Biblio

Accurate and synchronized timing information is required by power system operators for controlling the grid infrastructure (relays, Phasor Measurement Units (PMUs), etc.) and determining asset positions. Satellite-based global positioning system (GPS) is the primary source of timing information. However, GPS disruptions today (both intentional and unintentional) can significantly compromise the reliability and security of our electric grids. A robust alternate source for accurate timing is critical to serve both as a deterrent against malicious attacks and as a redundant system in enhancing the resilience against extreme events that could disrupt the GPS network. To achieve this, we rely on the highly accurate, terrestrial atomic clock-based network for alternative timing and synchronization. In this paper, we discuss an experimental setup for an alternative timing approach. The data obtained from this experimental setup is continuously monitored and analyzed using various time deviation metrics. We also use these metrics to compute deviations of our clock with respect to the National Institute of Standards and Technologys (NIST) GPS data. The results obtained from these metric computations are elaborately discussed. Finally, we discuss the integration of the procedures involved, like real-time data ingestion, metric computation, and result visualization, in a novel microservices-based architecture for situational awareness.

Control room video surveillance is an important source of information for ensuring public safety. To facilitate the process, a Decision-Support System (DSS) designed for the security task force is vital and necessary to take decisions rapidly using a sea of information. In case of mission critical operation, Situational Awareness (SA) which consists of knowing what is going on around you at any given time plays a crucial role across a variety of industries and should be placed at the center of our DSS. In our approach, SA system will take advantage of the human factor thanks to the reinforcement signal whereas previous work on this field focus on improving knowledge level of DSS at first and then, uses the human factor only for decision-making. In this paper, we propose a situational awareness-centric decision-support system framework for mission-critical operations driven by Quality of Experience (QoE). Our idea is inspired by the reinforcement learning feedback process which updates the environment understanding of our DSS. The feedback is injected by a QoE built on user perception. Our approach will allow our DSS to evolve according to the context with an up-to-date SA.

With the proliferation of data in Internet-related applications, incidences of cyber security have increased manyfold. Energy management, which is one of the smart city layers, has also been experiencing cyberattacks. Furthermore, the Distributed Energy Resources (DER), which depend on different controllers to provide energy to the main physical smart grid of a smart city, is prone to cyberattacks. The increased cyber-attacks on DER systems are mainly because of its dependency on digital communication and controls as there is an increase in the number of devices owned and controlled by consumers and third parties. This paper analyzes the major cyber security and privacy challenges that might inflict, damage or compromise the DER and related controllers in smart cities. These challenges highlight that the security and privacy on the Internet of Things (IoT), big data, artificial intelligence, and smart grid, which are the building blocks of a smart city, must be addressed in the DER sector. It is observed that the security and privacy challenges in smart cities can be solved through the distributed framework, by identifying and classifying stakeholders, using appropriate model, and by incorporating fault-tolerance techniques.

The electromagnetic energy harvesting technology is a new and effective way to supply power to the condition monitoring sensors installed on or near the transmission line. We will use Computer Simulation Technology Software to simulate the different designs of stand-alone electromagnetic energy harvesters The power generated by energy harvesters of different design structures is compared and analyzed through simulation and experimental results. We then propose an improved design of energy harvester.

Current sensors are widely used in power grid for power metering, automation and power equipment monitoring. Since the tradeoff between the sensitivity and the measurement range needs to be made to design a current sensor, it is difficult to deploy one sensor to measure both the small-magnitude and the large-magnitude current. In this research, we design a surface-mount current sensor by using the tunneling magneto-resistance (TMR) devices and show that the tradeoff between the sensitivity and the detection range can be broken. Two TMR devices of different sensitivity degrees were integrated into one current sensor module, and a signal processing algorithm was implemented to fusion the outputs of the two TMR devices. Then, a platform was setup to test the performance of the surface-mount current sensor. The results showed that the designed current sensor could measure the current from 2 mA to 100 A with an approximate 93 dB dynamic range. Besides, the nonintrusive feature of the surface-mount current sensor could make it convenient to be deployed on-site.

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

A unified cloud management platform is the key to efficient and secure management of cloud computing resources. To improve the operation effect of the power cloud service platform, power companies can use the micro-service architecture technology to carry out data processing, information integration, and innovative functional architecture of the power cloud service platform, realize the optimal design of the power cloud service platform and improve the power cloud service platform-security service quality. According to the technical requirements of the power cloud security management platform, this paper designs the technical architecture of the power unified cloud security management platform and expounds on the functional characteristics of the cloud security management platform to verify the feasibility and effectiveness of the cloud security management platform.

Effective information security risk management is essential for survival of any business that is dependent on IT. In this paper we present an efficient and effective solution to find best parameters for managing cyber risks using artificial intelligence. Genetic algorithm is use as it can provide our required optimization and intelligence. Results show that GA is professional in finding the best parameters and minimizing the risk.

In recent years, the need for seamless connectivity has increased across various network platforms with demands coming from industries, home, mobile, transportation and office networks. The 5th generation (5G) network is being deployed to meet such demand of high-speed seamless network device connections. The seamless connectivity 5G provides could be a security threat allowing attacks such as distributed denial of service (DDoS) because attackers might have easy access into the network infrastructure and higher bandwidth to enhance the effects of the attack. The aim of this research is to provide a security solution for 5G technology to DDoS attacks by managing the response to threats posed by DDoS. Deploying a security policy language which is reactive and event-oriented fits into a flexible, efficient, and lightweight security approach. A policy in our language consists of an event whose occurrence triggers a policy rule where one or more actions are taken.

As the COVID-19 pandemic scattered businesses and their workforces into new scales of remote work, vital security concerns arose surrounding remote access. Bring Your Own Device (BYOD) also plays a growing role in the ability of companies to support remote workforces. As more enterprises embrace concepts of zero trust in their network security posture, access control policy management problems become a more significant concern as it relates to BYOD security enforcement. This BYOD security policy must enable work from home, but enterprises have a vested interest in maintaining the security of their assets. Therefore, the BYOD security policy must strike a balance between access, security, and privacy, given the personal device use. This paper explores the challenges and opportunities of enabling zero trust in BYOD use cases. We present a BYOD policy specification to enable the zero trust access control known as BYOZ. Accompanying this policy specification, we have designed a network architecture to support enterprise zero trust BYOD use cases through the novel incorporation of continuous authentication & authorization enforcement. We evaluate our architecture through a demo implementation of BYOZ and demonstrate how it can meet the needs of existing enterprise networks using BYOD.

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure’s state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.

The cloud provides storage for users to share their files in the cloud. Nowadays some shared data auditing schemes are proposed for protecting data integrity. However, preserving the identity privacy of group users and secure user revocation usually result in high computational overhead. Then a shared data auditing scheme supporting identity privacy preserving is proposed that enables users to be effectively revoked. To preserve identity privacy during the audit process, we develop an efficient authenticator generation mechanism that enables public auditing. Our solution supports efficient user revocation, where the authenticator of the revoked user does not need to be regenerated and integrity checking can be performed appropriately. At the same time, the group manager maintains two tables to ensure user traceability. When the user updates data, two tables are modified and updated by the group manager promptly. It shows that our scheme is secure by security analysis. Moreover, concrete experiments prove the performance of the system.

Python continues to be one of the most popular programming languages and has been used in many safety-critical fields such as medical treatment, autonomous driving systems, and data science. These fields put forward higher security requirements to Python ecosystems. However, existing studies on machine learning systems in Python concentrate on data security, model security and model privacy, and just assume the underlying Python virtual machines (PVMs) are secure and trustworthy. Unfortunately, whether such an assumption really holds is still unknown.This paper presents, to the best of our knowledge, the first and most comprehensive empirical study on the security of CPython, the official and most deployed Python virtual machine. To this end, we first designed and implemented a software prototype dubbed PVMSCAN, then use it to scan the source code of the latest CPython (version 3.10) and other 10 versions (3.0 to 3.9), which consists of 3,838,606 lines of source code. Empirical results give relevant findings and insights towards the security of Python virtual machines, such as: 1) CPython virtual machines are still vulnerable, for example, PVMSCAN detected 239 vulnerabilities in version 3.10, including 55 null dereferences, 86 uninitialized variables and 98 dead stores; Python/C API-related vulnerabilities are very common and have become one of the most severe threats to the security of PVMs: for example, 70 Python/C API-related vulnerabilities are identified in CPython 3.10; 3) the overall quality of the code remained stable during the evolution of Python VMs with vulnerabilities per thousand line (VPTL) to be 0.50; and 4) automatic vulnerability rectification is effective: 166 out of 239 (69.46%) vulnerabilities can be rectified by a simple yet effective syntax-directed heuristics.We have reported our empirical results to the developers of CPython, and they have acknowledged us and already confirmed and fixed 2 bugs (as of this writing) while others are still being analyzed. This study not only demonstrates the effectiveness of our approach, but also highlights the need to improve the reliability of infrastructures like Python virtual machines by leveraging state-of-the-art security techniques and tools.

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different processes, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Physical or logical isolation mechanisms such as using dedicated tables and flushing during context-switch can provide security but incur non-trivial costs in space and/or execution time. Randomization mechanisms incurs the performance cost in a different way: those with higher securities add latency to the critical path of the pipeline, while the simpler alternatives leave vulnerabilities to more sophisticated attacks.This paper proposes HyBP, a practical hybrid protection and effective mechanism for building secure branch predictors. The design applies the physical isolation and randomization in the right component to achieve the best of both worlds. We propose to protect the smaller tables with physically isolation based on (thread, privilege) combination; and protect the large tables with randomization. Surprisingly, the physical isolation also significantly enhances the security of the last-level tables by naturally filtering out accesses, reducing the information flow to these bigger tables. As a result, key changes can happen less frequently and be performed conveniently at context switches. Moreover, we propose a latency hiding design for a strong cipher by precomputing the "code book" with a validated, cryptographically strong cipher. Overall, our design incurs a performance penalty of 0.5% compared to 5.1% of physical isolation under the default context switching interval in Linux.

In recent years, the epidemic of speculative side channels significantly increases the difficulty in enforcing domain isolation boundaries in a virtualized cloud environment. Although mitigations exist, the approach taken by the industry is neither a long-term nor a scalable solution, as we target each vulnerability with specific mitigations that add up to substantial performance penalties. We propose a different approach to secret isolation: guaranteeing that the hypervisor is Secret-Free (SF). A Secret-Free design partitions memory into secrets and non-secrets and reconstructs hypervisor isolation. It enforces that all domains have a minimal and secret-free view of the address space. In contrast to state-of-the-art, a Secret-Free hypervisor does not identify secrets to be hidden, but instead identifies non-secrets that can be shared, and only grants access necessary for the current operation, an allow-list approach. SF designs function with existing hardware and do not exhibit noticeable performance penalties in production workloads versus the unmitigated baseline, and outperform state-of-the-art techniques by allowing speculative execution where secrets are invisible. We implement SF in Xen (a Type-I hypervisor) to demonstrate that the design applies well to a commercial hypervisor. Evaluation shows performance comparable to baseline and up to 37% improvement in certain hypervisor paths compared with Xen default mitigations. Further, we demonstrate Secret-Free is a generic kernel isolation infrastructure for a variety of systems, not limited to Type-I hypervisors. We apply the same model in Hyper-V (Type-I), bhyve (Type-II) and FreeBSD (UNIX kernel) to evaluate its applicability and effectiveness. The successful implementations on these systems prove the generality of SF, and reveal the specific adaptations and optimizations required for each type of kernel.

Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.

In recent times, Network-on-Chip (NoC) has become state of the art for communication in Multiprocessor System-on-Chip due to the existing scalability issues in this area. However, these systems are exposed to security threats such as extraction of secret information. Therefore, the need for secure communication arises in such environments. In this work, we present a communication protocol based on authenticated encryption with recovery mechanisms to establish secure end-to-end communication between the NoC nodes. In addition, a selected key agreement approach required for secure communication is implemented. The security functionality is located in the network adapter of each processing element. If data is tampered with or deleted during transmission, recovery mechanisms ensure that the corrupted data is retransmitted by the network adapter without the need of interference from the processing element. We simulated and implemented the complete system with SystemC TLM using the NoC simulation platform PANACA. Our results show that we can keep a high rate of correctly transmitted information even when attackers infiltrated the NoC system.

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.

With the development of artificial intelligence, the need for data sharing is becoming more and more urgent. However, the existing data sharing methods can no longer fully meet the data sharing needs. Privacy breaches, lack of motivation and mutual distrust have become obstacles to data sharing. We design a privacy-preserving, decentralized data sharing method based on blockchain smart contracts, named PPDS. To protect data privacy, we transform the data sharing problem into a model sharing problem. This means that the data owner does not need to directly share the raw data, but the AI model trained with such data. The data requester and the data owner interact on the blockchain through a smart contract. The data owner trains the model with local data according to the requester's requirements. To fairly assess model quality, we set up several model evaluators to assess the validity of the model through voting. After the model is verified, the data owner who trained the model will receive reward in return through a smart contract. The sharing of the model avoids direct exposure of the raw data, and the reasonable incentive provides a motivation for the data owner to share the data. We describe the design and workflow of our PPDS, and analyze the security using formal verification technology, that is, we use Coloured Petri Nets (CPN) to build a formal model for our approach, proving its security through simulation execution and model checking. Finally, we demonstrate effectiveness of PPDS by developing a prototype with its corresponding case application.

Modern consumer electronic devices often provide intelligence services with deep neural networks. We have started migrating the computing locations of intelligence services from cloud servers (traditional AI systems) to the corresponding devices (on-device AI systems). On-device AI systems generally have the advantages of preserving privacy, removing network latency, and saving cloud costs. With the emergence of on-device AI systems having relatively low computing power, the inconsistent and varying hardware resources and capabilities pose difficulties. Authors' affiliation has started applying a stream pipeline framework, NNStreamer, for on-device AI systems, saving developmental costs and hardware resources and improving performance. We want to expand the types of devices and applications with on-device AI services products of both the affiliation and second/third parties. We also want to make each AI service atomic, re-deployable, and shared among connected devices of arbitrary vendors; we now have yet another requirement introduced as it always has been. The new requirement of “among-device AI” includes connectivity between AI pipelines so that they may share computing resources and hardware capabilities across a wide range of devices regardless of vendors and manufacturers. We propose extensions of the stream pipeline framework, NNStreamer, for on-device AI so that NNStreamer may provide among-device AI capability. This work is a Linux Foundation (LF AI & Data) open source project accepting contributions from the general public.

Shipboard marine radar systems are essential for safe navigation, helping seafarers perceive their surroundings as they provide bearing and range estimations, object detection, and tracking. Since onboard systems have become increasingly digitized, interconnecting distributed electronics, radars have been integrated into modern bridge systems. But digitization increases the risk of cyberattacks, especially as vessels cannot be considered air-gapped. Consequently, in-depth security is crucial. However, particularly radar systems are not sufficiently protected against harmful network-level adversaries. Therefore, we ask: Can seafarers believe their eyes? In this paper, we identify possible attacks on radar communication and discuss how these threaten safe vessel operation in an attack taxonomy. Furthermore, we develop a holistic simulation environment with radar, complementary nautical sensors, and prototypically implemented cyberattacks from our taxonomy. Finally, leveraging this environment, we create a comprehensive dataset (RadarPWN) with radar network attacks that provides a foundation for future security research to secure marine radar communication.

MQTT is widely adopted by IoT devices because it allows for the most efficient data transfer over a variety of communication lines. The security of MQTT has received increasing attention in recent years, and several studies have demonstrated the configurations of many MQTT brokers are insecure. Adversaries are allowed to exploit vulnerable brokers and publish malicious messages to subscribers. However, little has been done to understanding the security issues on the device side when devices handle unauthorized MQTT messages. To fill this research gap, we propose a fuzzing framework named ShadowFuzzer to find client-side vulnerabilities when processing incoming MQTT messages. To avoiding ethical issues, ShadowFuzzer redirects traffic destined for the actual broker to a shadow broker under the control to monitor vulnerabilities. We select 15 IoT devices communicating with vulnerable brokers and leverage ShadowFuzzer to find vulnerabilities when they parse MQTT messages. For these devices, ShadowFuzzer reports 34 zero-day vulnerabilities in 11 devices. We evaluated the exploitability of these vulnerabilities and received a total of 44,000 USD bug bounty rewards. And 16 CVE/CNVD/CN-NVD numbers have been assigned to us.

Obfuscation refers to changing the structure of code in a way that original semantics can be hidden. These techniques are often used by application developers for code hardening but it has been found that obfuscation techniques are widely used by malware developers in order to hide the work flow and semantics of malicious code. Class Encryption, Code Re-Ordering, Junk Code insertion and Control Flow modifications are Code Obfuscation techniques. In these techniques, code of the application is changed. These techniques change the signature of the application and also affect the systems that use sequence of instructions in order to detect maliciousness of an application. In this paper an ’Opcode sequence’ based detection system is designed and tested against obfuscated samples. It has been found that the system works efficiently for the detection of non obfuscated samples but the performance is effected significantly against obfuscated samples. The study tests different code obfuscation schemes and reports the effect of each on sequential opcode based analytic system.

Under the new situation of China's new infrastructure and digital transformation and upgrading, large IT companies such as the United States occupy the market of key information infrastructure components in important fields such as power and energy in China, which makes the risk of key information infrastructure in China's power enterprises become more and more prominent. In the power Internet of Things environment where everything is connected, the back doors and loopholes of basic software and hardware caused by the supply chain risks of key information infrastructure have broken through the foundation of power cyber-security and information security defense, and the security risk management of power key information infrastructure cyber-security has become urgent. Therefore, this paper studies the construction of the cyber-security management framework of key information infrastructure suitable for electric power enterprises, and defines the security risk assessment norms of each link of equipment access to the network. Implement the national cyber-security requirements, promote the cyber-security risk controllable assessment service of key information infrastructure, improve the security protection level of power grid information system from the source, and promote the construction and improvement of the network and information security system of power industry.

Vehicular crowdsensing (VCS) is a subset of crowd-sensing where data collection is outsourced to group vehicles. Here, an entity interested in collecting data from a set of Places of Sensing Interest (PsI), advertises a set of sensing tasks, and the associated rewards. Vehicles attracted by the offered rewards deviate from their ongoing trajectories to visit and collect from one or more PsI. In this win-to-win scenario, vehicles reach their final destination with the extra reward, and the entity obtains the desired samples. Unfortunately, the efficiency of VCS can be undermined by the Sybil attack, in which an attacker can benefit from the injection of false vehicle identities. In this paper, we present a case study and analyze the effects of such an attack. We also propose a defense mechanism based on generative adversarial neural networks (GANs). We discuss GANs' advantages, and drawbacks in the context of VCS, and new trends in GANs' training that make them suitable for VCS.