Biblio

Autonomous vehicles (AVs) offer a wide range of promising benefits by reducing traffic accidents, environmental pollution, traffic congestion and land usage etc. However, to reap the intended benefits of AVs, it is inevitable that this technology should be trusted and accepted by the public. The consumer's substantial trust upon AVs will lead to its widespread adoption in the real-life. It is well understood that the preservation of strong security and privacy features influence a consumer's trust on a product in a positive manner. In this paper, we introduce a novel concept of digital labels for AVs to increase consumers awareness and trust regarding the security level of their vehicle. We present an architecture called Cybersecurity Box (CSBox) that leverages digital labels to display and inform consumers and passengers about cybersecurity status of the AV in use. The introduction of cybersecurity digital labels on the dashboard of AVs would attempt to increase the trust level of consumers and passengers on this promising technology.

When location-based services (LBS) are delivered, location data should be protected against honest-but-curious LBS providers, them being quasi-identifiers. One of the existing approaches to achieving this goal is location k-anonymity, which leverages the presence of a trusted party, called location trusted service (LTS), playing the role of anonymizer. A drawback of this approach is that the location trusted service is a single point of failure and traces all the users. Moreover, the protection is completely nullified if a global passive adversary is allowed, able to monitor the flow of messages, as the source of the query can be identified despite location k-anonymity. In this paper, we propose a distributed and hierarchical LTS model, overcoming both the above drawbacks. Moreover, position notification is used as cover traffic to hide queries and multicast is minimally adopted to hide responses, to keep k-anonymity also against the global adversary, thus enabling the possibility that LBS are delivered within social networks.

We analyze partner health apps compatible with the Fitbit fitness tracker, and record what third parties they are talking to. We focus on the ten partner Android applications that have more than 50,000 downloads and are fitness-related. Our results show that most of the them contact “unexpected” third parties. Such third parties include social networks; analytics and advertisement services; weather APIs. We also investigate what information is shared by the partner apps with these unexpected entities. Our findings suggest that in many cases personal information of users might be shared, including the phone model; location and SIM carrier; email and connection history.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.

Malware evolves quickly as new attack, evasion and mutation techniques are commonly used by hackers to build new malicious malware families. For malware detection and classification, multi-class learning model is one of the most popular machine learning models being used. To recognize malicious programs, multi-class model requires malware types to be predefined as output classes in advance which cannot be dynamically adjusted after the model is trained. When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely. This consumes a significant amount of time and resources, and cannot adapt quickly to meet the timely requirement in dealing with dynamically evolving malware types. To cope with the problem, an evolvable malware classification deep learning model, namely EC-Model, is proposed in this paper which can dynamically adapt to new malware types without the need of fully retraining. Consequently, the reaction time can be significantly reduced to meet the timely requirement of malware classification. To our best knowledge, our work is the first attempt to adopt multi-task, deep learning for evolvable malware classification.

Experimental implementation of phase-locked loop (PLL) with bandpass filter is proposed. Such PLL is noteworthy for neuron-like dynamics. It generates both regular and chaotic spikes and bursts. Previously proposed hardware implementation of this system has significant disadvantage – absence of excitable (non-oscillating) mode that is vital for brain neurons. The proposed electronic neuron-like generator is modified and could be used for hardware implementation of spiking neural networks.

With the popularity of cloud computing, cloud storage technology has also been widely used. Among them, data integrity verification is a hot research topic. At present, the realization of public auditing has become the development trend of integrity verification. Most existing public auditing schemes rarely consider some indispensable functions at the same time. Thus, in this paper, we propose a comprehensive public auditing scheme (PDBPA) that can simultaneously realize data block privacy protection, data dynamics, and multi- user batch auditing. Our PDBPA scheme is implemented in bilinear pairing. By adding random masking in the audit phase, with the help of the characteristics of homomorphic verifiable tags (HVTs), it can not only ensure that the TPA performs the audit work correctly, but also prevent it from exploring the user’s sensitive data. In addition, by utilizing the modified index hash table (MIHT), data dynamics can be effectively achieved. Furthermore, we provide a specific process for the TPA to perform batch audits for multiple users. Moreover, we formally prove the security of the scheme; while achieving the audit correctness, it can resist three types of attacks.

It is important to ensure source location privacy (SLP) protection in safety-critical monitoring applications. Also, to achieve effective long-term monitoring, it is essential to design SLP protocols with high energy efficiency and energy balancing. Therefore, this study proposes a new phantom with angle (PwA) protocol. The PwA protocol employs dynamic routing paths which are designed to achieve SLP protection with energy efficiency and energy balancing. Analysis results reveal that the PwA protocol exhibits superior performance features to outperform existing protocols by achieving high levels of SLP protection for time petime periods. The results confirm that the PwA protocol is practical in long-term monitoring systems.riods. The results confirm that the PwA protocol is practical in long-term monitoring systems.

Smart Grid is used to improve the accuracy of the grid network query. Though it gives the accuracy, it has the data privacy issues. It is a big challenge to solve the privacy issue in the smart grid. We need secured algorithms to protect the data in the smart grid, since the data is very important. This paper explains about the k-anonymous algorithm and analyzes the enhanced L-diversity algorithm for data privacy and security. The algorithm can protect the data in the smart grid is proven by the experiments.

This work presents a system which allows image data and extracted features from a real-world location to be captured and modelled in a Virtual Reality (VR) environment combined with Augmented Situated Visualizations (ASV) overlaid and registered in a virtual environment. Combining these technologies with techniques from Data Science and Artificial Intelligence (AI)(such as image analysis and 3D reconstruction) allows the creation of a setting where remote locations can be modelled and interacted with from anywhere in the world. This Enhanced Photorealistic Immersive (EPI) system is highly adaptable to a wide range of use cases and users as it can be utilized to model and interact with any environment which can be captured as image data (such as training for operation in hazardous environments, accessibility solutions for exploration of historical/tourism locations and collaborative learning environments). A use case example focused on a structural examination of railway tunnels along with a pilot study is presented, which can demonstrate the usefulness of the EPI system.

Nowadays, face recognition is used everywhere in all fields. Though the face recognition is used for security purposes there is also chance in hacking the faces which is used for face recognition. For enhancing the face security, encryption and decryption technique is used. Face cognizance has been engaged in more than a few security-connected purposes such as supervision, e-passport, and etc… The significant use of biometric raises vital private concerns, in precise if the biometric same method is carried out at a central or unfrosted servers, and calls for implementation of Privacy improving technologies. For privacy concerns the encoding and decoding is used. For achieving the result we are using the Open Computer Vision (OpenCV) tool. With the help of this tool we are going to cipher the face and decode the face with advanced encryption standards techniques. OpenCV is the tool used in this project

Malicious software (malware) creators are constantly mutating malware files in order to avoid detection, resulting in hundreds of millions of new malware every year. Therefore, most malware files are unlabeled due to the time and cost needed to label them manually. This makes it very challenging to perform malware detection, i.e., deciding whether a file is malware or not, and malware classification, i.e., determining the family of the malware. Most solutions use supervised learning (e.g., ResNet and VGG) whose accuracy degrades significantly with the lack of abundance of labeled data. To solve this problem, this paper proposes a semi-supervised learning model for image-based malware classification. In this model, malware files are represented as grayscale images, and semi-supervised learning is carefully selected to handle the plethora of unlabeled data. Our proposed model is an enhanced version of the ∏-model, which makes it more accurate and consistent. Experiments show that our proposed model outperforms the original ∏-model by 4% in accuracy and three other supervised models by 6% in accuracy especially when the ratio of labeled samples is as low as 20%.

The unmatched threat of Android malware has tremendously increased the need for analyzing prominent malware samples. There are remarkable efforts in static and dynamic malware analysis using static features and API calls respectively. Nonetheless, there is a void to classify Android malware by analyzing its behavior using multiple dynamic characteristics. This paper proposes EntropLyzer, an entropy-based behavioral analysis technique for classifying the behavior of 12 eminent Android malware categories and 147 malware families taken from CCCS-CIC-AndMal2020 dataset. This work uses six classes of dynamic characteristics including memory, API, network, logcat, battery, and process to classify and characterize Android malware. Results reveal that the entropy-based analysis successfully determines the behavior of all malware categories and most of the malware families before and after rebooting the emulator.

The revolution caused by the advanced analysis features of Internet of Things and big data have made a big turnaround in the digital world. Data analysis is not only limited to collect useful data but also useful in analyzing information quickly. Therefore, most of the variants of the shared system based on the parallel structural model are explored simultaneously as the appropriate big data storage library stimulates researchers’ interest in the distributed system. Due to the emerging digital technologies, different groups such as healthcare facilities, financial institutions, e-commerce, food service and supply chain management generate a surprising amount of information. Although the process of statistical analysis is essential, it can cause significant security and privacy issues. Therefore, the analysis of data privacy protection is very important. Using the platform, technology should focus on providing Advanced Cryptography Policy (ACP). This research explores different security risks, evolutionary mechanisms and risks of privacy protection. It further recommends the post-statistical modern privacy protection act to manage data privacy protection in binary format, because it is kept confidential by the user. The user authentication program has already filed access restrictions. To maintain this purpose, everyone’s attitude is to achieve a changing identity. This article is designed to protect the privacy of users and propose a new system of restoration of controls.

Security is the key concern, which allows safe communication between any two mobile nodes in an unfavorable environment. Wireless Ad Hoc can be unsecured against attacks by means of malicious nodes. Hence this study assesses the influence of wormhole attacks on Mobile Ad Hoc network (MANET) system that is evaluated and validated based on the QualNet simulator. The MANET performance is investigated utilizing the wormhole attacks. The simulation is performed on Mobile node's network layer and data link layer in the WANET (wireless Ad Hoc network). The MANET performance was examined using “what-if” analyses too. Results showed that for security purposes, it is indeed necessary to assess the Mobile Ad Hoc node deployment.

Replacing real life with the virtual space has long ceased to be a theory. Among the whole variety of visualization, systems that allow projecting non-existent objects into real-world space are especially distinguished. Thus, augmented reality technology has found its application in many different fields. The article discusses the general concepts and principles of building augmented reality systems.

As the public cloud becomes one of the leading ways in data sharing nowadays, data confidentiality and user privacy are increasingly critical. Partially policy-hidden ciphertext policy attribute-based encryption (CP-ABE) can effectively protect data confidentiality while reducing privacy leakage by hiding part of the access structure. However, it cannot satisfy the need of data sharing in the public cloud with complex users and large amounts of data, both in terms of less expressive access structures and limited granularity of policy hiding. Moreover, the verification of access right to shared data and correctness of decryption are ignored or conducted by an untrusted third party, and the prime-order groups are seldom considered in the expressive policy-hidden schemes. This paper proposes a fully policy-hidden CP-ABE scheme constructed on LSSS access structure and prime-order groups for public cloud data sharing. To help users decrypt, HVE with a ``convert step'' is applied, which is more compatible with CP-ABE. Meanwhile, decentralized credible verification of access right to shared data and correctness of decryption based on blockchain are also provided. We prove the security of our scheme rigorously and compare the scheme with others comprehensively. The results show that our scheme performs better.

Tor is a famous routing overlay network based on the Onion multi-layered encryption to support communication anonymity in a threat model in which some network nodes are malicious. However, Tor does not provide any protection against the global passive adversary. In this threat model, an idea to obtain recipient anonymity, which is enough to have relationship anonymity, is to hide the recipient among a sufficiently large anonymity set. However, this would lead to high latency both in the set-up phase (which has a quadratic cost in the number of involved nodes) and in the successive communication. In this paper, we propose a way to arrange a Tor circuit with a tree-like topology, in which the anonymity set consists of all its nodes, whereas set-up and communication latency depends on the number of the sole branch nodes (which is a small fraction of all the nodes). Basically, the cost goes down from quadratic to linear. Anonymity is obtained by applying a broadcast-based technique for the forward message, and cover traffic (generated by the terminal-chain nodes) plus mixing over branch nodes, for the response.

We are attending a severe zero-day cyber attacks. Machine learning based anomaly detection is definitely the most efficient defence in depth approach. It consists to analyzing the network traffic in order to distinguish the normal behaviour from the abnormal one. This approach is usually implemented in a central server where all the network traffic is analyzed which can rise privacy issues. In fact, with the increasing adoption of Cloud infrastructures, it is important to reduce as much as possible the outsourcing of such sensitive information to the several network nodes. A better approach is to ask each node to analyze its own data and then to exchange its learning finding (model) with a coordinator. In this paper, we investigate the application of federated learning for network-based intrusion detection. Our experiment was conducted based on the C ICIDS2017 dataset. We present a f ederated learning on a deep learning algorithm C NN based on model averaging. It is a self-learning system for detecting anomalies caused by malicious adversaries without human intervention and can cope with new and unknown attacks without decreasing performance. These experimentation demonstrate that this approach is effective in detecting intrusion.

Third party mobile applications are dominating the business strategies of organisations and have become an integral part of personal life of individuals. These applications are used for financial transactions, sharing of sensitive data etc. The recent breaches in Android clearly indicate that use of third party applications have become a serious security threat. By design, Android framework keeps all these applications in untrusted domain. Due to this a common policy of resource control exists for all such applications. Further, user discretion in granting permissions to specific applications is not effective because users are not always aware of deep functionalities, mala fide intentions (in case of spywares) and bugs/flaws in these third-party applications. In this regard, we propose a security scheme to mitigate unauthorised access of resources by third party applications. Our proposed scheme is based on SEAndroid policies and achieves fine grained confinement with respect to access control for the third party applications. To the best of our knowledge, the proposed scheme is unique and first of its kind. The proposed scheme is integrated with Android Oreo 8.1.0 for performance and security analysis. It is compatible with any Android device with AOSP support.

Fitbit Versa is the most popular of its predecessors and successors in the Fitbit faction. Increasingly data stored on these smart fitness devices, their linked applications and cloud datacenters are being used for criminal convictions. There is limited research for investigators on wearable devices and specifically exploring evidence identification and methods of extraction. In this paper we present our analysis of Fitbit Versa using Cellebrite UFED and MSAB XRY. We present a clear scope for investigation and data significance based on the findings from our experiments. The data recovery will include logical and physical extractions using devices running Android 9 and iOS 12, comparing between Cellebrite and XRY capabilities. This paper discusses databases and datatypes that can be recovered using different extraction and analysis techniques, providing a robust outlook of data availability. We also discuss the accuracy of recorded data compared to planned test instances, verifying the accuracy of individual data types. The verifiable accuracy of some datatypes could prove useful if such data was required during the evidentiary processes of a forensic investigation.

In this current age, stakeholders exchange legal documents, as well as documents that are official, sensitive and confidential via digital channels[1]. To securely communicate information between stakeholders is not an easy task considering the intentional or unintentional changes and possible attacks that can occur during communication. This paper focuses on protecting and securing data by hiding the data using steganography techniques, after encrypting the data to avoid unauthorized changes or modification made by adversaries to the data through using the Simplified Data Encryption Technique. By leveraging on these two approaches, secret data security intensifies to two levels and a steganography image of high quality is attained. Cryptography converts plaintext into cipher text (unreadable text); whereas steganography is the technique of hiding secret messages in other messages. First encryption of data is done using the Simplified Data Encryption Standard (S-DES) algorithm after which the message encrypted is embedded in the cover image by means of the Least Significant Bit (LSB) approach.

The advent of 5G, one of the most recent and promising technologies currently under deployment, fulfills the emerging needs of mobile subscribers by introducing several new technological advancements. However, this may lead to numerous attacks in the emerging 5G networks. Thus, to guarantee the secure transmission of user data, 5G Authentication protocols such as Extensible Authentication Protocol - Authenticated Key Agreement Protocol (EAP-AKA) were developed. These protocols play an important role in ensuring security to the users as well as their data. However, there exists no guarantees about the security of the protocols. Thus formal verification is necessary to ensure that the authentication protocols are devoid of vulnerabilities or security loopholes. Towards this goal, we formally verify the security of the 5G EAP-AKA protocol using an automated verification tool called ProVerif. ProVerif identifies traces of attacks and checks for security loopholes that can be accessed by the attackers. In addition, we model the complete architecture of the 5G EAP-AKA protocol using the language called typed pi-calculus and analyze the protocol architecture through symbolic model checking. Our analysis shows that some cryptographic parameters in the architecture can be accessed by the attackers which cause the corresponding security properties to be violated.

Nowadays, source location privacy in underwater acoustic sensor networks (UASNs) has gained a lot of attention. The aim of source location privacy is to use specific technologies to protect the location of the source from being compromised. Among the many technologies available are fake packet technology, multi-path routing technology and so on. The fake packet technology uses a certain amount of fake packets to mask the transmission of the source packet, affecting the adversary's efficiency of hop-by-hop backtracking to the source. However, during the operation of the fake packet technology, the fake packet, and the source packet may interfere with each other. Focus on this, a fake packet time slot assignment-based source location privacy protection (FPTSA-SLP) scheme. The time slot assignment is adopted to avoid interference with the source packet. Also, a relay node selection method based on the handshake is further proposed to increase the diversity of the routing path to confuse the adversary. Compared with the comparison algorithm, the simulation results demonstrate that the proposed scheme has a better performance in safety time.

In this article we propose the architecture of a system in which its central objective is focused on creating a complete framework for creating outdoor environments of Augmented Reality (AR) and Virtual Reality (VR) allowing its users to digitize reality for hypermedia format. Subsequently, there will be an internal process with the objective of merging / grouping these 3D models, thus enabling clear and intuitive navigation within infinite virtual realities (based on the captured real world). In this way, the user is able to create points of interest within their parallel realities, being able to navigate and traverse their new worlds through these points.