Biblio

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2022-12-20
Lin, Xuanwei, Dong, Chen, Liu, Ximeng, Zhang, Yuanyuan.  2022.  SPA: An Efficient Adversarial Attack on Spiking Neural Networks using Spike Probabilistic. 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). :366–375.
With the future 6G era, spiking neural networks (SNNs) can be powerful processing tools in various areas due to their strong artificial intelligence (AI) processing capabilities, such as biometric recognition, AI robotics, autonomous drive, and healthcare. However, within Cyber Physical System (CPS), SNNs are surprisingly vulnerable to adversarial examples generated by benign samples with human-imperceptible noise, this will lead to serious consequences such as face recognition anomalies, autonomous drive-out of control, and wrong medical diagnosis. Only by fully understanding the principles of adversarial attacks with adversarial samples can we defend against them. Nowadays, most existing adversarial attacks result in a severe accuracy degradation to trained SNNs. Still, the critical issue is that they only generate adversarial samples by randomly adding, deleting, and flipping spike trains, making them easy to identify by filters, even by human eyes. Besides, the attack performance and speed also can be improved further. Hence, Spike Probabilistic Attack (SPA) is presented in this paper and aims to generate adversarial samples with more minor perturbations, greater model accuracy degradation, and faster iteration. SPA uses Poisson coding to generate spikes as probabilities, directly converting input data into spikes for faster speed and generating uniformly distributed perturbation for better attack performance. Moreover, an objective function is constructed for minor perturbations and keeping attack success rate, which speeds up the convergence by adjusting parameters. Both white-box and black-box settings are conducted to evaluate the merits of SPA. Experimental results show the model's accuracy under white-box attack decreases by 9.2S% 31.1S% better than others, and average success rates are 74.87% under the black-box setting. The experimental results indicate that SPA has better attack performance than other existing attacks in the white-box and better transferability performance in the black-box setting,
2023-05-30
Wang, Xuyang, Hu, Aiqun, Huang, Yongming, Fan, Xiangning.  2022.  The spatial cross-correlation of received voltage envelopes under non-line-of-sight. 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE). :303—308.
Physical-layer key (PLK) generation scheme is a new key generation scheme based on wireless channel reciprocity. However, the security of physical layer keys still lacks sufficient theoretical support in the presence of eavesdropping attacks until now, which affects the promotion in practical applications. By analyzing the propagation mode of multipath signals under non-line-of-sight (nLoS), an improved spatial cross-correlation model is constructed, where the spatial cross-correlation is between eavesdropping channel and legitimate channel. Results show that compared with the multipath and obstacle distribution of the channel, the azimuth and distance between the eavesdropper and the eavesdropped user have a greater impact on the cross-correlation.
2023-02-17
Ruaro, Nicola, Pagani, Fabio, Ortolani, Stefano, Kruegel, Christopher, Vigna, Giovanni.  2022.  SYMBEXCEL: Automated Analysis and Understanding of Malicious Excel 4.0 Macros. 2022 IEEE Symposium on Security and Privacy (SP). :1066–1081.
Malicious software (malware) poses a significant threat to the security of our networks and users. In the ever-evolving malware landscape, Excel 4.0 Office macros (XL4) have recently become an important attack vector. These macros are often hidden within apparently legitimate documents and under several layers of obfuscation. As such, they are difficult to analyze using static analysis techniques. Moreover, the analysis in a dynamic analysis environment (a sandbox) is challenging because the macros execute correctly only under specific environmental conditions that are not always easy to create. This paper presents SYMBEXCEL, a novel solution that leverages symbolic execution to deobfuscate and analyze Excel 4.0 macros automatically. Our approach proceeds in three stages: (1) The malicious document is parsed and loaded in memory; (2) Our symbolic execution engine executes the XL4 formulas; and (3) Our Engine concretizes any symbolic values encountered during the symbolic exploration, therefore evaluating the execution of each macro under a broad range of (meaningful) environment configurations. SYMBEXCEL significantly outperforms existing deobfuscation tools, allowing us to reliably extract Indicators of Compromise (IoCs) and other critical forensics information. Our experiments demonstrate the effectiveness of our approach, especially in deobfuscating novel malicious documents that make heavy use of environment variables and are often not identified by commercial anti-virus software.
ISSN: 2375-1207
2023-02-03
Liu, Qin, Yang, Jiamin, Jiang, Hongbo, Wu, Jie, Peng, Tao, Wang, Tian, Wang, Guojun.  2022.  When Deep Learning Meets Steganography: Protecting Inference Privacy in the Dark. IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. :590–599.
While cloud-based deep learning benefits for high-accuracy inference, it leads to potential privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on exploring the feasibility of steganography in preserving inference privacy. Specifically, we devise GHOST and GHOST+, two private inference solutions employing steganography to make sensitive images invisible in the inference phase. Motivated by the fact that deep neural networks (DNNs) are inherently vulnerable to adversarial attacks, our main idea is turning this vulnerability into the weapon for data privacy, enabling the DNN to misclassify a stego image into the class of the sensitive image hidden in it. The main difference is that GHOST retrains the DNN into a poisoned network to learn the hidden features of sensitive images, but GHOST+ leverages a generative adversarial network (GAN) to produce adversarial perturbations without altering the DNN. For enhanced privacy and a better computation-communication trade-off, both solutions adopt the edge-cloud collaborative framework. Compared with the previous solutions, this is the first work that successfully integrates steganography and the nature of DNNs to achieve private inference while ensuring high accuracy. Extensive experiments validate that steganography has excellent ability in accuracy-aware privacy protection of deep learning.
ISSN: 2641-9874
2023-06-09
Zhao, Junjie, Xu, Bingfeng, Chen, Xinkai, Wang, Bo, He, Gaofeng.  2022.  Analysis Method of Security Critical Components of Industrial Cyber Physical System based on SysML. 2022 Tenth International Conference on Advanced Cloud and Big Data (CBD). :270—275.
To solve the problem of an excessive number of component vulnerabilities and limited defense resources in industrial cyber physical systems, a method for analyzing security critical components of system is proposed. Firstly, the components and vulnerability information in the system are modeled based on SysML block definition diagram. Secondly, as SysML block definition diagram is challenging to support direct analysis, a block security dependency graph model is proposed. On this basis, the transformation rules from SysML block definition graph to block security dependency graph are established according to the structure of block definition graph and its vulnerability information. Then, the calculation method of component security importance is proposed, and a security critical component analysis tool is designed and implemented. Finally, an example of a Drone system is given to illustrate the effectiveness of the proposed method. The application of this method can provide theoretical and technical support for selecting key defense components in the industrial cyber physical system.
2023-07-21
Shiqi, Li, Yinghui, Han.  2022.  Detection of Bad Data and False Data Injection Based on Back-Propagation Neural Network. 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia). :101—105.
Power system state estimation is an essential tool for monitoring the operating conditions of the grid. However, the collected measurements may not always be reliable due to bad data from various faults as well as the increasing potential of being exposed to cyber-attacks, particularly from data injection attacks. To enhance the accuracy of state estimation, this paper presents a back-propagation neural network to detect and identify bad data and false data injections. A variety of training data exhibiting different statistical properties were used for training. The developed strategy was tested on the IEEE 30-bus and 118-bus power systems using MATLAB. Simulation results revealed the feasibility of the method for the detection and differentiation of bad data and false data injections in various operating scenarios.
2023-06-22
Awasthi, Divyanshu, Srivastava, Vinay Kumar.  2022.  Dual Image Watermarking using Hessenberg decomposition and RDWT-DCT-SVD in YCbCr color space. 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). :1–6.
A dual-image watermarking approach is presented in this research. The presented work utilizes the properties of Hessenberg decomposition, Redundant discrete wavelet transform (RDWT), Discrete cosine transform (DCT) and Singular value decomposition (SVD). For watermarking, the YCbCr color space is employed. Two watermark logos are for embedding. A YCbCr format conversion is performed on the RGB input image. The host image's Y and Cb components are divided into various sub-bands using RDWT. The Hessenberg decomposition is applied on high-low and low-high components. After that, SVD is applied to get dominant matrices. Two different logos are used for watermarking. Apply RDWT on both watermark images. After that, apply DCT and SVD to get dominant matrices of logos. Add dominant matrices of input host and watermark images to get the watermarked image. Average PSNR, MSE, Structural similarity index measurement (SSIM) and Normalized correlation coefficient (NCC) are used as the performance parameters. The resilience of the presented work is tested against various attacks such as Gaussian low pass filter, Speckle noise attack, Salt and Pepper, Gaussian noise, Rotation, Median and Average filter, Sharpening, Histogram equalization and JPEG compression. The presented scheme is robust and imperceptible when compared with other schemes.
2023-05-26
Basan, Elena, Mikhailova, Vasilisa, Shulika, Maria.  2022.  Exploring Security Testing Methods for Cyber-Physical Systems. 2022 International Siberian Conference on Control and Communications (SIBCON). :1—7.
A methodology for studying the level of security for various types of CPS through the analysis of the consequences was developed during the research process. An analysis of the architecture of cyber-physical systems was carried out, vulnerabilities and threats of specific devices were identified, a list of possible information attacks and their consequences after the exploitation of vulnerabilities was identified. The object of research is models of cyber-physical systems, including IoT devices, microcomputers, various sensors that function through communication channels, organized by cyber-physical objects. The main subjects of this investigation are methods and means of security testing of cyber-physical systems (CPS). The main objective of this investigation is to update the problem of security in cyber-physical systems, to analyze the security of these systems. In practice, the testing methodology for the cyber-physical system “Smart Factory” was implemented, which simulates the operation of a real CPS, with different types of links and protocols used.
2023-05-12
Yang, Wendi, Zhang, Ming, Li, Chuan, Wang, Zutao, Xiao, Menghan, Li, Jiawei, Li, Dingchen, Zheng, Wei.  2022.  Influence of Magnetic Field on Corona Discharge Characteristics under Different Humidity Conditions. 2022 IEEE 3rd China International Youth Conference on Electrical Engineering (CIYCEE). :1–7.
The humidity in the air parameters has an impact on the characteristics of corona discharge, and the magnetic field also affects the electron movement of corona discharge. We build a constant humidity chamber and use a wire-mesh electrode device to study the effects of humidity and magnetic field on the discharge. The enhancement of the discharge by humidity is caused by the combination of water vapor molecules and ions generated by the discharge into hydrated ions. By building a “water flow channel” between the high voltage wire electrode and the ground mesh electrode, the ions can pass more smoothly, thereby enhanced discharge. The ions are subjected to the Lorentz force in the electromagnetic field environment, the motion state of the ions changes, and the larmor motion in the electromagnetic field increases the movement path, the collision between the gas molecules increases, and more charged particles are generated, which increases the discharge current. During the period, the electrons and ions generated by the ionization of the wire electrode leave the ionization zone faster, which reduces the inhibitory effect of the ion aggregation on the discharge and promotes the discharge.
2023-04-28
López, Hiram H., Matthews, Gretchen L., Valvo, Daniel.  2022.  Secure MatDot codes: a secure, distributed matrix multiplication scheme. 2022 IEEE Information Theory Workshop (ITW). :149–154.
This paper presents secure MatDot codes, a family of evaluation codes that support secure distributed matrix multiplication via a careful selection of evaluation points that exploit the properties of the dual code. We show that the secure MatDot codes provide security against the user by using locally recoverable codes. These new codes complement the recently studied discrete Fourier transform codes for distributed matrix multiplication schemes that also provide security against the user. There are scenarios where the associated costs are the same for both families and instances where the secure MatDot codes offer a lower cost. In addition, the secure MatDot code provides an alternative way to handle the matrix multiplication by identifying the fastest servers in advance. In this way, it can determine a product using fewer servers, specified in advance, than the MatDot codes which achieve the optimal recovery threshold for distributed matrix multiplication schemes.
Pham, Quang Duc, Hayasaki, Yoshio.  2022.  Time of flight three-dimensional imaging camera using compressive sampling technique with sparse frequency intensity modulation light source. 2022 IEEE CPMT Symposium Japan (ICSJ). :168–171.
The camera constructed by a megahertz range intensity modulation active light source and a kilo-frame rate range fast camera based on compressive sensing (CS) technique for three-dimensional (3D) image acquisition was proposed in this research.
ISSN: 2475-8418
Lotfollahi, Mahsa, Tran, Nguyen, Gajjela, Chalapathi, Berisha, Sebastian, Han, Zhu, Mayerich, David, Reddy, Rohith.  2022.  Adaptive Compressive Sampling for Mid-Infrared Spectroscopic Imaging. 2022 IEEE International Conference on Image Processing (ICIP). :2336–2340.
Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.
ISSN: 2381-8549
2023-06-22
Ramneet, Mudita, Gupta, Deepali.  2022.  ASMBoT: An Intelligent Sanitizing Robot in the Coronavirus Outbreak. 2022 1st IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA). :106–109.
Technology plays a vital role in our lives to meet basic hygiene necessities. Currently, the whole world is facing an epidemic situation and the practice of using sanitizers is common nowadays. Sanitizers are used by people to sanitize their hands and bodies. It is also used for sanitizing objects that come into contact with the machine. While sanitizing a small area, people manage to sanitize via pumps, but it becomes difficult to sanitize the same area every day. One of the most severe sanitation concerns is a simple, economic and efficient method to adequately clean the indoor and outdoor environments. In particular, effective sanitization is required for people working in a clinical environment. Recently, some commonly used sanitizer techniques include electric sanitizer spray guns, electric sanitizer disinfectants, etc. However, these sanitizers are not automated, which means a person is required to roam personally with the device to every place to spray the disinfectant or sanitize an area. Therefore, a novel, cost-effective automatic sanitizing machine (ASM) named ASMBoT is designed that can dispense the sanitizer effectively by solving the aforementioned problems.
2023-07-11
Sennewald, Tom, Song, Xinya, Westermann, Dirk.  2022.  Assistance System to Consider Dynamic Phenomena for Secure System Operation. 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). :1—5.
This contribution provides the implementation of a digital twin-based assistance system to be used in future control rooms. By applying parameter estimation methods, the dynamic model in the digital twin is an accurate representation of the physical system. Therefore, a dynamic security assessment (DSA) that is highly dependent on a correctly parameterized dynamic model, can give more reliable information to a system operator in the control room. The assistance system is studied on the Cigré TB 536 benchmark system with an obscured set of machine parameters. Through the proposed parameter estimation approach the original parameters could be estimated, changing, and increasing the statement of the DSA in regard to imminent instabilities.
2023-01-20
Shyshkin, Oleksandr.  2022.  Cybersecurity Providing for Maritime Automatic Identification System. 2022 IEEE 41st International Conference on Electronics and Nanotechnology (ELNANO). :736–740.

Automatic Identification System (AIS) plays a leading role in maritime navigation, traffic control, local and global maritime situational awareness. Today, the reliable and secure AIS operation is threatened by probable cyber attacks such as imitation of ghost vessels, false distress or security messages, or fake virtual aids-to-navigation. We propose a method for ensuring the authentication and integrity of AIS messages based on the use of the Message Authentication Code scheme and digital watermarking (WM) technology to organize an additional tag transmission channel. The method provides full compatibility with the existing AIS functionality.

2023-06-22
Satyanarayana, D, Alasmi, Aisha Said.  2022.  Detection and Mitigation of DDOS based Attacks using Machine Learning Algorithm. 2022 International Conference on Cyber Resilience (ICCR). :1–5.

In recent decades, a Distributed Denial of Service (DDoS) attack is one of the most expensive attacks for business organizations. The DDoS is a form of cyber-attack that disrupts the operation of computer resources and networks. As technology advances, the styles and tools used in these attacks become more diverse. These attacks are increased in frequency, volume, and intensity, and they can quickly disrupt the victim, resulting in a significant financial loss. In this paper, it is described the significance of DDOS attacks and propose a new method for detecting and mitigating the DDOS attacks by analyzing the traffics coming to the server from the BOTNET in attacking system. The process of analyzing the requests coming from the BOTNET uses the Machine learning algorithm in the decision making. The simulation is carried out and the results analyze the DDOS attack.

2023-02-02
Mansoor, Niloofar, Muske, Tukaram, Serebrenik, Alexander, Sharif, Bonita.  2022.  An Empirical Assessment on Merging and Repositioning of Static Analysis Alarms. 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM). :219–229.
Static analysis tools generate a large number of alarms that require manual inspection. In prior work, repositioning of alarms is proposed to (1) merge multiple similar alarms together and replace them by a fewer alarms, and (2) report alarms as close as possible to the causes for their generation. The premise is that the proposed merging and repositioning of alarms will reduce the manual inspection effort. To evaluate the premise, this paper presents an empirical study with 249 developers on the proposed merging and repositioning of static alarms. The study is conducted using static analysis alarms generated on \$C\$ programs, where the alarms are representative of the merging vs. non-merging and repositioning vs. non-repositioning situations in real-life code. Developers were asked to manually inspect and determine whether assertions added corresponding to alarms in \$C\$ code hold. Additionally, two spatial cognitive tests are also done to determine relationship in performance. The empirical evaluation results indicate that, in contrast to expectations, there was no evidence that merging and repositioning of alarms reduces manual inspection effort or improves the inspection accuracy (at times a negative impact was found). Results on cognitive abilities correlated with comprehension and alarm inspection accuracy.
2023-08-16
Liu, Lisa, Engelen, Gints, Lynar, Timothy, Essam, Daryl, Joosen, Wouter.  2022.  Error Prevalence in NIDS datasets: A Case Study on CIC-IDS-2017 and CSE-CIC-IDS-2018. 2022 IEEE Conference on Communications and Network Security (CNS). :254—262.
Benchmark datasets are heavily depended upon by the research community to validate theoretical findings and track progression in the state-of-the-art. NIDS dataset creation presents numerous challenges on account of the volume, heterogeneity, and complexity of network traffic, making the process labor intensive, and thus, prone to error. This paper provides a critical review of CIC-IDS-2017 and CIC-CSE-IDS-2018, datasets which have seen extensive usage in the NIDS literature, and are currently considered primary benchmarking datasets for NIDS. We report a large number of previously undocumented errors throughout the dataset creation lifecycle, including in attack orchestration, feature generation, documentation, and labeling. The errors destabilize the results and challenge the findings of numerous publications that have relied on it as a benchmark. We demonstrate the implications of these errors through several experiments. We provide comprehensive documentation to summarize the discovery of these issues, as well as a fully-recreated dataset, with labeling logic that has been reverse-engineered, corrected, and made publicly available for the first time. We demonstrate the implications of dataset errors through a series of experiments. The findings serve to remind the research community of common pitfalls with dataset creation processes, and of the need to be vigilant when adopting new datasets. Lastly, we strongly recommend the release of labeling logic for any dataset released, to ensure full transparency.
2023-05-11
Teo, Jia Wei, Gunawan, Sean, Biswas, Partha P., Mashima, Daisuke.  2022.  Evaluating Synthetic Datasets for Training Machine Learning Models to Detect Malicious Commands. 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :315–321.
Electrical substations in power grid act as the critical interface points for the transmission and distribution networks. Over the years, digital technology has been integrated into the substations for remote control and automation. As a result, substations are more prone to cyber attacks and exposed to digital vulnerabilities. One of the notable cyber attack vectors is the malicious command injection, which can lead to shutting down of substations and subsequently power outages as demonstrated in Ukraine Power Plant Attack in 2015. Prevailing measures based on cyber rules (e.g., firewalls and intrusion detection systems) are often inadequate to detect advanced and stealthy attacks that use legitimate-looking measurements or control messages to cause physical damage. Additionally, defenses that use physics-based approaches (e.g., power flow simulation, state estimation, etc.) to detect malicious commands suffer from high latency. Machine learning serves as a potential solution in detecting command injection attacks with high accuracy and low latency. However, sufficient datasets are not readily available to train and evaluate the machine learning models. In this paper, focusing on this particular challenge, we discuss various approaches for the generation of synthetic data that can be used to train the machine learning models. Further, we evaluate the models trained with the synthetic data against attack datasets that simulates malicious commands injections with different levels of sophistication. Our findings show that synthetic data generated with some level of power grid domain knowledge helps train robust machine learning models against different types of attacks.
2022-12-01
Ajorpaz, Samira Mirbagher, Moghimi, Daniel, Collins, Jeffrey Neal, Pokam, Gilles, Abu-Ghazaleh, Nael, Tullsen, Dean.  2022.  EVAX: Towards a Practical, Pro-active & Adaptive Architecture for High Performance & Security. 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO). :1218—1236.
This paper provides an end-to-end solution to defend against known microarchitectural attacks such as speculative execution attacks, fault-injection attacks, covert and side channel attacks, and unknown or evasive versions of these attacks. Current defenses are attack specific and can have unacceptably high performance overhead. We propose an approach that reduces the overhead of state-of-art defenses by over 95%, by applying defenses only when attacks are detected. Many current proposed mitigations are not practical for deployment; for example, InvisiSpec has 27% overhead and Fencing has 74% overhead while protecting against only Spectre attacks. Other mitigations carry similar performance penalties. We reduce the overhead for InvisiSpec to 1.26% and for Fencing to 3.45% offering performance and security for not only spectre attacks but other known transient attacks as well, including the dangerous class of LVI and Rowhammer attacks, as well as covering a large set of future evasive and zero-day attacks. Critical to our approach is an accurate detector that is not fooled by evasive attacks and that can generalize to novel zero-day attacks. We use a novel Generative framework, Evasion Vaccination (EVAX) for training ML models and engineering new security-centric performance counters. EVAX significantly increases sensitivity to detect and classify attacks in time for mitigation to be deployed with low false positives (4 FPs in every 1M instructions in our experiments). Such performance enables efficient and timely mitigations, enabling the processor to automatically switch between performance and security as needed.
2023-01-20
Sen, Ömer, Eze, Chijioke, Ulbig, Andreas, Monti, Antonello.  2022.  On Holistic Multi-Step Cyberattack Detection via a Graph-based Correlation Approach. 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :380–386.
While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.
2023-08-16
Kara, Orhun.  2022.  How to Exploit Biham-Keller ID Characteristic to Minimize Data. 2022 15th International Conference on Information Security and Cryptography (ISCTURKEY). :44—48.
In this work, we examine the following question: How can we improve the best data complexity among the impossible differential (ID) attacks on AES? One of the most efficient attacks on AES are ID attacks. We have seen that the Biham-Keller ID characteristics are frequently used in these ID attacks. We observe the following fact: The probability that a given pair with a wrong key produce an ID characteristic is closely correlated to the data usage negatively. So, we maximize this probability by exploiting a Biham-Keller ID characteristic in a different manner than the other attacks. As a result, we mount an ID attack on 7-round AES-192 and obtain the best data requirement among all the ID attacks on 7-round AES. We make use of only 2$^\textrm58$ chosen plaintexts.
2023-05-11
Zhang, Zhi Jin, Bloch, Matthieu, Saeedifard, Maryam.  2022.  Load Redistribution Attacks in Multi-Terminal DC Grids. 2022 IEEE Energy Conversion Congress and Exposition (ECCE). :1–7.
The modernization of legacy power grids relies on the prevalence of information technology (IT). While the benefits are multi-fold and include increased reliability, more accurate monitoring, etc., the reliance on IT increases the attack surface of power grids by making them vulnerable to cyber-attacks. One of the modernization paths is the emergence of multi-terminal dc systems that offer numerous advantages over traditional ac systems. Therefore, cyber-security issues surrounding dc networks need to be investigated. Contributing to this effort, a class of false data injection attacks, called load redistribution (LR) attacks, that targets dc grids is proposed. These attacks aim to compromise the system load data and lead the system operator to dispatch incorrect power flow commands that lead to adverse consequences. Although similar attacks have been recently studied for ac systems, their feasibility in the converter-based dc grids has yet to be demonstrated. Such an attack assessment is necessary because the dc grids have a much smaller control timescale and are more dependent on IT than their traditional ac counterparts. Hence, this work formulates and evaluates dc grid LR attacks by incorporating voltage-sourced converter (VSC) control strategies that appropriately delineate dc system operations. The proposed attack strategy is solved with Gurobi, and the results show that both control and system conditions can affect the success of an LR attack.
ISSN: 2329-3748
2023-07-31
Liu, Lu, Song, Suwen, Wang, Zhongfeng.  2022.  A Novel Interleaving Scheme for Concatenated Codes on Burst-Error Channel. 2022 27th Asia Pacific Conference on Communications (APCC). :309—314.
With the rapid development of Ethernet, RS (544, 514) (KP4-forward error correction), which was widely used in high-speed Ethernet standards for its good performance-complexity trade-off, may not meet the demands of next-generation Ethernet for higher data transmission speed and better decoding performance. A concatenated code based on KP4-FEC has become a good solution because of its low complexity and excellent compatibility. For concatenated codes, aside from the selection of outer and inner codes, an efficient interleaving scheme is also very critical to deal with different channel conditions. Aiming at burst errors in wired communication, we propose a novel matrix interleaving scheme for concatenated codes which set the outer code as KP4-FEC and the inner code as Bose-Chaudhuri-Hocquenghem (BCH) code. In the proposed scheme, burst errors are evenly distributed to each BCH code as much as possible to improve their overall decoding efficiency. Meanwhile, the bit continuity in each symbol of the RS codeword is guaranteed during transmission, so the number of symbols affected by burst errors is minimized. Simulation results demonstrate that the proposed interleaving scheme can achieve a better decoding performance on burst-error channels than the original scheme. In some cases, the extra coding gain at the bit-error-rate (BER) of 1 × 10−15 can even reach 1 dB.
2023-03-31
Hirahara, Shuichi.  2022.  NP-Hardness of Learning Programs and Partial MCSP. 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS). :968–979.
A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT’90, SICOMP’91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness of learning programs. In this paper, we overcome Ko’s relativization barrier and prove NP-hardness of learning programs under randomized polynomial-time many-one reductions. Our result is provably non-relativizing, and comes somewhat close to the parameter range of improper learning: We observe that mildly improving our inapproximability factor is sufficient to exclude Heuristica, i.e., show the equivalence between average-case and worst-case complexities of N P. We also make progress on another long-standing open question of showing NP-hardness of the Minimum Circuit Size Problem (MCSP). We prove NP-hardness of the partial function variant of MCSP as well as other meta-computational problems, such as the problems MKTP* and MINKT* of computing the time-bounded Kolmogorov complexity of a given partial string, under randomized polynomial-time reductions. Our proofs are algorithmic information (a.k. a. Kolmogorov complexity) theoretic. We utilize black-box pseudorandom generator constructions, such as the Nisan-Wigderson generator, as a one-time encryption scheme secure against a program which “does not know” a random function. Our key technical contribution is to quantify the “knowledge” of a program by using conditional Kolmogorov complexity and show that no small program can know many random functions.