Biblio

Recent studies show that the state-of-the-art deep neural networks are vulnerable to model inversion attacks, in which access to a model is abused to reconstruct private training data of any given target class. Existing attacks rely on having access to either the complete target model (whitebox) or the model's soft-labels (blackbox). However, no prior work has been done in the harder but more practical scenario, in which the attacker only has access to the model's predicted label, without a confidence measure. In this paper, we introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert private training data using only the target model's predicted labels. The key idea of our algorithm is to evaluate the model's predicted labels over a sphere and then estimate the direction to reach the target class's centroid. Using the example of face recognition, we show that the images reconstructed by BREP-MI successfully reproduce the semantics of the private training data for various datasets and target model architectures. We compare BREP-MI with the state-of-the-art white-box and blackbox model inversion attacks, and the results show that despite assuming less knowledge about the target model, BREP-MI outperforms the blackbox attack and achieves comparable results to the whitebox attack. Our code is available online.11https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion

To detect human behaviour and measure accuracy of classification rate. Materials and Methods: A novel deep belief network with sample size 10 and support vector machine with sample size of 10. It was iterated at different times predicting the accuracy percentage of human behaviour. Results: Human behaviour detection utilizing novel deep belief network 87.9% accuracy compared with support vector machine 87.0% accuracy. Deep belief networks seem to perform essentially better compared to support vector machines \$(\textbackslashmathrmp=0.55)(\textbackslashtextPiˆ0.05)\$. The deep belief algorithm in computer vision appears to perform significantly better than the support vector machine algorithm. Conclusion: Within this human behaviour detection novel deep belief network has more precision than support vector machine.

Historically, energy resources are of strategic importance for the social welfare and economic growth. So, predicting crude oil price fluctuations is an important issue. Since crude oil price changes are affected by many risk factors in markets, this price shows more complicated nonlinear behavior and creates more risk levels for investors than in the past. We propose a new method of prediction of crude oil price to model nonlinear dynamics. The results of the experiments show that the superior performance of the model based on the proposed method against statistical previous works is statistically significant. In general, we found that the combination of the IDBN or LSTM model lowered the MSE value to 4.65, which is 0.81 lower than the related work (Chen et al. protocol), indicating an improvement in prediction accuracy.

Higher education management has problems producing 100% of graduates capable of responding to the needs of industry while industry also is struggling to find qualified graduates that responded to their needs in part because of the inefficient way of evaluating problems, as well as because of weaknesses in the evaluation of problem-solving capabilities. The objective of this paper is to propose an appropriate classification model to be used for predicting and evaluating the attributes of the data set of the student in order to meet the selection criteria required by the industries in the academic field. The dataset required for this analysis was obtained from a private firm and the execution was carried out using Chimp Optimization Algorithm (COA) based Deep Belief Neural Network (COA-DBNN) and the obtained results are compared with various classifiers such as Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The proposed model outperforms other classifiers in terms of various performance metrics. This critical analysis will help the college management to make a better long-term plan for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

Terrorism, and radicalization are major economic, political, and social issues faced by the world in today's era. The challenges that governments and citizens face in combating terrorism are growing by the day. Artificial intelligence, including machine learning and deep learning, has shown promising results in predicting terrorist attacks. In this paper, we attempted to build a machine learning model to predict terror activities using a global terrorism database in both relational and graphical forms. Using the Neo4j Sandbox, you can create a graph database from a relational database. We used the node2vec algorithm from Neo4j Sandbox's graph data science library to convert the high-dimensional graph to a low-dimensional vector form. In order to predict terror activities, seven machine learning models were used, and the performance parameters that were calculated were accuracy, precision, recall, and F1 score. According to our findings, the Logistic Regression model was the best performing model which was able to classify the dataset with an accuracy of 0.90, recall of 0.94 precision of 0.93, and an F1 score of 0.93.

A recommender system is a filtering application based on personalized information from acquired big data to predict a user's preference. Traditional recommender systems primarily rely on keywords or scene patterns. Users' subjective emotion data are rarely utilized for preference prediction. Novel Brain Computer Interfaces hold incredible promise and potential for intelligent applications that rely on collected user data like a recommender system. This paper describes a deep learning method that uses Brain Computer Interfaces (BCI) based neural measures to predict a user's preference on short music videos. Our models are employed on both population-wide and individualized preference predictions. The recognition method is based on dynamic histogram measurement and deep neural network for distinctive feature extraction and improved classification. Our models achieve 97.21%, 94.72%, 94.86%, and 96.34% classification accuracy on two-class, three-class, four-class, and nine-class individualized predictions. The findings provide evidence that a personalized recommender system on an implicit BCI has the potential to succeed.

This paper presents the machine learning algorithm to detect whether an executable binary is benign or ransomware. The ransomware cybercriminals have targeted our infrastructure, businesses, and everywhere which has directly affected our national security and daily life. Tackling the ransomware threats more effectively is a big challenge. We applied a machine-learning model to classify and identify the security level for a given suspected malware for ransomware detection and prevention. We use the feature selection data preprocessing to improve the prediction accuracy of the model.

Global cybersecurity threats have grown as a result of the evolving digital transformation. Cybercriminals have more opportunities as a result of digitization. Initially, cyberthreats take the form of phishing in order to gain confidential user credentials.As cyber-attacks get more sophisticated and sophisticated, the cybersecurity industry is faced with the problem of utilising cutting-edge technology and techniques to combat the ever-present hostile threats. Hackers use phishing to persuade customers to grant them access to a company’s digital assets and networks. As technology progressed, phishing attempts became more sophisticated, necessitating the development of tools to detect phishing.Machine learning is unsupervised one of the most powerful weapons in the fight against terrorist threats. The features used for phishing detection, as well as the approaches employed with machine learning, are discussed in this study.In this light, the study’s major goal is to propose a unique, robust ensemble machine learning model architecture that gives the highest prediction accuracy with the lowest error rate, while also recommending a few alternative robust machine learning models.Finally, the Random forest algorithm attained a maximum accuracy of 96.454 percent. But by implementing a hybrid model including the 3 classifiers- Decision Trees,Random forest, Gradient boosting classifiers, the accuracy increases to 98.4 percent.

Forming a secure autonomous vehicle group is extremely challenging since we have to consider threats and vulnerability of autonomous vehicles. Existing studies focus on communications among risk-free autonomous vehicles, which lack metrics to measure passenger security and cargo values. This work proposes a novel autonomous vehicle group formation method. We introduce risk assessment scoring to assess passenger security and cargo values, and propose an autonomous vehicle group formation method based on it. Our vehicle group is composed of a master node, and a number of core and border ones. Finally, the extensive simulation results show that our method is better than a Connectivity Prediction-based Dynamic Clustering model and a Low-InDependently clustering architecture in terms of node survival time, average change count of master nodes, and average risk assessment scoring.

Smart grid is a new generation of grid that inte-grates traditional grid and grid information system, and infor-mation security of smart grid is an extremely important part of the whole grid. The research of trusted computing technology provides new ideas to protect the information security of the power grid. To address the problem of large deviations in the calculation of credible dynamic thresholds due to the existence of characteristics such as self-similarity and traffic bursts in smart grid information collection, a traffic prediction model based on ARMA and Poisson process is proposed. And the Hurst coefficient is determined more accurately using R/S analysis, which finally improves the efficiency and accuracy of the trusted dynamic threshold calculation.

With the electric power distribution grid facing ever increasing complexity and new threats from cyber-attacks, situational awareness for system operators is quickly becoming indispensable. Identifying de-energized lines on the distribution system during a SCADA communication failure is a prime example where operators need to act quickly to deal with an emergent loss of service. Loss of cellular towers, poor signal strength, and even cyber-attacks can impact SCADA visibility of line devices on the distribution system. Neural Networks (NNs) provide a unique approach to learn the characteristics of normal system behavior, identify when abnormal conditions occur, and flag these conditions for system operators. This study applies a 24-hour load forecast for distribution line devices given the weather forecast and day of the week, then determines the current state of distribution devices based on changes in SCADA analogs from communicating line devices. A neural network-based algorithm is applied to historical events on Alabama Power's distribution system to identify de-energized sections of line when a significant amount of SCADA information is hidden.

When applied to short-term energy consumption forecasting, the federated learning framework allows for the creation of a predictive model without sharing raw data. There is a limit to the accuracy achieved by standard federated learning due to the heterogeneity of the individual clients' data, especially in the case of electricity data, where prediction of peak demand is a challenge. A set of clustering techniques has been explored in the literature to improve prediction quality while maintaining user privacy. These studies have mainly been conducted using sets of clients with similar attributes that may not reflect real-world consumer diversity. This paper explores, implements and compares these clustering techniques for privacy-preserving load forecasting on a representative electricity consumption dataset. The experimental results demonstrate the effects of electricity consumption heterogeneity on federated forecasting and a non-representative sample's impact on load forecasting.

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

The article deals with the issues of improving the quality of corporate information systems functioning and ensuring the information security of financial organizations that have a complex structure and serve a significant number of customers. The formation of the company's informational system and its integrated information security system is studied based on the process approach, methods of risk management and quality management. The risks and threats to the security of the informational system functioning and the quality of information support for customer service of a financial organization are analyzed. The methods and tools for improving the quality of information services and ensuring information security are considered on the example of an organization for social insurance. Recommendations are being developed to improve the quality of the informational system functioning in a large financial company.

In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e.g., X-Ray, CT, and MRI) and analysis tasks (e.g., classification, detection, and segmentation). Most existing BA methods are designed to attack natural image classification models, which apply spatial triggers to training images and inevitably corrupt the semantics of poisoned pixels, leading to the failures of attacking dense prediction models. To address this issue, we propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images. Since it preserves the semantics of the poisoned image pixels, FIBA can perform attacks on both classification and dense prediction models. Experiments on three benchmarks in MIA (i.e., ISIC-2019 [4] for skin lesion classification, KiTS-19 [17] for kidney tumor segmentation, and EAD-2019 [1] for endoscopic artifact detection), validate the effectiveness of FIBA and its superiority over stateof-the-art methods in attacking MIA models and bypassing backdoor defense. Source code will be available at code.

Being a part of today’s technical world, we are connected through a vast network. More we are addicted to these modernization techniques we need security. There must be reliability in a network security system so that it is capable of doing perfect monitoring of the whole network of an organization so that any unauthorized users or intruders wouldn’t be able to halt our security breaches. Firewalls are there for securing our internal network from unauthorized outsiders but still some time possibility of attacks is there as according to a survey 60% of attacks were internal to the network. So, the internal system needs the same higher level of security just like external. So, understanding the value of security measures with accuracy, efficiency, and speed we got to focus on implementing and comparing an improved intrusion detection system. A comprehensive literature review has been done and found that some feature selection techniques with standard scaling combined with Machine Learning Techniques can give better results over normal existing ML Techniques. In this survey paper with the help of the Uni-variate Feature selection method, the selection of 14 essential features out of 41 is performed which are used in comparative analysis. We implemented and compared both binary class classification and multi-class classification-based Intrusion Detection Systems (IDS) for two Supervised Machine Learning Techniques Support Vector Machine and Classification and Regression Techniques.

Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.

Machine Learning (ML) models are now commonly used as components in systems. As any other component, ML components can produce erroneous outputs that may penalize system utility. In this context, self-adaptive systems emerge as a natural approach to cope with ML mispredictions, through the execution of adaptation tactics such as model retraining. To synthesize an adaptation strategy, the self-adaptation manager needs to reason about the cost-benefit tradeoffs of the applicable tactics, which is a non-trivial task for tactics such as model retraining, whose benefits are both context- and data-dependent.To address this challenge, this paper proposes a probabilistic modeling framework that supports automated reasoning about the cost/benefit tradeoffs associated with improving ML components of ML-based systems. The key idea of the proposed approach is to decouple the problems of (i) estimating the expected performance improvement after retrain and (ii) estimating the impact of ML improved predictions on overall system utility.We demonstrate the application of the proposed framework by using it to self-adapt a state-of-the-art ML-based fraud-detection system, which we evaluate using a publicly-available, real fraud detection dataset. We show that by predicting system utility stemming from retraining a ML component, the probabilistic model checker can generate adaptation strategies that are significantly closer to the optimal, as compared against baselines such as periodic retraining, or reactive retraining.

Cyber Physical Systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical Systems are a crucial component of Internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the Internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective Network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection (NSRM-AD) using deep learning model is proposed. The security levels of the proposed model are contrasted with the proposed model and the results represent that the proposed model performance is accurate

The aim of this paper is to describe the battery lifetime estimation and energy consumption model of the sensor nodes in TDMA wireless mesh sensor using merged data collecting (MDC) methods based on lithium thionyl chloride batteries. Defining the energy consumption of the nodes in wireless mesh networks is crucial for battery lifetime estimation. In this paper, we describe the timing, energy consumption, and battery lifetime estimation of the MDC method in the TDMA mesh sensor networks using flooding routing. For the battery life estimation, we made a semiempirical model that describes the energy consumption of the nodes with a real battery model. In this model, the low-level constraints are based on the measured energy consumption of the sensor nodes in different operation phases.

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explain aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI [1]. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one; it can also generate multiple diverse explanations in step two.

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

Electrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model’s behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.

The number of publications related to Explainable Artificial Intelligence (XAI) has increased rapidly this last decade. However, the subjective nature of explainability has led to a lack of consensus regarding commonly used definitions for explainability and with differing problem statements falling under the XAI label resulting in a lack of comparisons. This paper proposes in broad terms a simple comparison framework for XAI methods based on the output and what we call the practical attributes. The aim of the framework is to ensure that everything that can be held constant for the purpose of comparison, is held constant and to ignore many of the subjective elements present in the area of XAI. An example utilizing such a comparison along the lines of the proposed framework is performed on local, post-hoc, model-agnostic XAI algorithms which are designed to measure the feature importance/contribution for a queried instance. These algorithms are assessed on two criteria using synthetic datasets across a range of classifiers. The first is based on selecting features which contribute to the underlying data structure and the second is how accurately the algorithms select the features used in a decision tree path. The results from the first comparison showed that when the classifier was able to pick up the underlying pattern in the model, the LIME algorithm was the most accurate at selecting the underlying ground truth features. The second test returned mixed results with some instances in which the XAI algorithms were able to accurately return the features used to produce predictions, however this result was not consistent.

Physically Unclonable Functions (PUFs) are hardware-based security primitives that utilize manufacturing process variations to realize binary keys (Weak PUFs) or binary functions (Strong PUFs). This primitive is desirable for key generation and authentication in constrained devices, due to its low power and low area overhead. However, in recent years many research papers are focused on the vulnerability of PUFs to modeling attacks. This attack is possible because the PUFs challenge and response exchanges are usually transmitted over communication channel without encryption. Thus, an attacker can collect challenge-response pairs and use it as input into a learning algorithm, to create a model that can predict responses given new challenges. In this paper we introduce a serial and a parallel novel 64-bits memory-based controlled PUF (Mc-PUF) architecture for device authentication that has high uniqueness and randomness, reliable, and resilient against modeling attacks. These architectures generate a response by utilizing bits extracted from the fingerprint of a synchronous random-access memory (SRAM) PUF with a control logic. The synthesis of the serial architecture yielded an area of 1.136K GE, while the parallel architecture was 3.013K GE. The best prediction accuracy obtained from the modeling attack was 50%, which prevents an attacker from accurately predicting responses to future challenges. We also showcase the scalability of the design through XOR-ing several Mc-PUFs, further improving upon its security and performance. The remainder of the paper presents the proposed architectures, along with their hardware implementations, area and power consumption, and security resilience against modeling attacks. The 3-XOR Mc-PUF had the greatest overhead, but it produced the best randomness, uniqueness, and resilience against modeling attacks.