Biblio

Learning a disentangled representation of the latent space has become one of the most fundamental problems studied in computer vision. Recently, many Generative Adversarial Networks (GANs) have shown promising results in generating high fidelity images. However, studies to understand the semantic layout of the latent space of pre-trained models are still limited. Several works train conditional GANs to generate faces with required semantic attributes. Unfortunately, in these attempts, the generated output is often not as photo-realistic as the unconditional state-of-the-art models. Besides, they also require large computational resources and specific datasets to generate high fidelity images. In our work, we have formulated a Markov Decision Process (MDP) over the latent space of a pre-trained GAN model to learn a conditional policy for semantic manipulation along specific attributes under defined identity bounds. Further, we have defined a semantic age manipulation scheme using a locally linear approximation over the latent space. Results show that our learned policy samples high fidelity images with required age alterations, while preserving the identity of the person.

Due to broadcast nature of acoustic signal, underwater acoustic sensor networks face security challenge. In the paper, we propose a physical layer security transmission scheme with cooperative jamming. The proposed scheme takes advantage of the long propagation delay of the underwater acoustic channel to interfere with eavesdropper without affecting the reception of intended users. The results of both simulation and field experiment show that the proposed mechanism can improve the secrecy capacity of the network and effectively jam eavesdropper.

Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%.

Anomalous users detection in social network is an imperative task for security problems. Motivated by the great power of Graph Neural Networks(GNNs), many current researches adopt GNN-based detectors to reveal the anomalous users. However, the increasing scale of social activities, explosive growth of users and manifold technical disguise render the user detection a difficult task. In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model (RAU-GNN) to obtain a fine-grained detection result. RAU-GNN first extracts multiple relations of all types of users in social network, including both benign and anomalous users, and accordingly constructs the multiple user relation graph. Secondly, we employ relevance-aware GNN framework to learn the hidden features of users, and discriminate the anomalous users after discriminating. Concretely, by integrating Graph Convolution Network(GCN) and Graph Attention Network(GAT), we design a GCN-based relation fusion layer to aggregate initial information from different relations, and a GAT-based embedding layer to obtain the high-level embeddings. Lastly, we feed the learned representations to the following GNN layer in order to consolidate the node embedding by aggregating the final users' embeddings. We conduct extensive experiment on real-world datasets. The experimental results show that our approach can achieve high accuracy for anomalous users detection.

In order to increase the availability and reliability of photovoltaic (PV) systems, fault diagnosis and condition monitoring of inverters are of crucial means to meet the goals. Numerous methods are implemented for fault diagnosis of PV inverters, providing robust features and handling massive amount of data. However, existing methods rely on simplistic frameworks that are incapable of inspecting a wide range of intrinsic and explicit features, as well as being time-consuming. In this paper, a novel method based on a multilayer deep belief network (DBN) is suggested for fault diagnosis, which allows the framework to discover the probabilistic reconstruction across its inputs. This approach equips a robust hierarchical generative model for exploiting features associated with faults, interprets functions that are highly variable, and needs lesser prior information. Moreover, the method instantaneously categorizes the fault conditions, which eventually strengthens the adaptability of applying it on a variety of diagnostic problems in an inverter domain. The proposed method is evaluated using multiple input signals at different sampling frequencies. To evaluate the efficacy of DBN, a test model based on a three-phase 2-level grid-tied PV inverter was used. The results show that the method is capable of achieving precise diagnosis operations.

With the spread of the Internet, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. The first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device at the end. To address these issues, fog computing, which is located in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. The main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk measures by evaluating the risk values. The proposed risk countermeasures for fog computing should help us to utilize IoT devices in a safe and secure manner.

In recent years, new cyber attacks such as targeted attacks have caused extensive damage. With the continuing development of the IoT society, various devices are now connected to the network and are being used for various purposes. The Internet of Things has the potential to link cyber risks to actual property damage, as cyberspace risks are connected to physical space. With this increase in unknown cyber risks, the demand for cyber insurance is increasing. One of the most serious emerging risks is the silent cyber risk, and it is likely to increase in the future. However, at present, security measures against silent cyber risks are insufficient. In this study, we conducted a risk management of silent cyber risk for organizations with the objective of contributing to the development of risk management methods for new cyber risks that are expected to increase in the future. Specifically, we modeled silent cyber risk by focusing on state transitions to different risks. We newly defined two types of silent cyber risk, namely, Alteration risk and Combination risk, and conducted risk assessment. Our assessment identified 23 risk factors, and after analyzing them, we found that all of them were classified as Risk Transference. We clarified that the most effective risk countermeasure for Alteration risk was insurance and for Combination risk was measures to reduce the impact of the risk factors themselves. Our evaluation showed that the silent cyber risk could be reduced by about 50%, thus demonstrating the effectiveness of the proposed countermeasures.

Advancing artificial neural networks to the coherent optical domain offers several advantages, such as a filterless synaptic interconnect with increased routing flexibility. Towards this direction, a coherent synaptic receptor with integrated multiplication function will be experimentally evaluated for a 1-GHz train of 130-ps spikes.

Threat Intelligence has been a key part of the success of Intrusion Detection, with several trusted sources leading to wide adoption and greater understanding of new and trending threats to computer networks. Identifying potential threats and live attacks on networks is only half the battle, knowing how to correctly respond to these threats and attacks requires in-depth and domain specific knowledge, which may be unique to subject experts and software vendors. Network Incident Responders and Intrusion Response Systems can benefit from a similar approach to Threat Intel, with a focus on potential Response actions. A qualitative comparison of current Threat Intel Sources and prominent Intrusion Response Systems is carried out to aid in the identification of key requirements to be met to enable the adoption of Response Intel. Building on these requirements, a template for Response Intel is proposed which incorporates standardised models developed by MITRE. Similarly, to facilitate the automated use of Response Intel, a structure for automated Response Actions is proposed.

Software vulnerabilities have become a major problem for the security analysts, since the number of new vulnerabilities is constantly growing. Thus, there was a need for a categorization system, in order to group and handle these vulnerabilities in a more efficient way. Hence, the MITRE corporation introduced the Common Weakness Enumeration that is a list of the most common software and hardware vulnerabilities. However, the manual task of understanding and analyzing new vulnerabilities by security experts, is a very slow and exhausting process. For this reason, a new automated classification methodology is introduced in this paper, based on the vulnerability textual descriptions from National Vulnerability Database. The proposed methodology, combines textual analysis and tree-based machine learning techniques in order to classify vulnerabilities automatically. The results of the experiments showed that the proposed methodology performed pretty well achieving an overall accuracy close to 80%.

With advantages of low cost, high mobility, and flexible deployment, unmanned aerial vehicle (UAVs) are employed to efficiently detect abnormal vehicle behaviors (AVBs) in the internet of vehicles (IoVs). However, due to limited resources including battery, computing, and communication, UAVs are selfish to work cooperatively. To solve the above problem, in this paper, a game theoretical UAV incentive scheme in IoVs is proposed. Specifically, the abnormal behavior model is first constructed, where three model categories are defined: velocity abnormality, distance abnormality, and overtaking abnormality. Then, the barging pricing framework is designed to model the interactions between UAVs and IoVs, where the transaction prices are determined with the abnormal behavior category detected by UAVs. At last, simulations are conducted to verify the feasibility and effectiveness of our proposed scheme.

The recent analysis indicates more than 250,000 people in the United States of America (USA) die every year because of medical errors. World Health Organisation (WHO) reports states that 2.6 million deaths occur due to medical and its prescription errors. Many of the errors related to the wrong drug/dosage administration by caregivers to patients due to indecipherable handwritings, drug interactions, confusing drug names, etc. The espousal of Mobile-based speech recognition applications will eliminate the errors. This allows physicians to narrate the prescription instead of writing. The application can be accessed through smartphones and can be used easily by everyone. An application program interface has been created for handling requests. Natural language processing is used to read text, interpret and determine the important words for generating prescriptions. The patient data is stored and used according to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) guidelines. The SMS4-BSK encryption scheme is used to provide the data transmission securely over Wireless LAN.

Cyber Threat Intelligence (CTI) sharing platforms are valuable tools in cybersecurity. However, despite the fact that effective CTI exchange highly depends on human aspects, cyber behavior in CTI sharing platforms has been notably less investigated by the security research community.Motivated by this research gap, we ground our work in the concrete challenge of understanding users’ perceptions of information sharing in CTI platforms. To this end, we propose a conceptual workflow and toolchain that would seek to verify whether users have an accurate comprehension of how far information travels when shared in a CTI sharing platform.We contextualize our concept within MISP as a use case, and discuss the benefits of our socio-technical approach as a potential tool for security analysis, simulation, or education/training support. We conclude with a brief outline of future work that would seek to evaluate and validate the proposed model.

Random K-out graphs, denoted \$$\backslash$mathbbH(n;K)\$, are generated by each of the \$n\$ nodes drawing \$K\$ out-edges towards \$K\$ distinct nodes selected uniformly at random, and then ignoring the orientation of the arcs. Recently, random K-out graphs have been used in applications as diverse as random (pairwise) key predistribution in ad-hoc networks, anonymous message routing in crypto-currency networks, and differentially-private federated averaging. In many applications, connectivity of the random K-out graph when some of its nodes are dishonest, have failed, or have been captured is of practical interest. We provide a comprehensive set of results on the connectivity and giant component size of \$$\backslash$mathbbH(n;K\_n,$\backslash$gamma\_n)\$, i.e., random K-out graph when \textsubscriptn of its nodes, selected uniformly at random, are deleted. First, we derive conditions for \textsubscriptn and \$n\$ that ensure, with high probability (whp), the connectivity of the remaining graph when the number of deleted nodes is \$$\backslash$gamma\_n=Ømega(n)\$ and \$$\backslash$gamma\_n=o(n)\$, respectively. Next, we derive conditions for \$$\backslash$mathbbH(n;K\_n, $\backslash$gamma\_n)\$ to have a giant component, i.e., a connected subgraph with \$Ømega(n)\$ nodes, whp. This is also done for different scalings of \textsubscriptn and upper bounds are provided for the number of nodes outside the giant component. Simulation results are presented to validate the usefulness of the results in the finite node regime.

With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9.25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.

The accessibility of the internet and mobile platforms has risen dramatically due to digital technology innovations. Web applications have opened up a variety of market possibilities by supplying consumers with a wide variety of digital technologies that benefit from high accessibility and functionality. Around the same time, web application protection continues to be an important challenge on the internet, and security must be taken seriously in order to secure confidential data. The threat is caused by inadequate validation of user input information, software developed without strict adherence to safety standards, vulnerability of reusable software libraries, software weakness, and so on. Through abusing a website's vulnerability, introduers are manipulating the user's information in order to exploit it for their own benefit. Then introduers inject their own malicious code, stealing passwords, manipulating user activities, and infringing on customers' privacy. As a result, information is leaked, applications malfunction, confidential data is accessed, etc. To mitigate the aforementioned issues, stacking ensemble based classifier model for Cross-site scripting (XSS) attack detection is proposed. Furthermore, the stacking ensembles technique is used in combination with different machine learning classification algorithms like k-Means, Random Forest and Decision Tree as base-learners to reliably detect XSS attack. Logistic Regression is used as meta-learner to predict the attack with greater accuracy. The classification algorithms in stacking model explore the problem in their own way and its results are given as input to the meta-learner to make final prediction, thus improving the overall detection accuracy of XSS attack in stacking than the individual models. The simulation findings demonstrate that the proposed model detects XSS attack successfully.

Proofs in simulation-based frameworks have the greatest rigor when they are machine checked. But the level of details in these proofs surpasses what the formal-methods community can handle with existing tools. Existing formal results consider streamlined versions of simulation-based frameworks to cope with this complexity. Hence, a central question is how to abstract details from composability results and enable their formal verification.In this paper, we focus on the modeling of system communication in composable security statements. Existing formal models consider fixed communication patterns to reduce the complexity of their proofs. However, as we will show, this can affect the reusability of security statements. We propose an abstract approach to modeling system communication in Constructive Cryptography that avoids this problem. Our approach is suitable for mechanized verification and we use CryptHOL, a framework for developing mechanized cryptography proofs, to implement it in the Isabelle/HOL theorem prover. As a case study, we formalize the construction of a secure channel using Diffie-Hellman key exchange and a one-time-pad.

Distributed denial of service attacks threaten the security and health of the Internet. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. Our previous work revealed a critical problem with conventional machine learning models. Conventional models are unable to generalize on the temporal nature of network flow data to classify attacks. We thus explored the use of deep learning techniques on real flow data. We found that a variety of attacks could be identified with high accuracy compared to previous approaches. We show that a convolutional neural network can be implemented for this problem that is suitable for large volumes of data while maintaining useful levels of accuracy.

The measurement of room acoustics plays a wide role in audio research, from physical acoustics modelling and virtual reality applications to speech enhancement. While vast literature exists on position-dependent room acoustics and coupling of rooms, little has explored the transition from one room to its neighbour. This paper presents the measurement and analysis of a dataset of spatial room impulse responses for the transition between four coupled room pairs. Each transition consists of 101 impulse responses recorded using a fourth-order spherical microphone array in 5 cm intervals, both with and without a continuous line-of-sight between the source and microphone. A numerical analysis of the room transitions is then presented, including direct-to-reverberant ratio and direction of arrival estimations, along with potential applications and uses of the dataset.

Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources.

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level information from the behavior of a trained object detector, by seeking the smallest areas of the image from which the object detector produces almost the same result as it does from the whole image. These areas constitute a bounding-box attribution map (BBAM), which identifies the target object in its bounding box and thus serves as pseudo ground-truth for weakly supervised semantic and instance segmentation. This approach significantly outperforms recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in weakly supervised semantic and instance segmentation. In addition, we provide a detailed analysis of our method, offering deeper insight into the behavior of the BBAM.

In recent years, more and more medical institutions have been using electronic medical records (EMRs) to improve service efficiency and reduce storage cost. However, it is difficult for medical institutions with different management methods to share medical data. The medical data of patients is easy to be abused, and there are security risks of privacy data leakage. The above problems seriously impede the sharing of medical data. To solve these problems, we propose a blockchain-based medical data sharing scheme with attribute-based searchable encryption, named BMDS. In BMDS, encrypted EMRs are securely stored in the interplanetary file system (IPFS), while corresponding indexes and other information are stored in a medical consortium blockchain. The proposed BMDS has the features of tamper-proof, privacy preservation, verifiability and secure key management, and there is no single point of failure. The performance evaluation of computational overhead and security analysis show that the proposed BMDS has more comprehensive security features and practicability.

Cryptography is the science or process used for the encryption and decryption of data that helps the users to store important information or share it across networks where it can be read only by the intended user. In this paper, Elliptic Curve Cryptography (ECC) has been proposed because of its small key size, less memory space and high speed. Elliptic curve scalar multiplication is an important part of elliptic curve systems. Here, the scalar multiplication is performed with the help of hybrid Karatsuba multiplier as the area utilization of Karatsuba multiplier is less. An alternative of digital signature algorithm, that is, Elliptic Curve Digital Signature Algorithm (ECDSA) along with the primary operations of elliptic curves have also been discussed in this paper.