then independently encrypted using a layered composition of a secure block cipher with the order-preserving encryption and prefix-preserving encryption schemes, and the resulting ciphertexts are stored in separate tables. At query processing time, range predicates are rewritten into an equivalent set of table-specific sub-range predicates, and the disjoint union of their results forms the query answer. A detailed evaluation of SPLIT on benchmark database queries indicates that its execution times are well within a factor of two of the corresponding plaintext times, testifying its efficiency in resisting active adversaries.

EXCHANge: Securing IoT via channel anonymity

Establishing confidentiality between communicating peers is still an issue in contexts where solutions based on asymmetric keys are not viable, such as in dynamic Internet of Things (IoT) systems made up of heterogeneous and resource constrained devices.From the current literature, channel anonymity emerges as a promising methodology able to support key-establishment protocols. But, to the best of authors’ knowledge, no works already demonstrated its practical adoption over a concrete communication technology. To bridge this gap, we experimentally show that a lightweight key-establishment protocol based on channel anonymity is viable.The contributions of this work are mainfold. First, we introduce EXCHANge, a protocol that achieves key-establishment exploiting channel anonymity despite the presence of either a passive or active global-eavesdropper adversary. Second, we evaluate the performance of EXCHANge through an extensive experimental campaign involving real world IoT devices (OpenMote-CC2538). Our results demonstrate that the proposed solution introduces a limited overhead, thus being able to meet the requirements of resource constrained devices Finally, we experimentally demonstrate the security of the EXCHANge protocol against passive and active adversaries. Overall, this paper proves that channel anonymity can be a powerful tool in the IoT setting, to achieve a secure, effective, and efficient key-establishment.

Anonymous transmission in a noisy quantum network using the W state

We consider the task of anonymously transmitting a quantum message in a network. We present a protocol that accomplishes this task using the W state and we analyze its performance in a quantum network where some form of noise is present. We then compare the performance of our protocol with some of the existing protocols developed for the task of anonymous transmission. We show that, in many regimes, our protocol tolerates more noise and achieves higher fidelities of the transmitted quantum message than the other ones. Furthermore, we demonstrate that our protocol tolerates one non-responsive node. We prove the security of our protocol in a semi-active adversary scenario, meaning that we consider an active adversary and a trusted source.

FAS: Forward secure sequential aggregate signatures for secure logging

Audit logs are an elemental part of computer systems due to their forensic value. Safeguarding audit logs on a physically unprotected machine are a challenging work, especially in the presence of active adversaries. Forward security is necessary for such a logging system. In this paper, we propose a new Forward secure sequential Aggregate Signature (FAS) scheme with optimal storage and communication for keys and signatures. To the best of our knowledge, our FAS scheme is the only scheme that has constant-sized public and secret keys as well as constant-sized aggregate signatures. Our proposed scheme supports aggregation of consecutive signatures by a third party. We prove the security of our proposed scheme under the hardness of factoring assumption, in the random oracle model.

Efficient privacy-preserving group-nearest-neighbor queries with the presence of active adversaries

Location-based services (LBSs) allow users to ask location-dependent queries and receive information based on their location. A group of users can send a group-nearest-neighbor (GNN) query in order to receive a Point Of Interest (POI). This POI in turn shows a point which is the minimum distance from all members of the group. To benefit from these services, it is important to preserve the location privacy of each group user from others in the group (Intragroup location privacy) as well as from anyone outside of the group, including the LBS, (Intergroup location privacy). It may also be necessary to protect the location privacy of the resulting POI from the LBS and other possible attackers. In this paper, we propose two different privacy-preserving protocols for finding the exact answer to a GNN query among a set of returned POIs. The first protocol assumes a semi-honest model while the second one works in a malicious model. The proposed protocols are based on the Anonymous Veto network and Burmester–Desmedt key establishment protocols. The security analysis shows that the proposed protocols provide both Intragroup and Intergroup location privacy; they also protect the location privacy of the resulting POI and are resistant to collusion and multi-point aggregate distance attacks. The performed analyses indicate that they incur a constant computation cost per user and are efficient in terms of computation and communication costs.

When Good Machine Learning Leads to Bad Security

While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing traditional rule based systems, owing to their ability to generalize and deal with large scale attacks which are not seen before. However, the naive transfer of machine learning principles to the domain of security needs to be taken with caution. Machine learning was not designed with security in mind and as such is prone to adversarial manipulation and reverse engineering. While most data based learning models rely on a static assumption of the world, the security landscape is one that is especially dynamic, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the "Dynamic Adversarial Mining" problem, and this paper provides motivation and foundation for this new interdisciplinary area of research, at the crossroads of machine learning, cybersecurity, and streaming data mining.

Age-based anonymity: a randomized routing approach to communication unobservability

Providing anonymous communication on networks of interconnected computers is an active area of research which aims to enhance the privacy of the users of such networks. Communication unobservability, stronger property compared to anonymity, attempts to guarantee that legitimate messages are not discernible from dummy traffic. A network with an active global adversary is one which it is assumed that all nodes in the network are potentially being monitored at all times, and also that at any time any node could be an adversary. This paper introduces a set of anonymous system design requirements for providing enhanced communication unobservability. A new anonymous networking system was designed based on these requirements to provide both sender and receiver anonymity. The proposed system has a structured peer-to-peer network architecture and a randomized routing algorithm to obfuscate the detection of communication paths and the message routing patterns. An age-based method is proposed to prevent even the first node after the sender from identifying the original sender. A simulation program was designed and implemented to test the proposed system. The effect of different parameters on the proposed algorithm is demonstrated using a simulation program.

Keyless Authentication and Authenticated Capacity

We consider the problem of keyless message authentication over noisy channels in the presence of an active adversary. Different from the existing models, in our model, the legitimate users do not have any pre-shared key for authentication. Instead, we use the noisy channel connecting the legitimate users for authentication. The main idea is to utilize the noisy channel connecting the legitimate users to distinguish a legitimate message from a fake message, by generating an output at the receiver that is difficult for the adversary to replicate through its noisy channel. By interpreting the message authentication as a hypothesis testing problem, we investigate the authentication exponent and the authenticated channel capacity of the noisy channel. In the authentication exponent problem, for a given message rate, we investigate the speed at which the optimal successful attack probability can be driven to zero. We fully characterize the authentication exponent for the zero-rate message case and provide both an upper bound and a lower bound on the exponent for the non-zero message rate case. In the authenticated capacity problem, we study the largest data transmission rate under which the attacker’s optimal successful attack probability can still be made arbitrarily small. We establish an all or nothing result. In particular, we show that the authenticated channel capacity is the same as the classic channel capacity if a simulatability condition is not satisfied, while the authenticated capacity will be zero if this condition is satisfied. We also provide efficient algorithms to check this condition. We further show that our results are robust to modeling uncertainties about the eavesdropper’s channels.

A survey of game theoretic approach for adversarial machine learning

The field of machine learning is progressing at a faster pace than ever before. Many organizations leverage machine learning tools to extract useful information from a massive amount of data. In particular, machine learning finds its application in cybersecurity that begins to enter the age of automation. However, machine learning applications in cybersecurity face unique challenges other domains rarely do—attacks from active adversaries. Problems in areas such as intrusion detection, banking fraud detection, spam filtering, and malware detection have to face challenges of adversarial attacks that modify data so that malicious instances would evade detection by the learning systems. The adversarial learning problem naturally resembles a game between the learning system and the adversary. In such a game, both players would attempt to play their best strategies against each other while maximizing their own payoffs. To solve the game, each player would search for an optimal strategy against the opponent based on the prediction of the opponent's strategy choice. The problem becomes even more complicated in settings where the learning system may have to deal with many adversaries of unknown types. Applying game‐theoretic approach, robust learning techniques have been developed to specifically address adversarial attacks and the preliminary results are promising. In this review, we summarize these results.This article is categorized under:Technologies > Machine LearningFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Efficient identity-based authenticated key agreement protocol with provable security for vehicular ad hoc networks

In vehicular ad hoc networks, establishing a secure channel between any two vehicles is fundamental. Authenticated key agreement is a useful mechanism, which can be used to negotiate a shared key for secure data transmission between authentic vehicles in vehicular ad hoc networks. Among the existing identity-based two-party authenticated key agreement protocols without pairings, there are only a few protocols that provide provable security in strong security models such as the extended Canetti–Krawczyk model. This article presents an efficient pairing-free identity-based one-round two-party authenticated key agreement protocol with provable security, which is more suitable for real-time application environments with highly dynamic topology such as vehicular ad hoc networks than the existing identity-based two-party authenticated key agreement protocols. The proposed protocol is proven secure under the passive and active adversaries in the extended Canetti–Krawczyk model based on the Gap Diffie–Hellman assumption. The proposed protocol can capture all essential security attributes including known-session key security, perfect forward secrecy, basic impersonation resistance, key compromise impersonation resistance, unknown key share resistance, no key control, and ephemeral secrets reveal resistance. Compared with the existing identity-based two-party authenticated key agreement protocols, the proposed protocol is superior in terms of computational cost and running time while providing higher security.

Cyber-physical systems information gathering: A smart home case study

With the growth in the use of Cyber-Physical Systems, such as Internet of Things (IoT) devices, there is a corresponding increase in the potential attack footprint of personal and corporate users. In this paper, we explore the potential for exploiting information retrieved from two IoT devices which, seemingly, are unlikely to store substantial amounts of data. We specifically focus on prominent smart home devices for the purpose of obtaining compromising information. We undertake a collection and analysis process, constrained by the limitations placed upon three types of adversaries, namely: forensic passive, forensic active and real-time active. The former two adversaries aim to comply with the requirements of forensic soundness, whereas the real-time active adversary does not have these constraints and therefore more closely models a malicious real-world attacker. The findings show that a variety of device data is available to even the passive adversary, and this data can be used to determine the actions and/or presence of an individual at a given time based on their interactions with the IoT device. These interactions can be both user initiated (e.g. powering on or off a switch or light) and device initiated (e.g. background polling).

Information-Theoretically Secure Erasure Codes for Distributed Storage

Repair operations in erasure-coded distributed storage systems involve a lot of data movement. This can potentially expose data to malicious acts of passive eavesdroppers or active adversaries, putting security of the system at risk. This paper presents coding schemes and repair algorithms that ensure security of the data in the presence of passive eavesdroppers and active adversaries while maintaining high availability, reliability, and resource efficiency in the system. The proposed codes are optimal in that they meet previously proposed lower bounds on storage and network-bandwidth requirements for a wide range of system parameters. The results thus establish the secure storage capacity of such systems. The proposed codes are based on an optimal class of codes called product-matrix codes. The constructions presented for security from active adversaries provide an additional appealing feature of “on-demand security,” where the desired level of security can be chosen separately for each instance of repair, and the proposed algorithms remain optimal simultaneously for all possible security levels. This paper also provides necessary and sufficient conditions governing the transformation of any (non-secure) code into one providing on-demand security.

How to manage resources to provide physical layer security: Active versus passive adversary?

This paper studies the required physical (e.g. relays and jammers) and radio resources (e.g. power) to provide physical layer security for relay and friendly jammer assisted multiple-input and single-output transmissions in the presence of multiple active and passive adversaries. The passive adversaries are half duplex and only able to overhear the transmissions from the legitimate transmitter to the legitimate receiver, while the active adversaries are full duplex and able to jam and eavesdrop simultaneously. Since the channel information between adversaries and other nodes are uncertain, robust optimization methods are considered. In this regard, the main aim is to maximize the worst case secrecy rate subject to the normalized transmit power constraints of legitimate transmitter, friendly jammer and relay, and channel state information uncertainty constraints. Through several examples, we then investigate the required increase in physical and radio resources to maintain secure communication when passive adversaries upgrades themselves to active adversaries.

Security and robustness of a modified ElGamal encryption scheme

In this paper, we propose a new and practical variant of ElGamal encryption which is secure against every passive and active adversary. Under the hardiness of the decisional Diffie-Hellman assumption, we can prove that the proposed scheme is secure against an adaptive chosen ciphertext attacks in the standard model. Such security verifies not only the confidentiality but also verifies the integrity and the authentication of communications. We display that the modified scheme furthermore achieves anonymity as well as strong robustness.