Visible to the public Biblio

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2021-10-12
Zhong, Zhenyu, Hu, Zhisheng, Chen, Xiaowei.  2020.  Quantifying DNN Model Robustness to the Real-World Threats. 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :150–157.
DNN models have suffered from adversarial example attacks, which lead to inconsistent prediction results. As opposed to the gradient-based attack, which assumes white-box access to the model by the attacker, we focus on more realistic input perturbations from the real-world and their actual impact on the model robustness without any presence of the attackers. In this work, we promote a standardized framework to quantify the robustness against real-world threats. It is composed of a set of safety properties associated with common violations, a group of metrics to measure the minimal perturbation that causes the offense, and various criteria that reflect different aspects of the model robustness. By revealing comparison results through this framework among 13 pre-trained ImageNet classifiers, three state-of-the-art object detectors, and three cloud-based content moderators, we deliver the status quo of the real-world model robustness. Beyond that, we provide robustness benchmarking datasets for the community.
2021-09-30
Bhowmick, Chandreyee, Jagannathan, S..  2020.  Availability-Resilient Control of Uncertain Linear Stochastic Networked Control Systems. 2020 American Control Conference (ACC). :4016–4021.
The resilient output feedback control of linear networked control (NCS) system with uncertain dynamics in the presence of Gaussian noise is presented under the denial of service (DoS) attacks on communication networks. The DoS attacks on the sensor-to-controller (S-C) and controller- to-actuator (C-A) networks induce random packet losses. The NCS is viewed as a jump linear system, where the linear NCS matrices are a function of induced losses that are considered unknown. A set of novel correlation detectors is introduced to detect packet drops in the network channels using the property of Gaussian noise. By using an augmented system representation, the output feedback Q-learning based control scheme is designed for the jump linear NCS with uncertain dynamics to cope with the changing values of the mean packet losses. Simulation results are included to support the theoretical claims.
2021-09-21
Li, Mingxuan, Lv, Shichao, Shi, Zhiqiang.  2020.  Malware Detection for Industrial Internet Based on GAN. 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA). 1:475–481.
This thesis focuses on the detection of malware in industrial Internet. The basic flow of the detection of malware contains feature extraction and sample identification. API graph can effectively represent the behavior information of malware. However, due to the high algorithm complexity of solving the problem of subgraph isomorphism, the efficiency of analysis based on graph structure feature is low. Due to the different scales of API graph of different malicious codes, the API graph needs to be normalized. Considering the difficulties of sample collection and manual marking, it is necessary to expand the number of malware samples in industrial Internet. This paper proposes a method that combines PageRank with TF-IDF to process the API graph. Besides, this paper proposes a method to construct the adversarial samples of malwares based on GAN.
2021-09-07
Huang, Weiqing, Peng, Xiao, Shi, Zhixin, Ma, Yuru.  2020.  Adversarial Attack against LSTM-Based DDoS Intrusion Detection System. 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). :686–693.
Nowadays, machine learning is a popular method for DDoS detection. However, machine learning algorithms are very vulnerable under the attacks of adversarial samples. Up to now, multiple methods of generating adversarial samples have been proposed. However, they cannot be applied to LSTM-based DDoS detection directly because of the discrete property and the utility requirement of its input samples. In this paper, we propose two methods to generate DDoS adversarial samples, named Genetic Attack (GA) and Probability Weighted Packet Saliency Attack (PWPSA) respectively. Both methods modify original input sample by inserting or replacing partial packets. In GA, we evolve a set of modified samples with genetic algorithm and find the evasive variant from it. In PWPSA, we modify original sample iteratively and use the position saliency as well as the packet score to determine insertion or replacement order at each step. Experimental results on CICIDS2017 dataset show that both methods can bypass DDoS detectors with high success rate.
2021-08-31
Adamov, Alexander, Carlsson, Anders.  2020.  Reinforcement Learning for Anti-Ransomware Testing. 2020 IEEE East-West Design Test Symposium (EWDTS). :1–5.
In this paper, we are going to verify the possibility to create a ransomware simulation that will use an arbitrary combination of known tactics and techniques to bypass an anti-malware defense. To verify this hypothesis, we conducted an experiment in which an agent was trained with the help of reinforcement learning to run the ransomware simulator in a way that can bypass anti-ransomware solution and encrypt the target files. The novelty of the proposed method lies in applying reinforcement learning to anti-ransomware testing that may help to identify weaknesses in the anti-ransomware defense and fix them before a real attack happens.
2021-06-30
Liu, Ming, Chen, Shichao, Lu, Fugang, Xing, Mengdao, Wei, Jingbiao.  2020.  A Target Detection Method in SAR Images Based on Superpixel Segmentation. 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT). :528—530.
A synthetic aperture radar (SAR) target detection method based on the fusion of multiscale superpixel segmentations is proposed in this paper. SAR images are segmented between land and sea firstly by using superpixel technology in different scales. Secondly, image segmentation results together with the constant false alarm rate (CFAR) detection result are coalesced. Finally, target detection is realized by fusing different scale results. The effectiveness of the proposed algorithm is tested on Sentinel-1A data.
Wang, Chenguang, Tindemans, Simon, Pan, Kaikai, Palensky, Peter.  2020.  Detection of False Data Injection Attacks Using the Autoencoder Approach. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). :1—6.
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
Ding, Xinyao, Wang, Yan.  2020.  False Data Injection Attack Detection Before Decoding in DF Cooperative Relay Network. 2020 Asia Conference on Computers and Communications (ACCC). :57—61.
False data injection (FDI) attacks could happen in decode-and-forward (DF) wireless cooperative relay networks. Although physical integrity check (PIC) can combat that by applying physical layer detection, the detector depends on the decoding results and low signal-to-noise ratio (SNR) further deteriorates the detecting results. In this paper, a physical layer detect-before-decode (DbD) method is proposed, which has low computational complexity with no sacrifice of false alarm and miss detection rates. One significant advantage of this method is the detector does not depend on the decoding results. In order to implement the proposed DbD method, a unified error sufficient statistic (UESS) containing the full information of FDI attacks is constructed. The proposed UESS simplifies the detector because it is applicable to all link conditions, which means there is no need to deal each link condition with a specialized sufficient statistic. Moreover, the source to destination outage probability (S2Dop) of the DF cooperative relay network utilizing the proposed DbD method is studied. Finally, numerical simulations verify the good performance of this DbD method.
2021-06-02
Zegers, Federico M., Hale, Matthew T., Shea, John M., Dixon, Warren E..  2020.  Reputation-Based Event-Triggered Formation Control and Leader Tracking with Resilience to Byzantine Adversaries. 2020 American Control Conference (ACC). :761—766.
A distributed event-triggered controller is developed for formation control and leader tracking (FCLT) with robustness to adversarial Byzantine agents for a class of heterogeneous multi-agent systems (MASs). A reputation-based strategy is developed for each agent to detect Byzantine agent behaviors within their neighbor set and then selectively disregard Byzantine state information. Selectively ignoring Byzantine agents results in time-varying discontinuous changes to the network topology. Nonsmooth dynamics also result from the use of the event-triggered strategy enabling intermittent communication. Nonsmooth Lyapunov methods are used to prove stability and FCLT of the MAS consisting of the remaining cooperative agents.
2021-06-01
Materzynska, Joanna, Xiao, Tete, Herzig, Roei, Xu, Huijuan, Wang, Xiaolong, Darrell, Trevor.  2020.  Something-Else: Compositional Action Recognition With Spatial-Temporal Interaction Networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :1046–1056.
Human action is naturally compositional: humans can easily recognize and perform actions with objects that are different from those used in training demonstrations. In this paper, we study the compositionality of action by looking into the dynamics of subject-object interactions. We propose a novel model which can explicitly reason about the geometric relations between constituent objects and an agent performing an action. To train our model, we collect dense object box annotations on the Something-Something dataset. We propose a novel compositional action recognition task where the training combinations of verbs and nouns do not overlap with the test set. The novel aspects of our model are applicable to activities with prominent object interaction dynamics and to objects which can be tracked using state-of-the-art approaches; for activities without clearly defined spatial object-agent interactions, we rely on baseline scene-level spatio-temporal representations. We show the effectiveness of our approach not only on the proposed compositional action recognition task but also in a few-shot compositional setting which requires the model to generalize across both object appearance and action category.
2021-04-08
Boato, G., Dang-Nguyen, D., Natale, F. G. B. De.  2020.  Morphological Filter Detector for Image Forensics Applications. IEEE Access. 8:13549—13560.
Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
2021-03-29
Xu, Z., Easwaran, A..  2020.  A Game-Theoretic Approach to Secure Estimation and Control for Cyber-Physical Systems with a Digital Twin. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :20–29.
Cyber-Physical Systems (CPSs) play an increasingly significant role in many critical applications. These valuable applications attract various sophisticated attacks. This paper considers a stealthy estimation attack, which aims to modify the state estimation of the CPSs. The intelligent attackers can learn defense strategies and use clandestine attack strategies to avoid detection. To address the issue, we design a Chi-square detector in a Digital Twin (DT), which is an online digital model of the physical system. We use a Signaling Game with Evidence (SGE) to find the optimal attack and defense strategies. Our analytical results show that the proposed defense strategies can mitigate the impact of the attack on the physical estimation and guarantee the stability of the CPSs. Finally, we use an illustrative application to evaluate the performance of the proposed framework.
2021-03-15
Salama, G. M., Taha, S. A..  2020.  Cooperative Spectrum Sensing and Hard Decision Rules for Cognitive Radio Network. 2020 3rd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
Cognitive radio is development of wireless communication and mobile computing. Spectrum is a limited source. The licensed spectrum is proposed to be used only by the spectrum owners. Cognitive radio is a new view of the recycle licensed spectrum in an unlicensed manner. The main condition of the cognitive radio network is sensing the spectrum hole. Cognitive radio can be detect unused spectrum. It shares this with no interference to the licensed spectrum. It can be a sense signals. It makes viable communication in the middle of multiple users through co-operation in a self-organized manner. The energy detector method is unseen signal detector because it reject the data of the signal.In this paper, has implemented Simulink Energy Detection of spectrum sensing cognitive radio in a MATLAB Simulink to Exploit spectrum holes and avoid damaging interference to licensed spectrum and unlicensed spectrum. The hidden primary user problem will happened because fading or shadowing. Ithappens when cognitive radio could not be detected by primer users because of its location. Cooperative sensing spectrum sensing is the best-proposed method to solve the hidden problem.
Shekhawat, G. K., Yadav, R. P..  2020.  Sparse Code Multiple Access based Cooperative Spectrum Sensing in 5G Cognitive Radio Networks. 2020 5th International Conference on Computing, Communication and Security (ICCCS). :1–6.
Fifth-generation (5G) network demands of higher data rate, massive user connectivity and large spectrum can be achieve using Sparse Code Multiple Access (SCMA) scheme. The integration of cognitive feature spectrum sensing with SCMA can enhance the spectrum efficiency in a heavily dense 5G wireless network. In this paper, we have investigated the primary user detection performance using SCMA in Centralized Cooperative Spectrum Sensing (CCSS). The developed model can support massive user connectivity, lower latency and higher spectrum utilization for future 5G networks. The simulation study is performed for AWGN and Rayleigh fading channel. Log-MPA iterative receiver based Log-Likelihood Ratio (LLR) soft test statistic is passed to Fusion Center (FC). The Wald-hypothesis test is used at FC to finalize the PU decision.
2021-03-09
Guibene, K., Ayaida, M., Khoukhi, L., MESSAI, N..  2020.  Black-box System Identification of CPS Protected by a Watermark-based Detector. 2020 IEEE 45th Conference on Local Computer Networks (LCN). :341–344.

The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.

2021-03-04
Wang, H., Sayadi, H., Kolhe, G., Sasan, A., Rafatirad, S., Homayoun, H..  2020.  Phased-Guard: Multi-Phase Machine Learning Framework for Detection and Identification of Zero-Day Microarchitectural Side-Channel Attacks. 2020 IEEE 38th International Conference on Computer Design (ICCD). :648—655.

Microarchitectural Side-Channel Attacks (SCAs) have emerged recently to compromise the security of computer systems by exploiting the existing processors' hardware vulnerabilities. In order to detect such attacks, prior studies have proposed the deployment of low-level features captured from built-in Hardware Performance Counter (HPC) registers in modern microprocessors to implement accurate Machine Learning (ML)-based SCAs detectors. Though effective, such attack detection techniques have mainly focused on binary classification models offering limited insights on identifying the type of attacks. In addition, while existing SCAs detectors required prior knowledge of attacks applications to detect the pattern of side-channel attacks using a variety of microarchitectural features, detecting unknown (zero-day) SCAs at run-time using the available HPCs remains a major challenge. In response, in this work we first identify the most important HPC features for SCA detection using an effective feature reduction method. Next, we propose Phased-Guard, a two-level machine learning-based framework to accurately detect and classify both known and unknown attacks at run-time using the most prominent low-level features. In the first level (SCA Detection), Phased-Guard using a binary classification model detects the existence of SCAs on the target system by determining the critical scenarios including system under attack and system under no attack. In the second level (SCA Identification) to further enhance the security against side-channel attacks, Phased-Guard deploys a multiclass classification model to identify the type of SCA applications. The experimental results indicate that Phased-Guard by monitoring only the victim applications' microarchitectural HPCs data, achieves up to 98 % attack detection accuracy and 99.5% SCA identification accuracy significantly outperforming the state-of-the-art solutions by up to 82 % in zero-day attack detection at the cost of only 4% performance overhead for monitoring.

2021-02-08
Chiang, M., Lau, S..  2011.  Automatic multiple faces tracking and detection using improved edge detector algorithm. 2011 7th International Conference on Information Technology in Asia. :1—5.

The automatic face tracking and detection has been one of the fastest developing areas due to its wide range of application, security and surveillance application in particular. It has been one of the most interest subjects, which suppose but yet to be wholly explored in various research areas due to various distinctive factors: varying ethnic groups, sizes, orientations, poses, occlusions and lighting conditions. The focus of this paper is to propose an improve algorithm to speed up the face tracking and detection process with the simple and efficient proposed novel edge detector to reject the non-face-likes regions, hence reduce the false detection rate in an automatic face tracking and detection in still images with multiple faces for facial expression system. The correct rates of 95.9% on the Haar face detection and proposed novel edge detector, which is higher 6.1% than the primitive integration of Haar and canny edge detector.

Qiao, B., Jin, L., Yang, Y..  2016.  An Adaptive Algorithm for Grey Image Edge Detection Based on Grey Correlation Analysis. 2016 12th International Conference on Computational Intelligence and Security (CIS). :470—474.

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

2021-01-25
Zhang, Z., Zhang, Q., Liu, T., Pang, Z., Cui, B., Jin, S., Liu, K..  2020.  Data-driven Stealthy Actuator Attack against Cyber-Physical Systems. 2020 39th Chinese Control Conference (CCC). :4395–4399.
This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.
2021-01-15
Kharbat, F. F., Elamsy, T., Mahmoud, A., Abdullah, R..  2019.  Image Feature Detectors for Deepfake Video Detection. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). :1—4.
Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.
Gandhi, A., Jain, S..  2020.  Adversarial Perturbations Fool Deepfake Detectors. 2020 International Joint Conference on Neural Networks (IJCNN). :1—8.
This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.
2020-11-20
Sui, T., Marelli, D., Sun, X., Fu, M..  2019.  Stealthiness of Attacks and Vulnerability of Stochastic Linear Systems. 2019 12th Asian Control Conference (ASCC). :734—739.
The security of Cyber-physical systems has been a hot topic in recent years. There are two main focuses in this area: Firstly, what kind of attacks can avoid detection, i.e., the stealthiness of attacks. Secondly, what kind of systems can stay stable under stealthy attacks, i.e., the invulnerability of systems. In this paper, we will give a detailed characterization for stealthy attacks and detection criterion for such attacks. We will also study conditions for the vulnerability of a stochastic linear system under stealthy attacks.
2020-11-17
Qian, K., Parizi, R. M., Lo, D..  2018.  OWASP Risk Analysis Driven Security Requirements Specification for Secure Android Mobile Software Development. 2018 IEEE Conference on Dependable and Secure Computing (DSC). :1—2.
The security threats to mobile applications are growing explosively. Mobile apps flaws and security defects open doors for hackers to break in and access sensitive information. Defensive requirements analysis should be an integral part of secure mobile SDLC. Developers need to consider the information confidentiality and data integrity, to verify the security early in the development lifecycle rather than fixing the security holes after attacking and data leaks take place. Early eliminating known security vulnerabilities will help developers increase the security of apps and reduce the likelihood of exploitation. However, many software developers lack the necessary security knowledge and skills at the development stage, and that's why Secure Mobile Software Development education is very necessary for mobile software engineers. In this paper, we propose a guided security requirement analysis based on OWASP Mobile Top ten security risk recommendations for Android mobile software development and its traceability of the developmental controls in SDLC. Building secure apps immune to the OWASP Mobile Top ten risks would be an effective approach to provide very useful mobile security guidelines.
2020-11-04
Apruzzese, G., Colajanni, M., Ferretti, L., Marchetti, M..  2019.  Addressing Adversarial Attacks Against Security Systems Based on Machine Learning. 2019 11th International Conference on Cyber Conflict (CyCon). 900:1—18.

Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.

2020-10-19
King, Pietro, Torrisi, Giuseppe, Gugiatti, Matteo, Carminati, Marco, Mertens, Susanne, Fiorini, Carlo.  2019.  Kerberos: a 48-Channel Analog Processing Platform for Scalable Readout of Large SDD Arrays. 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). :1–3.
The readout of large pixellated detectors with good spectroscopic quality represents a challenge for both front-end and back-end electronics. The TRISTAN project for the search of the Sterile neutrino in the keV-scale, envisions the operation of 21 detection modules equipped with a monolithic array of 166 SDDs each, for β-decay spectroscopy in the KATRIN experiment's spectrometer. Since the trace of the sterile neutrino existence would manifest as a kink of \textbackslashtextless; 1ppm in the continuous spectrum, high accuracy in the acquisition is required. Within this framework, we present the design of a multichannel scalable analog processing and DAQ system named Kerberos, aimed to provide a simple and low-cost multichannel readout option in the early phase of the TRISTAN detector development. It is based on three 16-channel integrated programmable analog pulse processors (SFERA ASICs), high linearity ADCs, and an FPGA. The platform is able to acquire data from up to 48 pixels in parallel, providing also different readout and multiplexing strategies. The use of an analog ASIC-based solution instead of a Digital Pulse Processor, represents a viable and scalable processing solution at the price of slightly limited versatility and count rate.