Biblio

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.

Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, and 48.14% lower inference time compared to the best performing prior works on automotive anomaly detection.

Fuzzing is a popular technique for finding soft-ware vulnerabilities. Despite their success, the state-of-art fuzzers will inevitably produce a large number of low-quality inputs. In recent years, Machine Learning (ML) based selection strategies have reported promising results. However, the existing ML-based fuzzers are limited by the lack of training data. Because the mutation strategy of fuzzing can not effectively generate useful input, it is prohibitively expensive to collect enough inputs to train models. In this paper, propose a generative adversarial networks based solution to generate a large number of inputs to solve the problem of insufficient data. We implement the proposal in the American Fuzzy Lop (AFL), and the experimental results show that it can find more crashes at the same time compared with the original AFL.

This paper uses the test tool provided by the Internet Protocol Version 6 (IPv6) Forum to test the protocol conformance of IPv6 devices. The installation and testing process of IPv6 Ready Logo protocol conformance test suite developed by TAHI PROJECT team is described in detail. This section describes the test content and evaluation criteria of the suite, analyzes the problems encountered during the installation and use of the suite, describes the method of analyzing the test results of the suite, and describes the test content added to the latest version of the test suite. The test suite can realize automatic testing, the test cases accurately reflect the requirements of the IPv6 protocol specification, can be used to judge whether IPv6-based Internet of Things(IoT) devices meets the relevant protocol standards.

An often overlooked but equally important aspect of unmanned aerial system (UAS) design is the security of their networking protocols and how they deal with cyberattacks. In this context, cyberattacks are malicious attempts to monitor or modify incoming and outgoing data from the system. These attacks could target anywhere in the system where a transfer of data occurs but are most common in the transfer of data between the control station and the UAS. A compromise in the networking system of a UAS could result in a variety of issues including increased network latency between the control station and the UAS, temporary loss of control over the UAS, or a complete loss of the UAS. A complete loss of the system could result in the UAS being disabled, crashing, or the attacker overtaking command and control of the platform, all of which would be done with little to no alert to the operator. Fortunately, the majority of higher-end, enterprise, and government UAS platforms are aware of these threats and take actions to mitigate them. However, as the consumer market continues to grow and prices continue to drop, network security may be overlooked or ignored in favor of producing the lowest cost product possible. Additionally, these commercial off-the-shelf UAS often use uniform, standardized frequency bands, autopilots, and security measures, meaning a cyberattack could be developed to affect a wide variety of models with minimal changes. This paper will focus on a low-cost educational-use UAS and test its resilience to a variety of cyberattack methods, including man-in-the-middle attacks, spoofing of data, and distributed denial-of-service attacks. Following this experiment will be a discussion of current cybersecurity practices for counteracting these attacks and how they can be applied onboard a UAS. Although in this case the cyberattacks were tested against a simpler platform, the methods discussed are applicable to any UAS platform attempting to defend against such cyberattack methods.

We demonstrate an in-house built Endpoint Detection and Response (EDR) for linux systems using open-sourced tools like Osquery and Elastic. The advantage of building an in-house EDR tools against using commercial EDR tools provides both the knowledge and the technical capability to detect and investigate security incidents. We discuss the architecture of the tools and advantages it offers. Specifically, in our method all the endpoint logs are collected at a common server which we leverage to perform correlation between events happening on different endpoints and automatically detect threats like pivoting and lateral movements. We discuss various attacks that can be detected by our tool.

Cognitive Radio Network makes intelligent use of the spectrum resources. However, spectrum sensing is vulnerable to numerous harmful assaults. To lower the network's performance, hackers attempt to alter the sensed result. In the fusion centre, blockchain technology is used to make broad judgments on spectrum sensing in order to detect and thwart hostile activities. The sensed local results are hashed using the SHA 3 technique. This improves spectrum sensing precision and effectively thwarts harmful attacks. In comparison to other established techniques like equal gain combining, the simulation results demonstrate higher detection probability and sensing precision. Thus, employing Blockchain technology, cognitive radio network security can be significantly enhanced.

Intelligent transportation systems, such as connected vehicles, are able to establish real-time, optimized and collision-free communication with the surrounding ecosystem. Introducing the internet of things (IoT) in connected vehicles relies on deployment of massive scale sensors, actuators, electronic control units (ECUs) and antennas with embedded software and communication technologies. Combined with the lack of designed-in security for sensors and ECUs, this creates challenges for security engineers and architects to identify, understand and analyze threats so that actions can be taken to protect the system assets. This paper proposes a novel STRIDE-based threat model for IoT sensors in connected vehicle networks aimed at addressing these challenges. Using a reference architecture of a connected vehicle, we identify system assets in connected vehicle sub-systems such as devices and peripherals that mostly involve sensors. Moreover, we provide a prioritized set of security recommendations, with consideration to the feasibility and deployment challenges, which enables practical applicability of the developed threat model to help specify security requirements to protect critical assets within the sensor network.

With the rapid development of artificial intelligence technology, deepfake technology based on deep learning is receiving more and more attention from society or the industry. While enriching people's cultural and entertainment life, in-depth fakes technology has also caused many social problems, especially potential risks to managing network credible identities. With the continuous advancement of deep fakes technology, the security threats and trust crisis caused by it will become more serious. It is urgent to take adequate measures to curb the abuse risk of deep fakes. The article first introduces the principles and characteristics of deep fakes technology and then deeply analyzes its severe challenges to network trusted identity management. Finally, it researches the supervision and technical level and puts forward targeted preventive countermeasures.

Cybersecurity attacks, which have many business impacts, continuously become more intelligent and complex. These attacks take the form of a combination of various attack elements. APT attacks reflect this characteristic well. To defend against APT attacks, organizations should sufficiently understand these attacks based on the attack elements and their relations and actively defend against these attacks in multiple dimensions. Most organizations perform risk management to manage their information security. Generally, they use the information system risk assessment (ISRA). However, the method has difficulties supporting sufficiently analyzing security risks and actively responding to these attacks due to the limitations of asset-driven qualitative evaluation activities. In this paper, we propose a threat-driven risk assessment method. This method can evaluate how dangerous APT attacks are for an organization, analyze security risks from multiple perspectives, and support establishing an adaptive security strategy.

The Inertial Navigation System(INS) and Doppler Velocity Logs(DVL) which are used frequently on autonomous underwater vehicles can be fused under different types of integration architectures. These architectures differ in terms of algorithm requirements and complexity. DVL may experience acoustic beam losses during operation due to environmental factors and abilities of the sensor. In these situations, radial velocity information cannot be received from lost acoustic beam. In this paper, the performances of INS and DVL integration under tightly and loosely coupled architectures are comparatively presented with simulations. In the tightly coupled approach, navigation filter is updated with solely available beam measurements by using sequential measurement update method, and the sensitivity of this method is investigated for acoustic beam losses.

Trip planning, which targets at planning a trip consisting of several ordered Points of Interest (POIs) under user-provided constraints, has long been treated as an important application for location-based services. The goal of trip planning is to maximize the chance that the users will follow the planned trip while it is difficult to directly quantify and optimize the chance. Conventional methods either leverage statistical analysis to rank POIs to form a trip or generate trips following pre-defined objectives based on constraint programming to bypass such a problem. However, these methods may fail to reflect the complex latent patterns hidden in the human mobility data. On the other hand, though there are a few deep learning-based trip recommendation methods, these methods still cannot handle the time budget constraint so far. To this end, we propose a TIme-aware Neural Trip Planning (TINT) framework to tackle the above challenges. First of all, we devise a novel attention-based encoder-decoder trip generator that can learn the correlations among POIs and generate trips under given constraints. Then, we propose a specially-designed reinforcement learning (RL) paradigm to directly optimize the objective to obtain an optimal trip generator. For this purpose, we introduce a discriminator, which distinguishes the generated trips from real-life trips taken by users, to provide reward signals to optimize the generator. Subsequently, to ensure the feedback from the discriminator is always instructive, we integrate an adversarial learning strategy into the RL paradigm to update the trip generator and the discriminator alternately. Moreover, we devise a novel pre-training schema to speed up the convergence for an efficient training process. Extensive experiments on four real-world datasets validate the effectiveness and efficiency of our framework, which shows that TINT could remarkably outperform the state-of-the-art baselines within short response time.

The Time-Triggered Architecture (TTA) presents a blueprint for building safe and real-time constrained distributed systems, based on a set of orthogonal concepts that make extensive use of the availability of a globally consistent notion of time and a priori knowledge of events. Although the TTA tolerates arbitrary failures of any of its nodes by architectural means (active node replication, a membership service, and bus guardians), the design of these means considers only accidental faults. However, distributed safety- and real-time critical systems have been emerging into more open and interconnected systems, operating autonomously for prolonged times and interfacing with other possibly non-real-time systems. Therefore, the existence of vulnerabilities that adversaries may exploit to compromise system safety cannot be ruled out. In this paper, we discuss potential targeted attacks capable of bypassing TTA's fault-tolerance mechanisms and demonstrate how two well-known recovery techniques - proactive and reactive rejuvenation - can be incorporated into TTA to reduce the window of vulnerability for attacks without introducing extensive and costly changes.

This paper presents a novel approach for a facial security system using elliptic curve cryptography. Face images extracted from input video are encrypted before sending to a remote server. The input face images are completely encrypted by mapping each pixel value of the detected face from the input video frame to a point on an elliptic curve. The original image can be recovered when needed using the elliptic curve cryptography decryption function. Specifically, we modify point multiplication designed for projective coordinates and apply the modified approach in affine coordinates to speed up scalar point multiplication operation. Image encryption and decryption operations are also facilitated using our existing scheme. Simulation results on Visual Studio demonstrate that the proposed systems help accelerate encryption and decryption operations while maintaining information confidentiality.

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.

In recent years, body-worn RFID and NFC (near field communication) devices have become one of the principal technologies concurring to the rise of healthcare internet of thing (H-IoT) systems. Similarly, points of care (PoCs) moved increasingly closer to patients to reduce the costs while supporting precision medicine and improving chronic illness management, thanks to timely and frequent feedback from the patients themselves. A typical PoC involves medical sensing devices capable of sampling human health, personal equipment with communications and computing capabilities (smartphone or tablet) and a secure software environment for data transmission to medical centers. Hybrid platforms simultaneously employing NFC and ultra-high frequency (UHF) RFID could be successfully developed for the first sensing layer. An application example of the proposed hybrid system for the monitoring of acute myocardial infarction (AMI) survivors details how the combined use of NFC and UHF-RFID in the same PoC can support the multifaceted need of AMI survivors while protecting the sensitive data on the patient’s health.

This paper discusses the outcome of combining insider threat agent taxonomies with the aim of enhancing insider threat detection. The objectives sought to explore taxonomy combinations and investigate threat sophistication from the taxonomy combinations. Investigations revealed the plausibility of combining the various taxonomy categories to derive a new taxonomy. An observation on category combinations yielded the introduction of the concept of a threat path. The proposed taxonomy tree consisted of more than a million threat-paths obtained using a formula from combinatorics analysis. The taxonomy category combinations thus increase the insider threat landscape and hence the gap between insider threat agent sophistication and countermeasures. On the defensive side, knowledge of insider threat agent taxonomy category combinations has the potential to enhance defensive countermeasure tactics, techniques and procedures, thus increasing the chances of insider threat detection.

Key management for self-organized wireless ad-hoc networks using peer-to-peer (P2P) keys is the primary goal of this article (SOWANs). Currently, wireless networks have centralized security architectures, making them difficult to secure. In most cases, ad-hoc wireless networks are not connected to trusted authorities or central servers. They are more prone to fragmentation and disintegration as a result of node and link failures. Traditional security solutions that rely on online trusted authorities do not work together to protect networks that are not planned. With open wireless networks, anyone can join or leave at any time with the right equipment, and no third party is required to verify their identity. These networks are best suited for this proposed method. Each node can make, distribute, and revoke its keying material in this paper. A minimal amount of communication and computation is required to accomplish this task. So that they can authenticate one another and create shared keys, nodes in the self-organized version of the system must communicate via a secure side channel between the users' devices.

DDoS attacks produce a lot of traffic on the network. DDoS attacks may be fought in a novel method thanks to the rise of Software Defined Networking (SDN). DDoS detection and data gathering may lead to larger system load utilization among SDN as well as systems, much expense of SDN, slow reaction period to DDoS if they are conducted at regular intervals. Using the Identification Retrieval algorithm, we offer a new DDoS detection framework for detecting resource scarcity type DDoS attacks. In designed to check low-density DDoS attacks, we employ a combination of network traffic characteristics. The KSVD technique is used to generate a dictionary of network traffic parameters. In addition to providing legitimate and attack traffic models for dictionary construction, the suggested technique may be used to network traffic as well. Matching Pursuit and Wavelet-based DDoS detection algorithms are also implemented and compared using two separate data sets. Despite the difficulties in identifying LR-DoS attacks, the results of the study show that our technique has a detection accuracy of 89%. DDoS attacks are explained for each type of DDoS, and how SDN weaknesses may be exploited. We conclude that machine learning-based DDoS detection mechanisms and cutoff point DDoS detection techniques are the two most prevalent methods used to identify DDoS attacks in SDN. More significantly, the generational process, benefits, and limitations of each DDoS detection system are explained. This is the case in our testing environment, where the intrusion detection system (IDS) is able to block all previously identified threats

Recent works have empirically shown that neural network interpretability is susceptible to malicious manipulations. However, existing attacks against Interpretable Deep Learning Systems (IDLSes) all focus on the white-box setting, which is obviously unpractical in real-world scenarios. In this paper, we make the first attempt to attack IDLSes in the decision-based black-box setting. We propose a new framework called Dual Black-box Adversarial Attack (DBAA) which can generate adversarial examples that are misclassified as the target class, yet have very similar interpretations to their benign cases. We conduct comprehensive experiments on different combinations of classifiers and interpreters to illustrate the effectiveness of DBAA. Empirical results show that in all the cases, DBAA achieves high attack success rates and Intersection over Union (IoU) scores.

Differential privacy is a widely-used metric, which provides rigorous privacy definitions and strong privacy guarantees. Much of the existing studies on differential privacy are based on datasets where the tuples are independent, and thus are not suitable for correlated data protection. In this paper, we focus on correlated differential privacy, by taking the data correlations and the prior knowledge of the initial data into account. The data correlations are modeled by Bayesian conditional probabilities, and the prior knowledge refers to the exact values of the data. We propose general correlated differential privacy conditions for the discrete and continuous random noise-adding mechanisms, respectively. In case that the conditions are inaccurate due to the insufficient prior knowledge, we introduce the tuple dependence based on rough set theory to improve the correlated differential privacy conditions. The obtained theoretical results reveal the relationship between the correlations and the privacy parameters. Moreover, the improved privacy condition helps strengthen the mechanism utility. Finally, evaluations are conducted over a micro-grid system to verify the privacy protection levels and utility guaranteed by correlated differential private mechanisms.

In Production System Engineering (PSE), domain experts aim at effectively and efficiently analyzing and mitigating information security risks to product and process qualities for manufacturing. However, traditional security standards do not connect security analysis to the value stream of the production system nor to production quality requirements. This paper aims at facilitating security analysis for production quality already in the design phase of PSE. In this paper, we (i) identify the connection between security and production quality, and (ii) introduce the Production Security Network (PSN) to efficiently derive reusable security requirements and design patterns for PSE. We evaluate the PSN with threat scenarios in a feasibility study. The study results indicate that the PSN satisfies the requirements for systematic security analysis. The design patterns provide a good foundation for improving the communication of domain experts by connecting security and quality concerns.

Classic black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model. However, these substitute models need to be trained by target models' training data, which is hard to acquire due to privacy or transmission reasons. Recognizing the limited availability of real data for adversarial queries, recent works proposed to train substitute models in a data-free black-box scenario. However, their generative adversarial networks (GANs) based framework suffers from the convergence failure and the model collapse, resulting in low efficiency. In this paper, by rethinking the collaborative relationship between the generator and the substitute model, we design a novel black-box attack framework. The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate. The comprehensive experiments over six datasets demonstrate the effectiveness of our method against the state-of-the-art attacks. Especially, we conduct both label-only and probability-only attacks on the Microsoft Azure online model, and achieve a 100% attack success rate with only 0.46% query budget of the SOTA method [49].

DDoS attacks still represent a severe threat to network services. While there are more or less workable solutions to defend against these attacks, there is a significant space for further research regarding automation of reactions and subsequent management. In this paper, we focus on one piece of the whole puzzle. We strive to automatically infer filtering rules which are specific to the current DoS attack to decrease the time to mitigation. We employ a machine learning technique to create a model of the traffic mix based on observing network traffic during the attack and normal period. The model is converted into the filtering rules. We evaluate our approach with various setups of hyperparameters. The results of our experiments show that the proposed approach is feasible in terms of the capability of inferring successful filtering rules.

Axie infinity is a complicated card game with a huge-scale action space. This makes it difficult to solve this challenge using generic Reinforcement Learning (RL) algorithms. We propose a hybrid RL framework to learn action representations and game strategies. To avoid evaluating every action in the large feasible action set, our method evaluates actions in a fixed-size set which is determined using action representations. We compare the performance of our method with two baseline methods in terms of their sample efficiency and the winning rates of the trained models. We empirically show that our method achieves an overall best winning rate and the best sample efficiency among the three methods.