Miracle-dz/UniFAR-FTUs huggingface dataset:
RPAD: An Unsupervised HTTP Request Parameter Anomaly Detection Method

Web servers in the Internet are vulnerable to Web attacks. A general way to launch Web attacks is to carry attack payloads in HTTP request parameters, e.g. SQL Injection and XSS attacks. To detect Web attacks, a commonly used method is to detect anomalies in the request parameters by making regular-expression-based matching rules for the parameters based on known security threats. However, such methods cannot detect unknown anomalies well and they can also be easily bypassed by using techniques like transcoding. Moreover, existing anomaly detection methods are usually based on supervised learning methods that require a large number of high-quality labelled samples as training sets, which are difficult to obtain in real situations. In this paper, we propose an unsupervised HTTP Request Parameter Anomaly Detection method called RPAD. RPAD uses five features of HTTP request parameters to perform anomaly detection including type, length, number of tokens, encoding type and character feature. After extracting the five features, RPAD uses the DBSCAN algorithm to cluster the parameters of each target access request and outputs the outliers found in the clustering process as anomalies. We evaluate the performance of RPAD on several datasets from multiple real websites of a Cyber Security Company. The results indicate that RPAD is highly efficient in detecting deviating abnormal parameter values with an accuracy of 99%.
LWRpro: An Energy-∃ fﬁcient Conﬁgurable Crypto-Processor for Module-LWR

—Saber, the only module-learning with rounding-based algorithm in NIST’s third round of post-quantum cryptography (PQC) standardization process, is characterized by simplicity and ﬂexibility. However, energy-efﬁcient implementation of Saber is still under investigation since the commonly used number theoretic transform can not be utilized directly. In this manuscript, an energy-efﬁcient conﬁgurable crypto-processor supporting multi-security-level key encapsulation mechanism of Saber, is proposed. First, an 8-level hierarchical Karatsuba framework is utilized to reduce degree-256 polynomial multiplication to the coefﬁcient-wise multiplication. Second, a hardware-efﬁcient Karatsuba scheduling strategy and an optimized pre-/post-processing structure is designed to reduce the area over-heads of scheduling strategy. Third, a task-rescheduling-based pipeline strategy and truncated multipliers are proposed to enable ﬁne-grained processing. Moreover, multiple parameter sets are supported in LWRpro to enable conﬁgurability among various security scenarios. Enabled by these optimizations, LWRpro requires 1066, 1456 and 1701 clock cycles for key generation, encapsulation, and decapsulation of Saber768. The post-layout version of LWRpro is implemented with TSMC 40 nm CMOS process within 0.38 mm 2 . The throughput for Saber768 is up to 275k encapsulation operations per second and the energy efﬁciency is 0.15 uJ/encapsulation while operating at 400 MHz, achieving nearly 50 × improvement and 31 × improvement, respectively compared with current PQC hardware solutions.
Conditional Integration Active Disturbance Rejection Controller for Path Tracking of Autonomous Driving Vehicles

Aim at rejecting uncertainty disturbance and actuator saturation, a path tracking method is proposed for autonomous driving vehicles, which is implement by active disturbance rejection controller (ADRC) with conditional integration. Firstly, a kinematic-dynamic vehicle model is deduced for describing path tracking process. Secondly, a nonlinear extended state observer is designed to observe the uncertainty disturbance, such as external disturbance and parameter uncertainties. Finally, in order to eliminate error and reject disturbance while resisting actuator saturation, a conditional integration is developed as feedback control low. The test results of lane changing scenarios show that the proposed algorithm can track the desired path quickly and accurately compared with PID and ADRC.
Wiping 3D-objects using Deep Learning Model based on Image/Force/Joint Information

We propose a deep learning model for a robot to wipe 3D-objects. Wiping of 3D-objects requires recognizing the shapes of objects and planning the motor angle adjustments for tracing the objects. Unlike previous research, our learning model does not require pre-designed computational models of target objects. The robot is able to wipe the objects to be placed by using image, force, and arm joint information. We evaluate the generalization ability of the model by confirming that the robot handles untrained cube and bowl shaped-objects. We also find that it is necessary to use both image and force information to recognize the shape of and wipe 3D objects consistently by comparing changes in the input sensor data to the model. To our knowledge, this is the first work enabling a robot to use learning sensorimotor information alone to trace various unknown 3D-shape.
Using Diverse Neural Networks for Safer Human Pose Estimation: Towards Making Neural Networks Know When They Don’t Know

In recent years, human pose estimation has seen great improvements by the use of neural networks. However, these approaches are unsuitable for safety-critical applications such as human-robot interaction (HRI), as no guarantees are given whether a produced detection is correct or not and false detections with high confidence scores are produced on a regular basis.In this work, we propose a method to identify and eliminate false detections by comparing keypoint detections from different neural networks and assigning a ’Don’t know’ label in the case of a mismatch. Our approach is driven by the principle of software diversity, a technique recommended by the safety standard IEC 61508-7 [1] for dealing with software implementation faults. We evaluate our general concept on the MPII human pose dataset [2] using available ground truth data to calculate a suitable threshold for our keypoint comparison, reducing the number of false detections by approx. 61%. For the application at runtime, where no ground truth data is available, we introduce a method to calculate the needed threshold directly from keypoint detections. In further experiments, it was possible to reduce the number of false detections by approx. 75%. Eliminating keypoints by comparison also lowers the correct detection rate, which we maintained above 75% in all experiments. As this effect is limited and non-critical regarding safety we believe that the proposed approach can lead the way to a safe use of neural networks for human pose estimation in the future.
Impedance Control of Humanoid Walking on Uneven Terrain With Centroidal Momentum Dynamics Using Quadratic Programming

In this paper, we propose the stabilization strategy for a soft landing in a biped walking using impedance control and the optimization-based whole-body control framework. Even though proper contact forces and desired trajectories of the robot are given, the robot can be unstable easily if unexpected forces are applied to the robot or impulsive contact force is produced in the landing state while the robot is walking. Therefore, the impedance control approach using contact forces is performed to obtain the modified references that regulate the modified desired position, velocity and acceleration of the swing foot, and improves the walking stability. Moreover, we perform a whole-body control using quadratic programming (QP) that tracks the modified trajectories constrained with the centroidal momentum dynamics. To validate the algorithm, a walking task on uneven terrain using a humanoid robot is shown.
Mapping Thigh Motion to Knee Motion: Implications for Motion Planning of Active Prosthetic Knees

One of the main challenges of the active assistive devices is how to estimate the motion of the missing/impaired limbs and joints in line with the remaining limbs. To do so, a motion planner is required. This study proposes a motion planner that can be used for active prosthetic/orthotic knees. The aim is to continuously estimate the knee joint positions based on the thigh motion, using as few inputs as possible. Data from thigh-mounted IMU (thigh acceleration and angle) are used as inputs to estimate knee joint positions as outputs. It is aimed to continuously estimate the outputs as opposed to the state-machine approaches which divide the gait cycles into different sections and require switching rules. The performance of the motion planner is investigated for five walking speeds (0.6, 0.9, 1.2, 1.4 and 1.6 m/s). The strengths and limitations of the motion planer are investigated at different scenarios.
Collaborative Interaction Models for Optimized Human-Robot Teamwork

Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human’s actions, but also react quickly and intuitively when its predictions are wrong. The robot must plan its actions to account for the human’s own plan, with the knowledge that the human’s behavior will change based on what the robot actually does. This cyclical game of predicting a human’s future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques. In this work, we describe a novel Model Predictive Control (MPC)-based framework for finding optimal trajectories in a collaborative, multi-agent setting, in which we simultaneously plan for the robot while predicting the actions of its external collaborators. We use human-robot handovers to demonstrate that with a strong model of the collaborator, our framework produces fluid, reactive human-robot interactions in novel, cluttered environments. Our method efficiently generates coordinated trajectories, and achieves a high success rate in handover, even in the presence of significant sensor noise.
Deep Learning-Based Autonomous Scanning Electron Microscope

By virtue of their ultra high resolution, scanning electron microscopes (SEMs) are essential to study topography, morphology, composition, and crystallography of materials, and thus are widely used for advanced researches in physics, chemistry, pharmacy, geology, etc. The major hindrance of using SEMs is that obtaining high quality images from SEMs requires a professional control of many control parameters. Therefore, it is not an easy task even for an experienced researcher to get high quality sample images without any help from SEM experts. In this paper, we propose and implement a deep learning-based autonomous SEM machine, which assesses image quality and controls parameters autonomously to get high quality sample images just as if human experts do. This world’s first autonomous SEM machine may be the first step to bring SEMs, previously used only for advanced researches due to its difficulty in use, into much broader applications such as education, manufacture, and mechanical diagnosis, which are previously meant for optical microscopes.
Special-purpose Model Extraction Attacks: Stealing Coarse Model with Fewer Queries

Model extraction (ME) attacks have been shown to cause financial losses for Machine-Learning-as-a-Service (MLaaS) providers. Attackers steal ML models on MLaaS platforms by building substitute models using queries to and responses from MLaaS platforms. The ML models targeted by attackers are called targeted models. In previous studies, researchers have assumed that attackers build substitute models that classify the same number of classes as targeted ones, which classify thousands or millions of classes to meet users' diverse expectations. We call such models general-purpose models. In fact, attackers can monetize stolen models if they accurately distinguish some classes from others. We call such models special-purpose models. For instance, a model that detects vehicles is useful for collision avoidance systems, and a model that detects wild animals is useful to drive them away from agricultural land. In this work, we investigate a threat of special-purpose ME attacks that steal special-purpose models. Our experimental results show that attackers can build an accurate special-purpose model, which achieves an 80% f-measure, with as few as 100 queries in the worst case. We discuss the difficulty in preventing the attacks with previously proposed defense methods and point out the necessity of a new defense method.
A novel Q-Learning Algorithm Based on the Stochastic Environment Path Planning Problem

In this paper, we proposed a path planning algorithm based on Q-learning model to simulate an environment model, which is suitable for the complex environment. A virtual simulation platform has been built to complete the experiments. The experimental results show that the algorithm proposed in this paper can be effectively applied to the solution of vehicle routing problems in the complex environment.
Objective Functions of Principal Contact Estimation from Motion Based on the Geometrical Singular Condition

In this paper, we propose objective functions to estimate the principal contact between a unknown manipulated target object and its unknown surroundings from the motion of the object. We derived the objective functions based on the fact that contact involves a pair of geometrical primitives (a point of vertex, a line of edge, and a plane of face) for the singular condition of the calculation for their intersection or their spanned space from the point of view of geometrical algebra. The minimization of the proposed objective functions, which are differential quadratic forms of the Kronecker product of geometrical parameters, efficiently provided us the contact geometries that constrained the object movements. Additionally, the proposed objective functions are fundamentals for identifying contact during compliant manipulation, and we showed that the objective functions provide a clue for contact identification via experiments.
Far from classification algorithm: dive into the preprocessing stage in DGA detection

Domain-Flux technique has been widely used by attackers to maintain a botnet for many years and the core of it is the adoption of domain generation algorithm (DGA). To combat attackers, there are lots of works in DGA domain detection area recently. But they usually collect quite limited data and conduct experiments in a closed dataset, meaning that the DGA data and the benign data they collected can not well represent the real distribution between them. Moreover, they handle the domains roughly and use the origin data to train the classifier directly, which is also not adequate to classify these two types of domains with lots of false positives and false negatives happening during the real-world deployment. In this paper, we conduct the first large-scale DGA domain analysis in traffic level and argue that the preprocessing stage is also vital for the final classifier, which is usually ignored by the existing works. We collect the largest amount of DGA domain data than prior works and collect DNS log offered by a big company, whose DNS data covers most important industries in China. Based on this data, we analyze the distribution of DGA domains in traffic and give quantifiable results showing that NXDomain (domain not exist) is more suitable for DGA detection. Moreover, we give detailed preprocessing steps to handle the original domains. Our experiment shows that with the preprocessing stage mentioned above, classifier performs better in DGA detection task. Our research indicates that improving the classification algorithm is far from enough in DGA detection and the preprocessing stage is also the key component in bringing the DGA detection methods from lab to product.
Application of Permutation Entropy in the Stratification of Patients With Chagas Disease

Chagas disease is an endemic disease that in recent decades has ceased to be a rural disease to become mainly an urban disease, becoming a public health problem because it is a life-threatening disease and because 70 million people are at risk of infection. This disease, which has cardiac involvement in its chronic phase, can often be silent and asymptomatic, because of this the establishment of early markers in this type of patients is of great interest. To achieve this goal this study proposes the use of the permutation entropy (PE) which has been shown satisfactory results in the analysis of hearth rhythm and the differentiation between healthy people and people who suffered of congestive heart failure, also known as heart failure. This study analyzes three groups: 83 volunteers (Con-trol), 102 patients with positive serology and no cardiac involvement diagnosed by conventional non-invasive methods (CH1) and 107 patients with positive serology and mild to moderate incipient heart failure (CH2). The used data comes from the 24-hour ECG record, the RR intervals from each of the three groups are shown in 288 frames of 5 minutes. The analysis performed shows significant differences between the three groups, with 90% to 100% specificity and sensitivity, making this study a promising precedent for the development of a low-cost, non-invasive method for detecting possible heart failure in asymptomatic patients.
DP-Eclat:A Vertical Frequent Itemset Mining Algorithm Based on Differential Privacy

Frequent itemset mining has been a focused theme in the field of data mining, which is widely used in business decision making, economics, medicine, bioinformatics and other fields. Frequent itemset mining can provide a lot of valuable information when making decisions, but it may bring the risk of privacy disclosure when mining and publishing frequent itemsets. In order to solve the privacy leakage problem, most of the existing solutions are using horizontal mining method to mine frequent itemsets under differential privacy. However, these solutions generally suffer from complex support computation and poor accuracy due to large candidate sets. In this paper, we propose a new vertical frequent itemset mining algorithm based on differential privacy, which is referred to as DP-Eclat. In DP-Eclat, a new privacy budget allocation strategy is proposed to rationalize the privacy budget allocation, which allows privacy budget to be used more fully. In addition, we devise a multiple pruning strategy to further improve the data utility by prune before and after the generation of candidate itemsets. Through privacy analysis, we prove that DP-Eclat satisfies $E$ -differential privacy. Extensive experiment results on multiple real datasets show that DP-Eclat significantly outperforms state-of-the-art algorithms in terms of data utility.
Unified Calibration for Multi-camera Multi-LiDAR Systems using a Single Checkerboard

In this paper, we propose a unified calibration method for multi-camera multi-LiDAR systems. Only using a single planar checkerboard, the captured checkerboard frames by each sensor are classified as either global frames if they are observed by at least two sensors, or a local frame if observed by a single camera. Both global and local frames of each camera are used to estimate its intrinsic parameters, whereas the global frames between sensors are for computing their relative poses. In contrast to the previous methods that simply combine the pairwise poses (e.g., camera-to-camera or camera-to-LiDAR) that are separately estimated, we further optimize the sensor poses in the system globally using all observations as the constraints in the optimization problem. We find that the point-to-plane distances are effective as camera-to-LiDAR constraints where the points are 3D positions of the checkerboard corners and the planes are estimated from the LiDAR point-cloud. Also, abundant corner observations in the local frames enable the joint optimization of intrinsic and extrinsic parameters in a unified framework. The proposed calibration method utilizes entire observations in a unified global optimization framework, and it significantly reduces the error caused by a simple composition of the relative sensor poses. We extensively evaluate the proposed algorithm qualitatively and quantitatively using real and synthetic datasets. We plan to make the implementation open to the public with the paper publication.
Family Identification of AGE-Generated Android Malware Using Tree-Based Feature

Application Generation Engine(AGE) is a development tool that can automatically generate simple Android applications by utilizing some boilerplate codes. People with little software programming background could also develop Android applications by using this tool based on their requirements. The emergence of AGE dramatically improves the ease of developing essential software and lowers the level of programming skills required for app developers. However, it also provides easy access for attackers to quickly develop a large number of malicious applications, which will seriously affect the device and data security of regular users. Since AGE mainly generates applications based on some boilerplate codes, the code structures of malicious apps created by AGE have a high degree of similarity when these apps belong to the same family. Based on the assumption that the package directory structures of the software from the same family are also similar, we designed a novel feature construction method to describe the application. Using this method, we extracted features from the leaf nodes of the smali tree, while each smali tree corresponds to the smali directory of the application. Unlike traditional static feature extraction of applications, the tree-based feature proposed in this paper can effectively counteract problems such as code obfuscation or reflection cause it can adequately reflect the semantic features of the smali files. To prove the effectiveness of tree-based features, we also conducted some experiments based on a dataset provided by the enterprise. This dataset contains 1792 AGE-generated applications, and these applications belong to 17 malicious families. We demonstrated that the feature construction method proposed in this paper is usable and can be applied to machine learning classification algorithms for the identification of malicious applications.
Power Allocation for Downlink Training in Cell-Free Massive MIMO Networks

In this paper, the problem of power allocation in downlink (DL) of cell-free (CF) massive multiple -input multiple -output (MaMIMO) systems is studied. The system consists of a large number of single -antenna access points (APs) that jointly serve a large number of users. In is assumed that the system operates in time division duplex (TDD) mode and downlink pilots are transmitted using beamforming training (BT) method. In this network, we propose a power control scheme that optimally allocates power to data and pilot symbols, considering a total energy budget for each AP. The objective of the power optimization problem is maximizing the minimum achievable rate among the users and the constraint is the total power constraint in each coherence interval. Numerical simulations show that the achievable rate corresponding to each user, employing the proposed power allocation considerably improved compared to the case that of provided with equal power between pilot and data symbols.