Biblio

Quickest detection of false data injection attacks (FDIAs) in dynamic smart grids is considered in this paper. The unknown time-varying state variables of the smart grid and the FDIAs impose a significant challenge for designing a computationally efficient detector. To address this challenge, we propose new Cumulative-Sum-type algorithms with computational complex scaling linearly with the number of meters. Moreover, for any constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed algorithm is provided. For any given threshold employed in the proposed algorithm, an upper bound on the worstcase expected detection delay is also derived. The proposed algorithm is numerically investigated in the context of an IEEE standard power system under FDIAs, and is shown to outperform some representative algorithm in the test case.

The need for cybersecurity knowledge and skills is constantly growing as our lives become more integrated with the digital world. In order to meet this demand, educational institutions must continue to innovate within the field of cybersecurity education and make this educational process as effective and efficient as possible. We seek to accomplish this goal by taking an existing cybersecurity educational technology and adding automated grading and assessment functionality to it. This will reduce costs and maximize scalability by reducing or even eliminating the need for human graders.

Smart contracts have been widely used on Ethereum to enable business services across various application domains. However, they are prone to different forms of security attacks due to the dynamic and non-deterministic blockchain runtime environment. In this work, we highlighted a general miner-side type of exploit, called concurrency exploit, which attacks smart contracts via generating malicious transaction sequences. Moreover, we designed a systematic algorithm to automatically detect such exploits. In our preliminary evaluation, our approach managed to identify real vulnerabilities that cannot be detected by other tools in the literature.

In recent years, Network-on-Chip (NoC) has gained increasing popularity as a promising solution for the challenging interconnection problem in multi-processor systems-on-chip (MPSoCs). However, the interest of adversaries to compromise such systems grew accordingly, mandating the integration of security measures into NoC designs. Within this paper, we introduce three novel lightweight approaches for securing communication in NoCs. The suggested solutions combine encryption, authentication, and network coding in order to ensure confidentiality, integrity, and robustness. With performance being critical in NoC environments, our solutions particularly emphasize low latencies and low chip area. Our approaches were evaluated through extensive software simulations. The results have shown that the performance degradation induced by the protection measures is clearly outweighed by the aforementioned benefits. Furthermore, the area overhead implied by the additional components is reasonably low.

False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.

This paper describes the goals of the ATMOSPHERE project, which is a multi-institutional research and development (R&D) effort aiming at designing and implementing a framework and platform to develop, build, deploy, measure and evolve trustworthy, cloud-enabled applications. The proposed system addresses the federation of geographically distributed cloud computing providers that rely on lightweight virtualization, and provide access to heterogeneous sets of resources. In addition, the system also considers both classic trustworthiness properties from the systems community, such as dependability and security, and from the machine learning community, such as fairness and transparency. We present the architecture that has been proposed to address these challenges and discuss some preliminary results.

Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.

The rapid developments of smart grids provide multiple benefits to the delivery of electric power, but at the same time makes the power grids under the threat of cyber attackers. The transmitted data could be deliberately modified without triggering the alarm of bad data detection procedure. In order to ensure the stable operation of the power systems, it is extremely significant to develop effective abnormal detection algorithms against injected false data. In this paper, we introduce the density-based LOF algorithm to detect the false data and dummy data. The simulation results show that the traditional density-clustering based LOF algorithm can effectively identify FDA, but the detection performance on DDA is not satisfactory. Therefore, we propose the improved LOF algorithm to detect DDA by setting reasonable density threshold.

Manufacturers often buy and/or license communication ICs from third-party suppliers. These communication ICs are then integrated into a complex computational system, resulting in a wide range of potential hardware-software security issues. This work proposes a compact supervisory circuit to classify the Bluetooth profile operation of a Bluetooth System-on-Chip (SoC) at low frequencies by monitoring the radio frequency (RF) output power of the Bluetooth SoC. The idea is to inexpensively manufacture an RF envelope detector to monitor the RF output power and a profile classification algorithm on a custom low-frequency integrated circuit in a low-cost legacy technology. When the supervisory circuit observes unexpected behavior, it can shut off power to the Bluetooth SoC. In this preliminary work, we proto-type the supervisory circuit using off-the-shelf components to collect a sufficient data set to train 11 different Machine Learning models. We extract smart descriptive time-domain features from the envelope of the RF output signal. Then, we train the machine learning models to classify three different Bluetooth operation profiles: sensor, hands-free, and headset. Our results demonstrate 100% classification accuracy with low computational complexity.

The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.

Complex military systems are typically cyber-physical systems which are the targets of high level threat actors, and must be able to operate within a highly contested cyber environment. There is an emerging need to provide a strong level of assurance against these threat actors, but the process by which this assurance can be tested and evaluated is not so clear. This paper outlines an initial framework developed through research for evaluating the cyber-worthiness of complex mission critical systems using threat models developed in SysML. The framework provides a visual model of the process by which a threat actor could attack the system. It builds on existing concepts from system safety engineering and expands on how to present the risks and mitigations in an understandable manner.

A prioritized cyber defense remediation plan is critical for effective risk management in cyber-physical systems (CPS). The increased integration of Information Technology (IT)/Operational Technology (OT) in CPS has to lead to the need to identify the critical assets which, when affected, will impact resilience and safety. In this work, we propose a methodology for prioritized cyber risk remediation plan that balances operational resilience and economic loss (safety impacts) in CPS. We present a platform for modeling and analysis of the effect of cyber threats and random system faults on the safety of CPS that could lead to catastrophic damages. We propose to develop a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in CPS. We develop an operational impact assessment to quantify the damages. Finally, we propose the development of a strategic response decision capability that proposes optimal mitigation actions and policies that balances the trade-off between operational resilience (Tactical Risk) and Strategic Risk.

Modern Internet of Things networks are often proprietary, although based on open standards, or are built on the basis of conventional Wi-Fi network, which does not allow the use of energy-saving modes and limits the range of solutions used. The paper is devoted to the study and comparison of two solutions based on Wi-Fi and Bluetooth with the functions of a self-organizing network and switching between transmission channels. The power consumption in relation to specific actions and volumes of transmitted data is investigated; a conclusion is drawn on the conditions for the application of a particular technology.

Concurrency vulnerabilities are extremely harmful and can be frequently exploited to launch severe attacks. Due to the non-determinism of multithreaded executions, it is very difficult to detect them. Recently, data race detectors and techniques based on maximal casual model have been applied to detect concurrency vulnerabilities. However, the former are ineffective and the latter report many false negatives. In this paper, we present CONVUL, an effective tool for concurrency vulnerability detection. CONVUL is based on exchangeable events, and adopts novel algorithms to detect three major kinds of concurrency vulnerabilities. In our experiments, CONVUL detected 9 of 10 known vulnerabilities, while other tools only detected at most 2 out of these 10 vulnerabilities. The 10 vulnerabilities are available at https://github.com/mryancai/ConVul.

The spotlight is on cybersecurity education programs to develop a qualified cybersecurity workforce to meet the demand of the professional field. The ACM CCECC (Committee for Computing Education in Community Colleges) is leading the creation of a set of guidelines for associate degree cybersecurity programs called Cyber2yr, formerly known as CSEC2Y. A task force of community college educators have created a student competency focused curriculum that will serve as a global cybersecurity guide for applied (AAS) and transfer (AS) degree programs to develop a knowledgeable and capable associate level cybersecurity workforce. Based on the importance of the Cyber2yr work; ABET a nonprofit, non-governmental agency that accredits computing programs has created accreditation criteria for two-year cybersecurity programs.

The search for alternative delivery modes to teaching has been one of the pressing concerns of numerous educational institutions. One key innovation to improve teaching and learning is e-learning which has undergone enormous improvements. From its focus on text-based environment, it has evolved into Virtual Learning Environments (VLEs) which provide more stimulating and immersive experiences among learners and educators. An example of VLEs is the virtual world which is an emerging educational platform among universities worldwide. One very interesting topic that can be taught using the virtual world is cybersecurity. Simulating cybersecurity in the virtual world may give a realistic experience to students which can be hardly achieved by classroom teaching. To date, there are quite a number of studies focused on cybersecurity awareness and cybersecurity behavior. But none has focused looking into the effect of digital simulation in the virtual world, as a new educational platform, in the cybersecurity attitude of the students. It is in this regard that this study has been conducted by designing simulation in the virtual world lessons that teaches the five aspects of cybersecurity namely; malware, phishing, social engineering, password usage and online scam, which are the most common cybersecurity issues. The study sought to examine the effect of this digital simulation design in the cybersecurity knowledge and attitude of the students. The result of the study ascertains that students exposed under simulation in the virtual world have a greater positive change in cybersecurity knowledge and attitude than their counterparts.

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

Over the last two decades we have seen a dramatic improvement in the efficiency of conflict-driven clause-learning Boolean satisfiability (CDCL SAT) solvers on industrial problems from a variety of domains. The availability of such a powerful general-purpose search tools as SAT solvers has led many researchers to propose SAT-based methods for cryptanalysis, including techniques for finding collisions in hash functions and breaking symmetric encryption schemes. Most of the previously proposed SAT-based cryptanalysis approaches are blackbox techniques, in the sense that the cryptanalysis problem is encoded as a SAT instance and then a CDCL SAT solver is invoked to solve the said instance. A weakness of this approach is that the encoding thus generated may be too large for any modern solver to solve efficiently. Perhaps a more important weakness of this approach is that the solver is in no way specialized or tuned to solve the given instance. To address these issues, we propose an approach called CDCL(Crypto) (inspired by the CDCL(T) paradigm in Satisfiability Modulo Theory solvers) to tailor the internal subroutines of the CDCL SAT solver with domain-specific knowledge about cryptographic primitives. Specifically, we extend the propagation and conflict analysis subroutines of CDCL solvers with specialized codes that have knowledge about the cryptographic primitive being analyzed by the solver. We demonstrate the power of this approach in differential path a nd a lgebraic fault analysis of hash functions. Our initial results encourages the fact that this approach can significantly improve the blackbox SAT-based cryptanalysis.

Graph pattern matching is one of the most fundamental problems in graph database and is associated with a wide spectrum of applications. Due to its computational intensiveness, researchers have primarily devoted their efforts to improving the performance of the algorithm while constraining the graphs to have singular labels on vertices (edges) or no label. Whereas in practice graphs are typically associated with rich properties, thus the main focus in the industry is instead on powerful query languages that can express a sufficient number of pattern matching scenarios. We demo PatMat in this work to glue together the academic efforts on performance and the industrial efforts on expressiveness. To do so, we leverage the state-of-the-art join-based algorithms in the distributed contexts and Cypher query language - the most widely-adopted declarative language for graph pattern matching. The experiments demonstrate how we are capable of turning complex Cypher semantics into a distributed solution with high performance.

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

This paper introduces complex network into software clone detection and proposes a clone code detection method based on software complex network feature matching. This method has the following properties. It builds a software network model with many added features and codes written with different languages can be detected by a single method. It reduces the space of code comparison, and it searches similar subnetworks to detect clones without knowing any clone codes information. This method can be used in detecting open source code which has been reused in software for security analysis.

Wear and tear are essential in establishing the market value of an asset. From shutter counters on DSLRs to odometers inside cars, specific counters, that encode the degree of wear, exist on most products. But malicious modification of the information that they report was always a concern. Our work explores a solution to this problem by using the blockchain technology, a layered encoding of product attributes and identity-based cryptography. Merging such technologies is essential since blockchains facilitate the construction of a distributed database that is resilient to adversarial modifications, while identity-based signatures set room for a more convenient way to check the correctness of the reported values based on the name of the product and pseudonym of the owner alone. Nonetheless, we reinforce security by using ownership cards deployed around NFC tokens. Since odometer fraud is still a major practical concern, we discuss a practical scenario centered on vehicles, but the framework can be easily extended to many other assets.

This paper focuses on the creation of information centric Cyber-Human Learning Frameworks involving Virtual Reality based mediums. A generalized framework is proposed, which is adapted for two educational domains: one to support education and training of residents in orthopedic surgery and the other focusing on science learning for children with autism. Users, experts and technology based mediums play a key role in the design of such a Cyber-Human framework. Virtual Reality based immersive and haptic mediums were two of the technologies explored in the implementation of the framework for these learning domains. The proposed framework emphasizes the importance of Information-Centric Systems Engineering (ICSE) principles which emphasizes a user centric approach along with formalizing understanding of target subjects or processes for which the learning environments are being created.

Trajectory data in the Internet of Things contains many behavioral information of users, and the method of Mix-zone can be used to separate the association among the user's movement trajectories. In this paper, the weighted undirected graph is used to establish a mathematical model for the Mix-zone, and a user flow-based algorithm is proposed to estimate the probability of migration between nodes in the graph. In response to the attack method basing on the migration probability, the traditional Mix-zone is improved. Finally, an algorithms for adaptively building enhanced Mix-zone is proposed and the simulation using real data sets shows the superiority of the algorithm.

Cyber attacks on transmission lines are one of the main challenges in security of smart grids. These targeted attacks, if not detected, might cause cascading problems in power systems. This paper proposes a bi-level mixed integer linear programming (MILP) optimization model for false data injection on targeted buses in a power system to overflow targeted transmission lines. The upper level optimization problem outputs the optimized false data injections on targeted load buses to overflow a targeted transmission line without violating bad data detection constraints. The lower level problem integrates the false data injections into the optimal power flow problem without violating the optimal power flow constraints. A few case studies are designed to validate the proposed attack model on IEEE 118-bus power system.