Biblio

Detection of previously unknown attacks and malicious messages is a challenging problem faced by modern network intrusion detection systems. Anomaly-based solutions, despite being able to detect unknown attacks, have not been used often in practice due to their high false positive rate, and because they provide little actionable information to the security officer in case of an alert. In this paper we focus on intrusion detection in industrial control systems networks and we propose an innovative, practical and semantics-aware framework for anomaly detection. The network communication model and alerts generated by our framework are userunderstandable, making them much easier to manage. At the same time the framework exhibits an excellent tradeoff between detection rate and false positive rate, which we show by comparing it with two existing payload-based anomaly detection methods on several ICS datasets.

Security vulnerability assessment is an important process that must be conducted against any system before the deployment, and emerging technologies are no exceptions. Software-Defined Networking (SDN) has aggressively evolved in the past few years and is now almost at the early adoption stage. At this stage, the attack surface of SDN should be thoroughly investigated and assessed in order to mitigate possible security breaches against SDN. Inspired by the necessity, we reveal three attack scenarios that leverage SDN application to attack SDNs, and test the attack scenarios against three of the most popular SDN controllers available today. In addition, we discuss the possible defense mechanisms against such application-originated attacks.

We show that elliptic-curve cryptography implementations on mobile devices are vulnerable to electromagnetic and power side-channel attacks. We demonstrate full extraction of ECDSA secret signing keys from OpenSSL and CoreBitcoin running on iOS devices, and partial key leakage from OpenSSL running on Android and from iOS's CommonCrypto. These non-intrusive attacks use a simple magnetic probe placed in proximity to the device, or a power probe on the phone's USB cable. They use a bandwidth of merely a few hundred kHz, and can be performed cheaply using an audio card and an improvised magnetic probe.

Embedded Systems (ES) are an integral part of Cyber-Physical Systems (CPS), the Internet of Things (IoT), and consumer devices like smartphones. ES often have limited resources, and - if used in CPS and IoT - have to satisfy real time requirements. Therefore, ES rarely employ the security measures established for computer systems and networks. Due to the growth of both CPS and IoT it is important to identify ongoing attacks on ES without interfering with realtime constraints. Furthermore, security solutions that can be retrofit to legacy systems are desirable, especially when ES are used in Industrial Control Systems (ICS) that often maintain the same hardware for decades. To tackle this problem, several researchers have proposed using side-channels (i.e., physical emanations accompanying cyber processes) to detect such attacks. While prior work focuses on the anomaly detection approach, this might not always be sufficient, especially in complex ES whose behavior depends on the input data. In this paper, we determine whether one of the most common attacks - a buffer overflow attack - generates distinct side-channel signatures if executed on a vulnerable ES. We only consider the power consumption side-channel. We collect and analyze power traces from normal program operation and four cases of buffer overflow attack categories: (i) crash program execution, (ii) injection of executable code, (iii) return to existing function, and (iv) Return Oriented Programming (ROP) with gadgets. Our analysis shows that for some of these cases a power signature-based detection of a buffer overflow attack is possible.

Riding on the success of SDN for enterprise and data center networks, recently researchers have shown much interest in applying SDN for critical infrastructures. A key concern, however, is the vulnerability of the SDN controller as a single point of failure. In this paper, we develop a cyber-physical simulation platform that interconnects Mininet (an SDN emulator), hardware SDN switches, and PowerWorld (a high-fidelity, industry-strength power grid simulator). We report initial experiments on how a number of representative controller faults may impact the delay of smart grid communications. We further evaluate how this delay may affect the performance of the underlying physical system, namely automatic gain control (AGC) as a fundamental closed-loop control that regulates the grid frequency to a critical nominal value. Our results show that when the fault-induced delay reaches seconds (e.g., more than four seconds in some of our experiments), degradation of the AGC becomes evident. Particularly, the AGC is most vulnerable when it is in a transient following say step changes in loading, because the significant state fluctuations will exacerbate the effects of using a stale system state in the control.

In classical runtime analysis it has been observed that certain working principles of an evolutionary algorithm cannot be understood by only looking at the asymptotic order of the runtime, but that more precise estimates are needed. In this work we demonstrate that the same observation applies to black-box complexity analysis. We prove that the unary unbiased black-box complexity of the classic OneMax function class is n ln(n) – cn ± o(n) for a constant c between 0.2539 and 0.2665. Our analysis yields a simple (1+1)-type algorithm achieving this runtime bound via a fitness-dependent mutation strength. When translated into a fixed-budget perspective, our algorithm with the same budget computes a solution that asymptotically is 13% closer to the optimum (given that the budget is at least 0.2675n).

This paper establishes a new framework for electrical cyber-physical systems (ECPSs). The communication network is designed by the characteristics of a power grid. The interdependent relationship of communication networks and power grids is described by data-uploading channels and commands-downloading channels. Control strategies (such as load shedding and relay protection) are extended to this new framework for analyzing the performance of ECPSs under several attack scenarios. The fragility of ECPSs under cyber attacks (DoS attack and false data injection attack) and the effectiveness of relay protection policies are verified by experimental results.

It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. This is the quantum analogue of the problem of estimating a probability distribution given some number of samples. Previously, it was known only that estimating states to error є in trace distance required O(dr2/є2) copies for a d-dimensional density matrix of rank r. Here, we give a measurement scheme (POVM) that uses O( (dr/ δ ) ln(d/δ) ) copies to estimate ρ to error δ in infidelity. This implies O( (dr / є2)· ln(d/є) ) copies suffice to achieve error є in trace distance. For fixed d, our measurement can be implemented on a quantum computer in time polynomial in n. We also use the Holevo bound from quantum information theory to prove a lower bound of Ω(dr/є2)/ log(d/rє) copies needed to achieve error є in trace distance. This implies a lower bound Ω(dr/δ)/log(d/rδ) for the estimation error δ in infidelity. These match our upper bounds up to log factors. Our techniques can also show an Ω(r2d/δ) lower bound for measurement strategies in which each copy is measured individually and then the outcomes are classically post-processed to produce an estimate. This matches the known achievability results and proves for the first time that such “product” measurements have asymptotically suboptimal scaling with d and r.

It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. This is the quantum analogue of the problem of estimating a probability distribution given some number of samples. Previously, it was known only that estimating states to error є in trace distance required O(dr2/є2) copies for a d-dimensional density matrix of rank r. Here, we give a measurement scheme (POVM) that uses O( (dr/ δ ) ln(d/δ) ) copies to estimate ρ to error δ in infidelity. This implies O( (dr / є2)· ln(d/є) ) copies suffice to achieve error є in trace distance. For fixed d, our measurement can be implemented on a quantum computer in time polynomial in n. We also use the Holevo bound from quantum information theory to prove a lower bound of Ω(dr/є2)/ log(d/rє) copies needed to achieve error є in trace distance. This implies a lower bound Ω(dr/δ)/log(d/rδ) for the estimation error δ in infidelity. These match our upper bounds up to log factors. Our techniques can also show an Ω(r2d/δ) lower bound for measurement strategies in which each copy is measured individually and then the outcomes are classically post-processed to produce an estimate. This matches the known achievability results and proves for the first time that such “product” measurements have asymptotically suboptimal scaling with d and r.

In this paper we present an approach to implement security as a Virtualized Network Function (VNF) that is implemented within a Software-Defined Infrastructure (SDI). We present a scalable, flexible, and seamless design for a Deep Packet Inspection (DPI) system for network intrusion detection and prevention. We discuss how our design introduces significant reductions in both capital and operational expenses (CAPEX and OPEX). As proof of concept, we describe an implementation for a modular security solution that uses the SAVI SDI testbed to first detect and then block an attack or to re-direct it to a honey-pot for further analysis. We discuss our testing methodology and provide measurement results for the test cases where an application faces various security attacks.

The vulnerabilities in today's supply chain have raised serious concerns about the security and trustworthiness of electronic components and systems. Testing for device provenance, detection of counterfeit integrated circuits/systems, and traceability are challenging issues to address. In this paper, we develop a novel RFID-based system suitable for electronic component and system Counterfeit detection and System Traceability called CST. CST is composed of different types of on-chip sensors and in-system structures that provide the information needed to detect multiple counterfeit IC types (recycled, cloned, etc.), verify the authenticity of the system with some degree of confidence, and track/identify boards. Central to CST is an RFID tag employed as storage and a channel to read the information from different types of chips on the printed circuit board (PCB) in both power-off and power-on scenarios. Simulations and experimental results using Spartan 3E FPGAs demonstrate the effectiveness of this system. The efficiency of the radio frequency (RF) communication has also been verified via a PCB prototype with a printed slot antenna.

This paper presents a six-layer Aluminum Industry 4.0 architecture for the aluminum production and full lifecycle supply chain management. It integrates a series of innovative technologies, including the IoT sensing physical system, industrial cloud platform for data management, model-driven and big data driven analysis & decision making, standardization & securitization intelligent control and management, as well as visual monitoring and backtracking process etc. The main relevant control models are studied. The applications of real-time accurate perception & intelligent decision technology in the aluminum electrolytic industry are introduced.

The study of the characteristics of disturbance propagation in the interconnected power networks is of great importance to control the spreading of disturbance and improve the security level of power systems. In this paper, the characteristics of disturbance propagation in a one-dimensional chained power network are studied from the electromechanical wave point of view. The electromechanical wave equation is built based on the discrete inertia model of power networks. The wave transfer function which can describe the variations of amplitude and the phase is derived. Then, the propagation characteristics of different frequency disturbances are analyzed. The corner frequency of the discrete inertia model is proposed. Furthermore, the frequency dispersion and local oscillation are considered and their relationships with the corner frequency are revealed as well. Computer simulations for a 50 generators chained network are carried out to verify the propagation characteristics of disturbances with different frequencies.

RFID-enabled product supply chain visibility is usually implemented by building up a view of the product history of its activities starting from manufacturing or even earlier with a dynamically updated e-pedigree for track-and-trace, which is examined and authenticated at each node of the supply chain for data consistence with the pre-defined one. However, while effectively reducing the risk of fakes, this visibility can't guarantee that the product is authentic without taking further security measures. To the best of our knowledge, this requires deeper understandings on associations of object events with the counterfeiting activities, which is unfortunately left blank. In this paper, the taxonomy of counterfeiting possibilities is initially developed and analyzed, the structure of EPC-based events is then re-examined, and an object-centric coding mechanism is proposed to construct the object-based event “pedigree” for such event exception detection and inference. On this basis, the system architecture framework to achieve the objectivity of object event visibility for anti-counterfeiting is presented, which is also applicable to other aspects of supply chain management.

The Internet of Things (IoT), an emerging global network of uniquely identifiable embedded computing devices within the existing Internet infrastructure, is transforming how we live and work by increasing the connectedness of people and things on a scale that was once unimaginable. In addition to increased communication efficiency between connected objects, the IoT also brings new security and privacy challenges. Comprehensive measures that enable IoT device authentication and secure access control need to be established. Existing hardware, software, and network protection methods, however, are designed against fraction of real security issues and lack the capability to trace the provenance and history information of IoT devices. To mitigate this shortcoming, we propose an RFID-enabled solution that aims at protecting endpoint devices in IoT supply chain. We take advantage of the connection between RFID tag and control chip in an IoT device to enable data transfer from tag memory to centralized database for authentication once deployed. Finally, we evaluate the security of our proposed scheme against various attacks.

The QR codes have gained wide popularity in mobile marketing and advertising campaigns. However, the hidden security threat on the involved information system might endanger QR codes' success, and this issue has not been adequately addressed. In this paper we propose to examine the life cycle of a redesigned QR code ecosystem to identify the possible security risks. On top of this examination, we further propose standard changes to enhance security through a digital signature mechanism.

An enormous number of images are currently shared through social networking services such as Facebook. These images usually contain appearance of people and may violate the people's privacy if they are published without permission from each person. To remedy this privacy concern, visual privacy protection, such as blurring, is applied to facial regions of people without permission. However, in addition to image quality degradation, this may spoil the context of the image: If some people are filtered while the others are not, missing facial expression makes comprehension of the image difficult. This paper proposes an image melding-based method that modifies facial regions in a visually unintrusive way with preserving facial expression. Our experimental results demonstrated that the proposed method can retain facial expression while protecting privacy.

This paper proposes an improved mesh simplification algorithm based on quadric error metrics (QEM) to efficiently processing the huge data in 3D image processing. This method fully uses geometric information around vertices to avoid model edge from being simplified and to keep details. Meanwhile, the differences between simplified triangular meshes and equilateral triangles are added as weights of errors to decrease the possibilities of narrow triangle and then to avoid the visual mutation. Experiments show that our algorithm has obvious advantages over the time cost, and can better save the visual characteristics of model, which is suitable for solving most image processing, that is, "Real-time interactive" problem.

In this paper, we propose a novelregularization term for super-resolution by combining a bilateral total variation (BTV) regularizer and a sparsity prior model on the image. The term is composed of the weighted least squares minimization and the bilateral filter proposed by Elad, but adding an ℓ1/2 regularizer. It is referred to as ℓ1/2-BTV. The proposed algorithm serves to restore image details more precisely and eliminate image noise more effectively by introducing the sparsity of the ℓ1/2 regularizer into the traditional bilateral total variation (BTV) regularizer. Experiments were conducted on both simulated and real image sequences. The results show that the proposed algorithm generates high-resolution images of better quality, as defined by both de-noising and edge-preservation metrics, than other methods.

A number of blind Image Quality Evaluation Metrics (IQEMs) for Unmanned Aerial Vehicle (UAV) photograph application are presented. Nowadays, the visible light camera is widely used for UAV photograph application because of its vivid imaging effect; however, the outdoor environment light will produce great negative influences on its imaging output unfortunately. In this paper, to conquer this problem above, we design and reuse a series of blind IQEMs to analyze the imaging quality of UAV application. The Human Visual System (HVS) based IQEMs, including the image brightness level, the image contrast level, the image noise level, the image edge blur level, the image texture intensity level, the image jitter level, and the image flicker level, are all considered in our application. Once these IQEMs are calculated, they can be utilized to provide a computational reference for the following image processing application, such as image understanding and recognition. Some preliminary experiments for image enhancement have proved the correctness and validity of our proposed technique.

The paper presents a joint optimization algorithm for coverage and capacity in heterogeneous cellular networks. A joint optimization objective related to capacity loss considering both coverage hole and overlap area based on power density distribution is proposed. The optimization object is a NP problem due to that the adjusting parameters are mixed with discrete and continuous, so the bacterial foraging (BF) algorithm is improved based on network performance analysis result to find a more effective direction than randomly selected. The results of simulation show that the optimization object is feasible gains a better effect than traditional method.

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.

Because of poor performance of heuristic algorithms on virtual machine placement problem in cloud environments, a multi-objective constraint optimization model of virtual machine placement is presented, which taking energy consumption and resource wastage as the objective. We solve the model based on the proposed discrete firefly algorithm. It takes firefly's location as the placement result, brightness as the objective value. Its movement strategy makes darker fireflies move to brighter fireflies in solution space. The continuous position after movement is discretized by the proposed discrete strategy. In order to speed up the search for solution, the local search mechanism for the optimal solution is introduced. The experimental results in OpenStack cloud platform show that the proposed algorithm makes less energy consumption and resource wastage compared with other algorithms.

Twitter provides a public streaming API that is strictly limited, making it difficult to simultaneously achieve good coverage and relevance when monitoring tweets for a specific topic of interest. In this paper, we address the tweet acquisition challenge to enhance monitoring of tweets based on the client/application needs in an online adaptive manner such that the quality and quantity of the results improves over time. We propose a Tweet Acquisition System (TAS), that iteratively selects phrases to track based on an explore-exploit strategy. Our experimental studies show that TAS significantly improves recall of relevant tweets and the performance improves when the topics are more specific.

The ExFAT file system is for large capacity flash memory medium. On the base of analyzing the characteristics of ExFAT file system, this paper presents a model of electronic data recovery forensics and judicial Identification based on ExFAT. The proposed model aims at different destroyed situation of data recovery medium. It uses the file location algorithm, file character code algorithm, document fragment reassembly algorithm for accurate, efficient recovery of electronic data for forensics and judicial Identification. The model implements the digital multi-signature, process monitoring, media mirror and Hash authentication in the data recovery process to improve the acceptability, weight of evidence and Legal effect of the electronic data in the lawsuit. The experimental results show that the model has good work efficiency based on accuracy.