Biblio

ARP-attack often occurs in LAN network [1], which directly affects the user's online experience. The common type of ARP-attack is MITM-Attack (Man-in-the-Middle Attack) with two-types, disguising a host or a gateway. Common means of ARP-attack prevention is by deploying network-security equipment or binding IP-MAC in LAN manually[10]. This paper studies an automatic ARP-attack prevention technology for multi-object, based on the domain-control technology and batch-processing technology. Compared with the common ARP-attack-prevention measure, this study has advantages of low-cost, wide-application, and maintenance-free. By experimentally researching, this paper demonstrates the research correctness and technical feasibility. This research result, multi-object-oriented automatic defense technology for ARP-attacking, can apply to enterprise network.

Fog computing architecture allows the end-user devices of an Internet of Things (IoT) application to meet their latency and computation requirements by offloading tasks to a fog node in proximity. This fog node in turn may offload the task to a neighboring fog node or the cloud-based on an optimal node selection policy. Several such node selection policies have been proposed that facilitate the selection of an optimal node, minimizing delay and energy consumption. However, one crucial assumption of these schemes is that all the networked fog nodes are authorized part of the fog network. This assumption is not valid, especially in a cooperative fog computing environment like a smart city, where fog nodes of multiple applications cooperate to meet their latency and computation requirements. In this paper, we propose a secure task-offloading framework for a distributed fog computing environment based on smart-contracts on the blockchain. The proposed framework allows a fog-node to securely offload tasks to a neighboring fog node, even if no prior trust-relation exists. The security analysis of the proposed framework shows how non-authenticated fog nodes are prevented from taking up offloading tasks.

Cloud-based infrastructures have grown in popularity over the last decade leveraging virtualisation, server, storage, compute power and network components to develop flexible applications. The requirements for instantaneous deployment and reduced costs have led the shift from virtual machine deployment to containerisation, increasing the overall flexibility of applications and increasing performances. However, containers require a fully fleshed operating system to execute, increasing the attack surface of an application. Unikernels, on the other hand, provide a lightweight memory footprint, ease of application packaging and reduced start-up times. Moreover, Unikernels reduce the attack surface due to the self-contained environment only enabling low-level features. In this work, we provide an exhaustive description of the unikernel ecosystem; we demonstrate unikernel vulnerabilities and further discuss the security implications of Unikernel-enabled environments through different use-cases.

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.

The most popular CAPTCHA service in use today is Google reCAPTCHA v2, whose main offering is an image-based CAPTCHA challenge. This paper looks into the security measures used in reCAPTCHA v2's image challenges and proposes a deep learning-based solution that can be used to automatically solve them. The proposed method is tested with both a custom object- detection deep learning model as well as Google's own Cloud Vision API, in conjunction with human mimicking mouse movements to bypass the challenges. The paper also suggests some potential defense measures to increase overall security and other additional attack directions for reCAPTCHA v2.

Alongside the rapid expansion of Internet of Things (IoT), and network evolution (5G, 6G technologies), comes the need for security of higher level and less hardware demanding modules. New cryptographic systems are developed, in order to satisfy the special needs of security, that have emerged in modern applications. In this paper, a novel lightweight data streaming system, is proposed, which operates in alternative modes. Each one of them, performs efficiently as one of three in total, stream ciphering modules. The operation of the proposed system, is based on reconfigurable logic. It aims at a lower hardware utilization and good performance, at the same time. In addition, in order to have a fair and detailed comparison, a second one design is also integrated and introduced. This one proposes a conventional architecture, consisting of the same three stream ciphering modes, implemented on the same device, as separate operation modules. The FPGA synthesis results prove that the proposed reconfigurable design achieves to minimize the area resources, from 18% to 30%, compared to the conventional one, while maintaining high performance values, for the supported modes.

Modern electric grids that integrate smart grid technologies require different approaches to grid operations. There has been a shift towards increased reliance on distributed sensors to monitor bidirectional power flows and machine learning based load forecasting methods (e.g., using deep learning). These methods are fairly accurate under normal circumstances, but become highly vulnerable to stealthy adversarial attacks that could be deployed on the load forecasters. This paper provides a novel model-based Testbed for Simulation-based Evaluation of Resilience (TeSER) that enables evaluating deep learning based load forecasters against stealthy adversarial attacks. The testbed leverages three existing technologies, viz. DeepForge: for designing neural networks and machine learning pipelines, GridLAB-D: for electric grid distribution system simulation, and WebGME: for creating web-based collaborative metamodeling environments. The testbed architecture is described, and a case study to demonstrate its capabilities for evaluating load forecasters is provided.

Explainable Artificial Intelligence (XAI), i.e., the development of more transparent and interpretable AI models, has gained increased traction over the last few years. This is due to the fact that, in conjunction with their growth into powerful and ubiquitous tools, AI models exhibit one detrimental characteristic: a performance-transparency trade-off. This describes the fact that the more complex a model's inner workings, the less clear it is how its predictions or decisions were achieved. But, especially considering Machine Learning (ML) methods like Reinforcement Learning (RL) where the system learns autonomously, the necessity to understand the underlying reasoning for their decisions becomes apparent. Since, to the best of our knowledge, there exists no single work offering an overview of Explainable Reinforcement Learning (XRL) methods, this survey attempts to address this gap. We give a short summary of the problem, a definition of important terms, and offer a classification and assessment of current XRL methods. We found that a) the majority of XRL methods function by mimicking and simplifying a complex model instead of designing an inherently simple one, and b) XRL (and XAI) methods often neglect to consider the human side of the equation, not taking into account research from related fields like psychology or philosophy. Thus, an interdisciplinary effort is needed to adapt the generated explanations to a (non-expert) human user in order to effectively progress in the field of XRL and XAI in general.

Information Technology (IT) is a complex domain. In order to properly manage IT related processes, several frameworks including ITIL (Information Technologies Infrastructure Library), COBIT (Control OBjectives for Information and related Technologies), IT Service CMMI (IT Service Capability Maturity Model) and many others have emerged in recent decades. Meanwhile, the prevalence of Agile methods has increased, posing the coexistence of Agile approach with different IT frameworks already adopted in organizations. More specifically, the pursuit of being agile in the area of digitalization pushes organizations to go for agile transformation while preserving full compliance to IT frameworks for the sake of their survival. The necessity for this coexistence, however, brings its own challenges and solutions for harmonizing the requirements of both parties. In this paper, we focus on harmonizing the requirements of COBIT and Scrum in a same organization, which is especially challenging when a full compliance to COBIT is expected. Therefore, this study aims to identifying the challenges of and possible solutions for the coexistence of Scrum and COBIT (version 4.1 in this case) in an organization, by considering two case studies: one from the literature and the case of Akbank delivered in this study. Thus, it extends the corresponding previous case study from two points: adds one more case study to enrich the results from the previous case study and provides more opportunity to make generalization by considering two independent cases.

Machine Learning has revolutionized many fields of computer science. Reinforcement Learning (RL), in particular, stands out as a solution to sequential decision making problems. With the growing complexity of computer networks in the face of new emerging technologies, such as the Internet of Things and the growing complexity of threat vectors, there is a dire need for autonomous network systems. RL is a viable solution for achieving this autonomy. Software-defined Networking (SDN) provides a global network view and programmability of network behaviour, which can be employed for security management. Previous works in RL-based threat mitigation have mostly focused on very specific problems, mostly non-sequential, with ad-hoc solutions. In this paper, we propose ATMoS, a general framework designed to facilitate the rapid design of RL applications for network security management using SDN. We evaluate our framework for implementing RL applications for threat mitigation, by showcasing the use of ATMoS with a Neural Fitted Q-learning agent to mitigate an Advanced Persistent Threat. We present the RL model's convergence results showing the feasibility of our solution for active threat mitigation.

Conflicts may arise at any time during military debriefing meetings, especially in high intensity deployed settings. When such conflicts arise, it takes time to get everyone back into a receptive state of mind so that they engage in reflective discussion rather than unproductive arguing. It has been proposed by some that the use of social robots equipped with social abilities such as emotion regulation through rapport building may help to deescalate these situations to facilitate critical operational decisions. However, in military settings, the same AI agent used in the pre-brief of a mission may not be the same one used in the debrief. The purpose of this study was to determine whether a brief rapport-building session with a social robot could create a connection between a human and a robot agent, and whether consistency in the embodiment of the robot agent was necessary for maintaining this connection once formed. We report the results of a pilot study conducted at the United States Air Force Academy which simulated a military mission (i.e., Gravity and Strike). Participants' connection with the agent, sense of trust, and overall likeability revealed that early rapport building can be beneficial for military missions.

Remote Attestation (RA) is a security service that detects malware presence on remote IoT devices by verifying their software integrity by a trusted party (verifier). There are three main types of RA: software (SW)-, hardware (HW)-, and hybrid (SW/HW)-based. Hybrid techniques obtain secure RA with minimal hardware requirements imposed on the architectures of existing microcontrollers units (MCUs). In recent years, considerable attention has been devoted to hybrid techniques since prior software-based ones lack concrete security guarantees in a remote setting, while hardware-based approaches are too costly for low-end MCUs. However, one key problem is that many already deployed IoT devices neither satisfy minimal hardware requirements nor support hardware modifications, needed for hybrid RA. This paper bridges the gap between software-based and hybrid RA by proposing a novel RA scheme based on software virtualization. In particular, it proposes a new scheme, called SIMPLE, which meets the minimal hardware requirements needed for secure RA via reliable software. SIMPLE depends on a formally-verified software-based memory isolation technique, called Security MicroVisor (Sμ V). Its reliability is achieved by extending the formally-verified safety and correctness properties to cover the entire software architecture of SIMPLE. Furthermore, SIMPLE is used to construct SIMPLE+, an efficient swarm attestation scheme for static and dynamic heterogeneous IoT networks. We implement and evaluate SIMPLE and SIMPLE+ on Atmel AVR architecture, a common MCU platform.

Enterprise networks are increasingly moving towards Software Defined Networking, which is becoming a major trend in the networking arena. With the increased popularity of SDN, there is a greater need for security measures for protecting the enterprise networks. This paper focuses on the design and implementation of an integrated security architecture for SDN based enterprise networks. The integrated security architecture uses a policy-based approach to coordinate different security mechanisms to detect and counteract a range of security attacks in the SDN. A distinguishing characteristic of the proposed architecture is its ability to deal with dynamic changes in the security attacks as well as changes in trust associated with the network devices in the infrastructure. The adaptability of the proposed architecture to dynamic changes is achieved by having feedback between the various security components/mechanisms in the architecture and managing them using a dynamic policy framework. The paper describes the prototype implementation of the proposed architecture and presents security and performance analysis for different attack scenarios. We believe that the proposed integrated security architecture provides a significant step towards achieving a secure SDN for enterprises.

Artificial Intelligence (AI) and Machine Learning (ML) techniques have found use in many fields, including hardware-based security defenses and novel threats to hardware security. In this paper, we briefly examine the growing overlap between AI/ML and hardware for security, where AI/ML techniques provide practitioners with new ways to monitor runtime behavior but also provide new tools for attackers to steal secret information. We also explore how AI/ML is reshaping concerns for improving the security of hardware, particularly as AI/ML appear throughout the design flow. While AI/ML techniques contribute towards better hardware Trojan detection, improved design turnaround time, and design space exploration, such techniques may also introduce new threat vectors into the supply chain. With these in mind, we present some potential challenges and new directions at these intersections, giving new insights into how hardware security, alongside AI/M,L advances.

The modern electronics supply chain is a globalized marketplace with the increasing threat of counterfeit integrated circuits (ICs) being installed into mission critical systems. A number of methods for detecting counterfeit ICs exist; however, effective test and evaluation (T&E) methods to assess the confidence of detecting recycled ICs are needed. Additionally, methods for the trustworthy tracking of recycled ICs in the supply chain are also needed. In this work, we propose a novel methodology to address the detection and tracking of recycled ICs at each stage of the electronics supply chain. We present a case study demonstrating our assessment model to calculate the confidence levels of authentic and recycled ICs, and to confidently track these types of ICs throughout the electronics supply chain.

Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We validated the system by designing a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability and social presence. Our results suggest that identity migration had a positive effect on trust, competence and social presence, while information migration had a positive effect on trust, competence and likeability. Overall, users report highest trust, competence, likeability and social presence towards the conversational agent when both identity and information were migrated across embodiments.

Modern power systems depend on cyber-physical systems to link physical devices and control technologies. A major concern in the implementation of smart power networks is to minimize the risk of data privacy violation (e.g., by adversaries using data poisoning and inference attacks). In this article, we propose a privacy-preserving framework to achieve both privacy and security in smart power networks. The framework includes two main modules: a two-level privacy module and an anomaly detection module. In the two-level privacy module, an enhanced-proof-of-work-technique-based blockchain is designed to verify data integrity and mitigate data poisoning attacks, and a variational autoencoder is simultaneously applied for transforming data into an encoded format for preventing inference attacks. In the anomaly detection module, a long short-term memory deep learning technique is used for training and validating the outputs of the two-level privacy module using two public datasets. The results highlight that the proposed framework can efficiently protect data of smart power networks and discover abnormal behaviors, in comparison to several state-of-the-art techniques.

With the continuous improvement of people's safety awareness, infrared products as human motion detection technology have been widely used in the field of security. In order to better apply infrared products to life, improving the performance of infrared products and reducing the cost of products has become the main goal. According to the signal collected by Pyroelectric infrared (PIR) sensor, this paper establishes a database model. According to the data collected, Kalman filter is used to preprocess the data. The validity of the data after preprocessing is judged by the algorithm. The experimental results show that the accuracy of the model can reach 97% by using a support vector machine (SVM) algorithm incorporated with Fast Fourier Transform (FFT). According to the above algorithm flow, a real-time intellectual property (IP) core is designed by using hardware description language, after establishing the data processing algorithm. The interface design, timing design and function design of the IP core are designed. The IP core can be connected to the microcontroller unit (MCU) as an independent peripheral to form a PIR special processor, which can detect the distance of 15 m in real time.

This paper proposes a method to synthesis acoustic wave (AW) filters with Chebyshev response automatically. Meanwhile, each AW resonator used to design the filter can be easily fabricated on the same piezoelectric substrate. The method is based on an optimization algorithm with constraints for constant electromechanical coupling coefficient ( kt2) to minimize the defined cost function. Finally, the experimental result for a surface acoustic wave (SAW) filter of global positioning system (GPS) frequency band based on the 42° lithium tantalate (LiTaO3) substrate validates the simulation results. The designed filter shows insertion loss (IL) and return loss (RL) better than 2.5dB and 18dB respectively in the pass-band, and out-band reflection larger than 30dB.

Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.

In order to significantly improve the reliable interaction and fast processing when TT&C(Tracking, Telemetry and Command) Resource Scheduling and Management System (TRSMS) communicate with external systems which are diverse, multiple directional and high concurrent, this paper designs and implements a highly concurrent and secure middleware for TT&C Resource Automatic Scheduling Interface (TRASI). The middleware designs memory pool, data pool, thread pool and task pool to improve the efficiency of concurrent processing, uses the rule dictionary, communication handshake and wait retransmission mechanism to ensure the data interaction security and reliability. This middleware can effectively meet the requirements of TRASI for data exchange with external users and system, significantly improve the data processing speed and efficiency, and promote the information technology and automation level of Aerospace TT&C Network Management Center (TNMC).

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.

The classical key distribution systems used for data transmission in networked microgrids (NMGs) rely on mathematical assumptions, which however can be broken by attacks from quantum computers. This paper addresses this quantum-era challenge by using quantum key distribution (QKD). Specifically, the novelty of this paper includes 1) a QKD-enabled communication architecture it devises for NMGs, 2) a real-time QKD- enabled NMGs testbed it builds in an RTDS environment, and 3) a novel two-level key pool sharing (TLKPS) strategy it designs to improve the system resilience against cyberattacks. Test results validate the effectiveness of the presented strategy, and provide insightful resources for building quantum-secure NMGs.

At present, the on-site safety problems of substations and critical power equipment are mainly through inspection methods. Still, manual inspection is difficult, time-consuming, and uninterrupted inspection is not possible. The current safety management is mainly guaranteed by rules and regulations and standardized operating procedures. In the on-site environment, it is very dependent on manual execution and confirmation, and the requirements for safety supervision and operating personnel are relatively high. However, the reliability, the continuity of control and patrol cannot be fully guaranteed, and it is easy to cause security vulnerabilities and cause security accidents due to personnel slackness. In response to this shortcoming, this paper uses edge computing and image processing techniques to discover security risks in time and designs a deep convolution attention mechanism network to perform image processing. Then the network is cropped and compressed so that it can be processed at the edge, and the results are aggregated to the cloud for unified management. A comprehensive security assessment module is designed in the cloud to conduct an overall risk assessment of the results reported by all edges, and give an alarm prompt. The experimental results in the real environment show the effectiveness of this method.