Biblio

This paper discusses formulations and algorithms which allow a number of agents to collectively solve problems involving both (non-convex) minimization and (concave) maximization operations. These problems have a number of interesting applications in information processing and machine learning, and in particular can be used to model an adversary learning problem called network data poisoning. We develop a number of algorithms to efficiently solve these non-convex min-max optimization problems, by combining techniques such as gradient tracking in the decentralized optimization literature and gradient descent-ascent schemes in the min-max optimization literature. Also, we establish convergence to a first order stationary point under certain conditions. Finally, we perform experiments to demonstrate that the proposed algorithms are effective in the data poisoning attack.

Reverse engineers are in high demand in digital forensics for their ability to investigate malicious cyberspace threats. This group faces unique challenges due to the security-intensive environment, such as working in isolated networks, a limited ability to share files with others, immense time pressure, and a lack of cognitive support tools supporting the iterative exploration of binary executables. This paper presents an exploratory study that interviewed experienced reverse engineers' work processes, tools, challenges, and visualization needs. The findings demonstrate that engineers have difficulties managing hypotheses, organizing results, and reporting findings during their analysis. By considering the provenance support techniques of existing research in other domains, this study contributes new insights about the needs and opportunities for reverse engineering provenance tools.

This paper considers the problem of the quickest detection of a change in distribution while taking privacy considerations into account. Our goal is to sanitize the signal to satisfy information privacy requirements while being able to detect a change quickly. We formulate the privacy-aware quickest change detection (QCD) problem by including a privacy constraint to Lorden's minimax formulation. We show that the Generalized Likelihood Ratio (GLR) CuSum achieves asymptotic optimality with a properly designed sanitization channel and formulate the design of this sanitization channel as an optimization problem. For computational tractability, a continuous relaxation for the discrete counting constraint is proposed and the augmented Lagrangian method is applied to obtain locally optimal solutions.

Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.

The limited output of various drives means a challenge in controller design whenever the acceleration need of the "nominal trajectory to be tracked" temporarily exceeds the abilities of the saturated control system. The prevailing control design methods can tackle this problem either in a single theoretical step or in two consecutive steps. In this latter case in the first step the design happens without taking into account the actuator constraints, then apply a saturation compensator if the phenomenon of windup is observed. In the Fixed Point Iteration- based Adaptive Control (FPIAC) that has been developed as an alternative of the Lyapunov function-based approach the actuator saturation causes problems in its both elementary levels: in the kinematic/kinetic level where the desired acceleration is calculated, and in the iterative process that compensates the effects of modeling errors of the dynamic system under control and that of the external disturbances. The here presented approach tackles this problem in both levels by relatively simple considerations. To illustrate the method's efficiency simulation investigations were done in the FPIAC control of a modification of the van der Pol oscillator to which an additional strongly nonlinear term was added.

Endpoint Detection and Response (EDR) tools provide visibility into sophisticated intrusions by matching system events against known adversarial behaviors. However, current solutions suffer from three challenges: 1) EDR tools generate a high volume of false alarms, creating backlogs of investigation tasks for analysts; 2) determining the veracity of these threat alerts requires tedious manual labor due to the overwhelming amount of low-level system logs, creating a "needle-in-a-haystack" problem; and 3) due to the tremendous resource burden of log retention, in practice the system logs describing long-lived attack campaigns are often deleted before an investigation is ever initiated.This paper describes an effort to bring the benefits of data provenance to commercial EDR tools. We introduce the notion of Tactical Provenance Graphs (TPGs) that, rather than encoding low-level system event dependencies, reason about causal dependencies between EDR-generated threat alerts. TPGs provide compact visualization of multi-stage attacks to analysts, accelerating investigation. To address EDR's false alarm problem, we introduce a threat scoring methodology that assesses risk based on the temporal ordering between individual threat alerts present in the TPG. In contrast to the retention of unwieldy system logs, we maintain a minimally-sufficient skeleton graph that can provide linkability between existing and future threat alerts. We evaluate our system, RapSheet, using the Symantec EDR tool in an enterprise environment. Results show that our approach can rank truly malicious TPGs higher than false alarm TPGs. Moreover, our skeleton graph reduces the long-term burden of log retention by up to 87%.

Certificate Authorities (CAs) are important components for digital certificate issuances in Public Key Infrastructure(PKI). However, current CAs have some intrinsic weaknesses due to the CA-centric implementation. And when browser and operating system vendors contain a CA in the software, they place complete trust in the CA. In this paper, we utilize natural characteristics of tamper-proof and transparency of smart contracts in blockchain platforms to design an independent entity, named the CA proxy, to manage life cycle of digital certificates. This management will achieve the certificate transparency. We propose a new system architecture easy to integrate the CA proxy with current CAs through applying the blockchain oracle service. In this architecture, the CA proxy, CAs, and even professional identity verification parties can accomplish life cycle management of certificates, signature of certificates, identity verification for certificates correspondingly. The achievement of the certificate transparency through life cycle management of digital certificates in blockchain platforms, when compared with traditional CAs, solves traditional CAs' trust model weaknesses and improve the security.

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.

By analogy to nature, sight is the main integral component of robotic complexes, including unmanned vehicles. In this connection, one of the urgent tasks in the modern development of unmanned vehicles is the solution to the problem of providing security for new advanced systems, algorithms, methods, and principles of space navigation of robots. In the paper, we present an approach to the protection of machine vision systems based on technologies of deep learning. At the heart of the approach lies the “Feature Squeezing” method that works on the phase of model operation. It allows us to detect “adversarial” examples. Considering the urgency and importance of the target process, the features of unmanned vehicle hardware platforms and also the necessity of execution of tasks on detecting of the objects in real-time mode, it was offered to carry out an additional simple computational procedure of localization and classification of required objects in case of crossing a defined in advance threshold of “adversarial” object testing.

The autonomy of intelligent systems and their safety rely on their ability for local decision making based on collected environmental information. This is even more for cyber-physical systems running safety critical activities. While this intelligence is partial and fragmented, and cognitive techniques are of limited maturity, the decision function must produce results whose validity and scope must be weighted in light of the underlying assumptions, unavoidable uncertainty and hypothetical safety limitation. Besides the cognitive techniques dependability, it is about the assurance level of the decision self-making. Beyond the pure decision-making capabilities of the autonomous intelligent system, we need techniques that guarantee the system assurance required for the intended use. Security mechanisms for cognitive systems may be consequently tightly intricated. We propose a trustworthiness module which is part of the system and its resulting safety. In this paper, we briefly review the state of the art regarding the dependability of cognitive techniques, the assurance level definition in this context, and related engineering practices. We elaborate regarding the design of autonomous intelligent systems safety, then we discuss its security design and approaches for the mitigation of safety violations by the cognitive functions.

Today, Internet of Things (IoT) devices mostly operate in enclosed, proprietary environments. To unfold the full potential of IoT applications, a unifying and permissionless environment is crucial. All IoT devices, even unknown to each other, would be able to trade services and assets across various domains. In order to realize those applications, uniquely resolvable identities are essential. However, quantifiable trust in identities and their authentication are not trivially provided in such an environment due to the absence of a trusted authority. This research presents a new identity and trust framework for IoT devices, based on Distributed Ledger Technology (DLT). IoT devices assign identities to themselves, which are managed publicly and decentralized on the DLT's network as Self Sovereign Identities (SSI). In addition to the Identity Management System (IdMS), the framework provides a Web of Trust (WoT) approach to enable automatic trust rating of arbitrary identities. For the framework we used the IOTA Tangle to access and store data, achieving high scalability and low computational overhead. To demonstrate the feasibility of our framework, we provide a proof-of-concept implementation and evaluate the set objectives for real world applicability as well as the vulnerability against common threats in IdMSs and WoTs.

We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks.

The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the prevailing approach to process non-English text, applying MT on hacker forum text results in mistranslations. In this study, we draw upon Long-Short Term Memory (LSTM), Cross-Lingual Knowledge Transfer (CLKT), and Generative Adversarial Networks (GANs) principles to design a novel Adversarial CLKT (A-CLKT) approach. A-CLKT operates on untranslated text to retain the original semantics of the language and leverages the collective knowledge about cyber threats across languages to create a language invariant representation without any manual feature engineering or external resources. Three experiments demonstrate how A-CLKT outperforms state-of-the-art machine learning, deep learning, and CLKT algorithms in identifying cyber-threats in French and Russian forums.

The existing anonymized differential privacy model adopts a unified anonymity method, ignoring the difference of personal privacy, which may lead to the problem of excessive or insufficient protection of the original data [1]. Therefore, this paper proposes a personalized k-anonymity model for tuples (PKA) and proposes a differential privacy data publishing algorithm (DPPA) based on personalized anonymity, firstly based on the tuple personality factor set by the user in the original data set. The values are classified and the corresponding privacy protection relevance is calculated. Then according to the tuple personality factor classification value, the data set is clustered by clustering method with different anonymity, and the quasi-identifier attribute of each cluster is aggregated and noise-added to realize anonymized differential privacy; finally merge the subset to get the data set that meets the release requirements. In this paper, the correctness of the algorithm is analyzed theoretically, and the feasibility and effectiveness of the proposed algorithm are verified by comparison with similar algorithms.

We propose a novel attestation architecture for the Internet of Things (IoT). Our distributed attestation network (DAN) utilizes blockchain technology to store and share device information. We present the design of this new attestation architecture as well as a prototype system chosen to emulate an IoT deployment with a network of Raspberry Pi, Infineon TPMs, and a Hyperledger Fabric blockchain.

The mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. This paper introduces a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to improve the information propagation delay in the Bitcoin network.

Bluetooth Classic (BT) remains the de facto connectivity technology in car stereo systems, wireless headsets, laptops, and a plethora of wearables, especially for applications that require high data rates, such as audio streaming, voice calling, tethering, etc. Unlike in Bluetooth Low Energy (BLE), where address randomization is a feature available to manufactures, BT addresses are not randomized because they are largely believed to be immune to tracking attacks. We analyze the design of BT and devise a robust de-anonymization technique that hinges on the apparently benign information leaking from frame encoding, to infer a piconet's clock, hopping sequence, and ultimately the Upper Address Part (UAP) of the master device's physical address, which are never exchanged in clear. Used together with the Lower Address Part (LAP), which is present in all frames transmitted, this enables tracking of the piconet master, thereby debunking the privacy guarantees of BT. We validate this attack by developing the first Software-defined Radio (SDR) based sniffer that allows full BT spectrum analysis (79 MHz) and implements the proposed de-anonymization technique. We study the feasibility of privacy attacks with multiple testbeds, considering different numbers of devices, traffic regimes, and communication ranges. We demonstrate that it is possible to track BT devices up to 85 meters from the sniffer, and achieve more than 80% device identification accuracy within less than 1 second of sniffing and 100% detection within less than 4 seconds. Lastly, we study the identified privacy attack in the wild, capturing BT traffic at a road junction over 5 days, demonstrating that our system can re-identify hundreds of users and infer their commuting patterns.

One of the most efficient tool for human face recognition is neural networks. However, the result of recognition can be spoiled by facial expressions and other deviation from the canonical face representation. In this paper, we propose a resampling method of human faces represented by 3D point clouds. The method is based on a non-rigid Iterative Closest Point (ICP) algorithm. To improve the facial recognition performance, we use a combination of the proposed method and convolutional neural network (CNN). Computer simulation results are provided to illustrate the performance of the proposed method.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.

Electronic voting systems have enhanced efficiency in student elections management in universities, supporting such elections to become less expensive, logistically simple, with higher accuracy levels as compared to manually conducted elections. However, e-voting systems that are confined to campus hall voting inhibits access to eligible voters who are away from campus. This study examined the challenges of lack of wide access and impersonation of voter in the student elections of 2018 in Kabarak University. The main objective of this study was therefore to upgrade the offline electronic voting system through developing a secure online voting system and deploying the system for use in the 2019 student elections at Kabarak University. The resultant system and development process employed demonstrate the applicability of a secure online voting not only in the higher education context, but also in other democracies where infusion of online access and authentication in the voting processes is a requisite.

Indoor localization has been a popular research subject in recent years. Usually, object localization using sound involves devices on the objects, acquiring data from stationary sound sources, or by localizing the objects with external sensors when the object generates sounds. Indoor localization systems using microphones have traditionally also used systems with several microphones, setting the limitations on cost efficiency and required space for the systems. In this paper, the goal is to investigate whether it is possible for a stationary system to localize a silent object in a room, with only one microphone and ambient noise as information carrier. A subtraction method has been combined with a fingerprint technique, to define and distinguish the noise absorption characteristic of the silent object in the frequency domain for different object positions. The absorption characteristics of several positions of the object is taken as comparison references, serving as fingerprints of known positions for an object. With the experiment result, the tentative idea has been verified as feasible, and noise signal based lateral localization of silent objects can be achieved.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

With the rapid technological advancement of the universal internet of vehicles (UIoV), it becomes crucial to ensure safe and secure communication over the network, in an effort to achieve the implementation objective of UIoV effectively. A UIoV is characterized by highly dynamic topology, scalability, and thus vulnerable to various types of security and privacy attacks (i.e., replay attack, impersonation attack, man-in-middle attack, non-repudiation, and modification). Since the components of UIoV are constrained by numerous factors (e.g., low memory devices, low power), which makes UIoV highly susceptible. Therefore, existing schemes to address the privacy and security facets of UIoV exhibit an enormous scope of improvement in terms of time complexity and efficiency. This paper presents a lightweight authentication and batch verification scheme (LABVS) for UIoV using a bilinear map and cryptographic operations (i.e., one-way hash function, concatenation, XOR) to minimize the rate of message loss occurred due to delay in response time as in single message verification scheme. Subsequently, the scheme results in a high level of security and privacy. Moreover, the performance analysis substantiates that LABVS minimizes the computational delay and has better performance in the delay-sensitive network in terms of security and privacy as compared to the existing schemes.

Increased peripheral performance is causing strain on the memory subsystem of modern processors. For example, available DRAM throughput can no longer sustain the traffic of a modern network card. Scrambling to deliver the promised performance, instead of transferring peripheral data to and from DRAM, modern Intel processors perform I/O operations directly on the Last Level Cache (LLC). While Direct Cache Access (DCA) instead of Direct Memory Access (DMA) is a sensible performance optimization, it is unfortunately implemented without care for security, as the LLC is now shared between the CPU and all the attached devices, including the network card.In this paper, we reverse engineer the behavior of DCA, widely referred to as Data-Direct I/O (DDIO), on recent Intel processors and present its first security analysis. Based on our analysis, we present NetCAT, the first Network-based PRIME+PROBE Cache Attack on the processor's LLC of a remote machine. We show that NetCAT not only enables attacks in cooperative settings where an attacker can build a covert channel between a network client and a sandboxed server process (without network), but more worryingly, in general adversarial settings. In such settings, NetCAT can enable disclosure of network timing-based sensitive information. As an example, we show a keystroke timing attack on a victim SSH connection belonging to another client on the target server. Our results should caution processor vendors against unsupervised sharing of (additional) microarchitectural components with peripherals exposed to malicious input.

Due to the wide adoption of IoT/CPS systems, embedded devices (IoT frontends) become increasingly connected and mission-critical, which in turn has attracted advanced attacks (e.g., control-flow hijacks and data-only attacks). Unfortunately, IoT backends (e.g., remote controllers or in-cloud services) are unable to detect if such attacks have happened while receiving data, service requests, or operation status from IoT devices (remotely deployed embedded devices). As a result, currently, IoT backends are forced to blindly trust the IoT devices that they interact with.To fill this void, we first formulate a new security property for embedded devices, called "Operation Execution Integrity" or OEI. We then design and build a system, OAT, that enables remote OEI attestation for ARM-based bare-metal embedded devices. Our formulation of OEI captures the integrity of both control flow and critical data involved in an operation execution. Therefore, satisfying OEI entails that an operation execution is free of unexpected control and data manipulations, which existing attestation methods cannot check. Our design of OAT strikes a balance between prover's constraints (embedded devices' limited computing power and storage) and verifier's requirements (complete verifiability and forensic assistance). OAT uses a new control-flow measurement scheme, which enables lightweight and space-efficient collection of measurements (97% space reduction from the trace-based approach). OAT performs the remote control-flow verification through abstract execution, which is fast and deterministic. OAT also features lightweight integrity checking for critical data (74% less instrumentation needed than previous work). Our security analysis shows that OAT allows remote verifiers or IoT backends to detect both controlflow hijacks and data-only attacks that affect the execution of operations on IoT devices. In our evaluation using real embedded programs, OAT incurs a runtime overhead of 2.7%.