Biblio

Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.

In this short paper, we survey some work at the intersection of Artificial Intelligence (AI) and security that are based on game theoretic considerations, and particularly focus on the author's (our) contribution in these areas. One half of this paper focuses on applications of game theoretic and learning reasoning for addressing security applications such as in public safety and wildlife conservation. In the second half, we present recent work that attacks the learning components of these works, leading to sub-optimal defense allocation. We finally end by pointing to issues and potential research problems that can arise due to data quality in the real world.

Due to the intermittent nature of renewable energy sources, the implementation of energy storage systems (ESSs) is crucial for the reliable operation of microgrids. This paper proposes a peer-to-peer distributed secondary control scheme for accurate voltage restoration of distributed ESS units in a DC microgrid. The presented control framework only requires local and neighboring information to function. Besides, the ESSs communicate with each other through a sparse network in a discrete fashion compared to existing approaches based on continuous data exchange. This feature ensures reliability, expandability, and flexibility of the proposed strategy for a more practical realization of distributed control paradigm. A simulation case study is presented using MATLAB/Simulink to illustrate the performance and effectiveness of the proposed control strategy.

Embedded systems involve an integration of a large number of intellectual property (IP) blocks to shorten chip's time to market, in which, many IPs are acquired from the untrusted third-party suppliers. However, existing IP trust verification techniques cannot provide an adequate security assurance that no hardware Trojan was implanted inside the untrusted IPs. Hardware Trojans in untrusted IPs may cause processor program execution failures by tampering instruction code and return address. Therefore, this paper presents a secure RISC-V embedded system by integrating a Security Monitoring Unit (SMU), in which, instruction integrity monitoring by the fine-grained program basic blocks and function return address monitoring by the shadow stack are implemented, respectively. The hardware-assisted SMU is tested and validated that while CPU executes a CoreMark program, the SMU does not incur significant performance overhead on providing instruction security monitoring. And the proposed RISC-V embedded system satisfies good balance between performance overhead and resource consumption.

A vast volume of digital electronic health records is exchanged across the open network in this modern era. Cross all the existing security methods, encryption is a dependable method of data security. This study discusses an encryption technique for digital medical images that uses chaos combined with deoxyribonucleic acid (DNA). In fact, Rossler's and Lorenz's chaotic systems along with DNA encoding are used in the suggested medical image cryptographic system. Chaos is used to create a random key stream. The DNA encoding rules are then used to encode the key and the input original image. A hardware design of the proposed scheme is implemented on the Zedboard development kit. The experimental findings show that the proposed cryptosystem has strong security while maintaining acceptable hardware performances.

Image watermarking techniques provides security, reliability copyright protection for various multimedia contents. In this paper Integer Wavelet Transform Schur decomposition and Singular value decomposition (SVD) based image watermarking scheme is suggested for the integrity protection of dicom images. In the proposed technique 3-level Integer wavelet transform (IWT) is subjected into the Dicom ultrasound image of liver cover image and in HH sub-band Schur decomposition is applied. The upper triangular matrix obtained from Schur decomposition of HH sub-band is further processed with SVD to attain the singular values. The X-ray watermark image is pre-processed before embedding into cover image by applying 3-level IWT is applied into it and singular matrix of LL sub-band is embedded. The watermarked image is encrypted using Arnold chaotic encryption for its integrity protection. The performance of suggested scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping, contrast enhancement, gamma correction) and noise (Gaussian, speckle, Salt & Pepper Noise). The proposed technique provides strong robustness against various attacks and chaotic encryption provides integrity to watermarked image.

Model compression is one of the most preferred techniques for efficiently deploying deep neural networks (DNNs) on resource- constrained Internet of Things (IoT) platforms. However, the simply compressed model is often vulnerable to adversarial attacks, leading to a conflict between robustness and efficiency, especially for IoT devices exposed to complex real-world scenarios. We, for the first time, address this problem by developing a novel framework dubbed Magical-Decomposition to simultaneously enhance both robustness and efficiency for hardware. By leveraging a hardware-friendly model compression method called singular value decomposition, the defending algorithm can be supported by most of the existing DNN hardware accelerators. To step further, by using a recently developed DNN interpretation tool, the underlying scheme of how the adversarial accuracy can be increased in the compressed model is highlighted clearly. Ablation studies and extensive experiments under various attacks/models/datasets consistently validate the effectiveness and scalability of the proposed framework.

In the present era of the internet, image watermarking schemes are used to provide content authentication, security and reliability of various multimedia contents. In this paper image watermarking scheme which utilizes the properties of Integer Wavelet Transform (IWT), Schur decomposition and Singular value decomposition (SVD) based is proposed. In the suggested method, the cover image is subjected to a 3-level Integer wavelet transform (IWT), and the HH3 subband is subjected to Schur decomposition. In order to retrieve its singular values, the upper triangular matrix from the HH3 subband’s Schur decomposition is then subjected to SVD. The watermark image is first encrypted using a chaotic map, followed by the application of a 3-level IWT to the encrypted watermark and the usage of singular values of the LL-subband to embed by manipulating the singular values of the processed cover image. The proposed scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping) and noise (Gaussian, Salt & Pepper Noise). The suggested scheme provides strong robustness against numerous attacks and chaotic encryption provides security to watermark.

With the rapid development of cloud storage technology, an increasing number of enterprises and users choose to store data in the cloud, which can reduce the local overhead and ensure safe storage, sharing, and deletion. In cloud storage, safe data deletion is a critical and challenging problem. This paper proposes an assured data deletion scheme based on multi-authoritative users in the semi-trusted cloud storage scenario (MAU-AD), which aims to realize the secure management of the key without introducing any trusted third party and achieve assured deletion of cloud data. MAU-AD uses access policy graphs to achieve fine-grained access control and data sharing. Besides, the data security is guaranteed by mutual restriction between authoritative users, and the system robustness is improved by multiple authoritative users jointly managing keys. In addition, the traceability of misconduct in the system can be realized by blockchain technology. Through simulation experiments and comparison with related schemes, MAU-AD is proven safe and effective, and it provides a novel application scenario for the assured deletion of cloud storage data.

In this era, with a great extent of automation and connection, modern production processes are highly prone to cyber-attacks. The sensor-controller chain becomes an obvious target for attacks because sensors are commonly used to regulate production facilities. In this research, we introduce a new control configuration for the system, which is sensitive to time delay attacks (TDA), in which data transfer from the sensor to the controller is intentionally delayed. The attackers want to disrupt and damage the system by forcing controllers to use obsolete data about the system status. In order to improve the accuracy of delay identification and prediction, as well as erroneous limit and estimation for control, a new control structure is developed by an Internal Model Control (IMC) based Proportional-Integral-Derivative (PID) scheme with a fractional filter. An additional concept is included to mitigate the effect of time delay attack, i.e., the smith predictor. Simulation studies of the established control framework have been implemented with two numerical examples. The performance assessment of the proposed method has been done based on integral square error (ISE), integral absolute error (IAE) and total variation (TV).

The security control problem of cyber-physical system (CPS) under actuator attacks is studied in the paper. Considering the strict-feedback cyber-physical systems with external disturbance, a security control scheme is proposed by combining backstepping method and super-twisting sliding mode technology when the transmission control input signal of network layer is under false data injection(FDI) attack. Firstly, the unknown nonlinear function of the CPS is identified by Radial Basis Function Neural Network. Secondly, the backstepping method and super-twisting sliding mode algorithm are combined to eliminate the influence of actuator attack and ensure the robustness of the control system. Then, by Lyapunov stability theory, it is proved that the proposed control scheme can ensure that all signals in the closed-loop system are semi-global and ultimately uniformly bounded. Finally, the effectiveness of the proposed control scheme is verified by the inverted pendulum simulation.

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.

In this paper, we deal with the resilient consensus problem in networked control systems in which a group of agents are interacting with each other. A min-max-based resilient consensus algorithm has been proposed to help normal agents reach an agreement upon their state values in the presence of misbehaving ones. It is shown that the use of the developed algorithm will result in less computational load and fast convergence. Both synchronous and asynchronous update schemes for the network have been studied. Finally, the effectiveness of the proposed algorithm has been evaluated through numerical examples.

The robustness of supply chain networks (SCNs) against sequential topology attacks is significant for maintaining firm relationships and activities. Although SCNs have experienced many emergencies demonstrating that mixed failures exacerbate the impact of cascading failures, existing studies of sequential attacks rarely consider the influence of mixed failure modes on cascading failures. In this paper, a reinforcement learning (RL)-based sequential attack strategy is applied to SCNs with cascading failures that consider mixed failure modes. To solve the large state space search problem in SCNs, a deep Q-network (DQN) optimization framework combining deep neural networks (DNNs) and RL is proposed to extract features of state space. Then, it is compared with the traditional random-based, degree-based, and load-based sequential attack strategies. Simulation results on Barabasi-Albert (BA), Erdos-Renyi (ER), and Watts-Strogatz (WS) networks show that the proposed RL-based sequential attack strategy outperforms three existing sequential attack strategies. It can trigger cascading failures with greater influence. This work provides insights for effectively reducing failure propagation and improving the robustness of SCNs.

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.

CAPTCHAs are designed to prevent malicious bot programs from abusing websites. Most online service providers deploy audio CAPTCHAs as an alternative to text and image CAPTCHAs for visually impaired users. However, prior research investigating the security of audio CAPTCHAs found them highly vulnerable to automated attacks using Automatic Speech Recognition (ASR) systems. To improve the robustness of audio CAPTCHAs against automated abuses, we present the design and implementation of an audio adversarial CAPTCHA (aaeCAPTCHA) system in this paper. The aaeCAPTCHA system exploits audio adversarial examples as CAPTCHAs to prevent the ASR systems from automatically solving them. Furthermore, we conducted a rigorous security evaluation of our new audio CAPTCHA design against five state-of-the-art DNN-based ASR systems and three commercial Speech-to-Text (STT) services. Our experimental evaluations demonstrate that aaeCAPTCHA is highly secure against these speech recognition technologies, even when the attacker has complete knowledge of the current attacks against audio adversarial examples. We also conducted a usability evaluation of the proof-of-concept implementation of the aaeCAPTCHA scheme. Our results show that it achieves high robustness at a moderate usability cost compared to normal audio CAPTCHAs. Finally, our extensive analysis highlights that aaeCAPTCHA can significantly enhance the security and robustness of traditional audio CAPTCHA systems while maintaining similar usability.

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.

Co-salient object detection (CoSOD) has recently achieved significant progress and played a key role in retrieval-related tasks. However, it inevitably poses an entirely new safety and security issue, i.e., highly personal and sensitive content can potentially be extracting by powerful CoSOD methods. In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack. Specially, given an image selected from a group of images containing some common and salient objects, we aim to generate an adversarial version that can mislead CoSOD methods to predict incorrect co-salient regions. Note that, compared with general white-box adversarial attacks for classification, this new task faces two additional challenges: (1) low success rate due to the diverse appearance of images in the group; (2) low transferability across CoSOD methods due to the considerable difference between CoSOD pipelines. To address these challenges, we propose the very first blackbox joint adversarial exposure and noise attack (Jadena), where we jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function. Our method, without any information on the state-of-the-art CoSOD methods, leads to significant performance degradation on various co-saliency detection datasets and makes the co-salient objects undetectable. This can have strong practical benefits in properly securing the large number of personal photos currently shared on the Internet. Moreover, our method is potential to be utilized as a metric for evaluating the robustness of CoSOD methods.

Nowadays, data is essential in several fields, such as science, finance, medicine, and transportation, which means its value continues to rise. Relational databases are vulnerable to copyright threats when transmitted and shared as a carrier of data. The watermarking technique is seen as a partial solution to the problem of securing copyright ownership. However, most of them are currently restricted to numerical attributes in relational databases, limiting their versatility. Furthermore, they modify the source data to a large extent, failing to keep the characteristics of the original database, and they are susceptible to solid malicious attacks. This paper proposes a new robust reversible watermarking technique, Fields Based Inserting Position Tuples algorithm (FBIPT), for relational databases. FBIPT does not modify the original database directly; instead, it inserts some position tuples based on three Fields―Group Field, Feature Field, and Control Field. Field information can be calculated by numeric attributes and any attribute that can be transformed into binary bits. FBIPT technique retains all the characteristics of the source database, and experimental results prove the effectiveness of FBIPT and show its highly robust performance compared to state-of-the-art watermarking schemes.

The robustness of the encryption systems in all of their types depends on the key generation. Thus, an encryption system can be said robust if the generated key(s) are very complex and random which prevent attackers or other analytical tools to break the encryption system. This paper proposed an enhanced key generation based on iris image as biometric, to be implemented dynamically in both of authentication process and data encryption. The captured iris image during the authentication process will be stored in a cloud server to be used in the next login to decrypt data. While in the current login, the previously stored iris image in the cloud server would be used to decrypt data in the current session. The results showed that the generated key meets the required randomness for several NIST tests that is reasonable for one use. The strength of the proposed approach produced unrepeated keys for encryption and each key will be used once. The weakness of the produced key may be enhanced to become more random.

A novel secure physical layer key generation method for Connected and Autonomous Vehicles (CAVs) against an attacker is proposed under fading and Additive White Gaussian Noise (AWGN). In the proposed method, a random sequence key is added to the demodulated sequence to generate a unique pre-shared key (PSK) to enhance security. Extensive computer simulation results proved that an attacker cannot extract the same legitimate PSK generated by the received vehicle even if identical fading and AWGN parameters are used both for the legitimate vehicle and attacker.

Robustness verification of neural networks (NNs) is a challenging and significant problem, which draws great attention in recent years. Existing researches have shown that bound propagation is a scalable and effective method for robustness verification, and it can be implemented on GPUs and TPUs to get parallelized. However, the bound propagation methods naturally produce weak bound due to linear relaxations on the neurons, which may cause failure in verification. Although tightening techniques for simple ReLU networks have been explored, they are not applicable for NNs with general activation functions such as Sigmoid and Tanh. Improving robustness verification on these NNs is still challenging. In this paper, we propose a Branch-and-Bound (BaB) style method to address this problem. The proposed BaB procedure improves the weak bound by splitting the input domain of neurons into sub-domains and solving the corresponding sub-problems. We propose a generic heuristic function to determine the priority of neuron splitting by scoring the relaxation and impact of neurons. Moreover, we combine bound optimization with the BaB procedure to improve the weak bound. Experimental results demonstrate that the proposed method gains up to 35% improvement compared to the state-of-art CROWN method on Sigmoid and Tanh networks.

This paper presents a definition of a secure system and design principles, which help govern security policies within an embedded system. By understanding a secure system, a common system on chip (SoC) architecture is evaluated and their vulnerabilities explored. This effort helped define requirements for a framework for a secure and isolated SoC architecture for users to develop in. Throughout this paper, a SoC architecture framework for isolated domains has been proposed and its robustness verified against different attack scenarios. To support different levels of criticality and complexity in developing user applications, three computing domains were proposed: security and safety critical (SSC) domain, high performance (HP) domain, and sandbox domain. These domains allow for complex applications to be realized with varying levels of security. Isolation between different computing domains is established using consumer off the shelf (COTS) techniques and architectural components provided by the Zynq Ultrascale+ (ZU+) multiprocessor SoC (MPSoC). To the best of our knowledge, this is the first work that implements a secure system design on the ZU+ platform. There have been many other implementations in hardware security to mitigate certain attack scenarios such as side channel attacks, temporal attacks, hardware trojans, etc. However, our work is different than others, as it establishes the framework for isolated computing domains for secure applications and also verifies system security by attacking one domain from the others.

The globalization of the integrated circuit (IC) manufacturing industry has lured the adversary to come up with numerous malicious activities in the IC supply chain. Logic locking has risen to prominence as a proactive defense strategy against such threats. CAS-Lock (proposed in CHES'20), is an advanced logic locking technique that harnesses the concept of single-point function in providing SAT-attack resiliency. It is claimed to be powerful and efficient enough in mitigating existing state-of-the-art attacks against logic locking techniques. Despite the security robustness of CAS-Lock as claimed by the authors, we expose a serious vulnerability and by exploiting the same we devise a novel attack algorithm against CAS-Lock. The proposed attack can not only reveal the correct key but also the exact AND/OR structure of the implemented CAS-Lock design along with all the key gates utilized in both the blocks of CAS-Lock. It simply relies on the externally observable Distinguishing Input Patterns (DIPs) pertaining to a carefully chosen key simulation of the locked design without the requirement of structural analysis of any kind of the locked netlist. Our attack is successful against various AND/OR cascaded-chain configurations of CAS-Lock and reports 100% success rate in recovering the correct key. It has an attack complexity of \$\textbackslashmathcalO(m)\$, where \$m\$ denotes the number of DIPs obtained for an incorrect key simulation.