Biblio

The recent technological advances and changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not have access to this information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project’s Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.

Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%.

A door enables you to enter a room without breaking through a wall. Also, a door enables you for privacy, environmental or security reasons. The problem statement which is the biometric system sometimes is sensitive and will not be able to sense the biological pattern of the employer’s fingerprint due to sweat and other factors. Next, people tend to misplace their key or RFID card. Apart from that, people tend to forget their pin number for a door lock. The objective of this paper is to present a secret knock intensity for door lock security system using Arduino and mobile. This project works by using a knock intensity and send the information to mobile application via wireless network to unlock or lock the door.

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.

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.

Over the last few years, the world has been moving towards digital healthcare, where harnessing medical data distributed across multiple healthcare providers is essential to achieving personalized treatments. Though the efficiency and speed of the diagnosis process have increased due to the digitalization of healthcare data, it is at constant risk of cyberattacks. Medical images, in particular, seem to have become a regular victim of hackers, due to which there is a need to find a feasible solution for storing them securely. This work proposes a blockchain-based framework that leverages the InterPlanetary File system (IPFS) to provide decentralized storage for medical images. Our proposed blockchain storage model is implemented in the IPFS distributed file-sharing system, where each image is stored on IPFS, and its corresponding unique content-addressed hash is stored in the blockchain. The proposed model ensures the security of the medical images without any third-party dependency and eliminates the obstacles that arise due to centralized storage.

The main objective of the proposed work is to build a reliable and secure architecture for cloud servers where users may safely store and transfer their data. This platform ensures secure communication between the client and the server during data transfer. Furthermore, it provides a safe method for sharing and transferring files from one person to another. As a result, for ensuring safe data on cloud servers, this research work presents a secure architecture combining three DNA cryptography, HMAC, and a third party Auditor. In order to provide security by utilizing various strategies, a number of traditional and novel cryptographic methods are investigated. In the first step, data will be encrypted with the help of DNA cryptography, where the encoded document will be stored in the cloud server. In next step, create a HMAC value of encrypted file, which was stored on cloud by using secret key and sends to TPA. In addition, Third Party Auditor is used for authenticate the purity of stored documents in cloud at the time of verification TPA also create HMAC value from Cloud stored data and verify it. DNA-based cryptographic technique, hash based message authentic code and third party auditor will provide more secured framework for data security and integrity in cloud server.

Dynamic searchable symmetric encryption (DSSE) that enables a client to perform searches and updates on encrypted data has been intensively studied in cloud computing. Recently, forward privacy and backward privacy has engaged significant attention to protect DSSE from the leakage of updates. However, the research in this field almost focused on keyword-level updates. That is, the client needs to know the keywords of the documents in advance. In this paper, we proposed a document-level update scheme, DBP, which supports immediate deletion while guaranteeing forward privacy and backward privacy. Compared with existing forward and backward private DSSE schemes, our DBP scheme has the following merits: 1) Practicality. It achieves deletion based on document identifiers rather than document/keyword pairs; 2) Efficiency. It utilizes only lightweight primitives to realize backward privacy while supporting immediate deletion. Experimental evaluation on two real datasets demonstrates the practical efficiency of our scheme.

Cloud computing is a technology that provides users with a large storage space and an enormous computing power. For privacy purpose, the sensitive data should be encrypted before being outsourced to the cloud. To search over the outsourced data, searchable encryption (SE) schemes have been proposed in the literature. An SE scheme should perform searches over encrypted data without causing any sensitive information leakage. To this end, a few security constraints were elaborated to guarantee the security of the SE schemes, namely, the keyword privacy, the trapdoor unlinkability, and the access pattern. The latter is very hard to be respected and most approaches fail to guarantee the access pattern constraint when performing a search. This constraint consists in hiding from the server the search result returned to the user. The non respect of this constraint may cause sensitive information leakage as demonstrated in the literature. To fix this security lack, we propose a method that allows to securely request and receive the needed documents from the server after performing a search. The proposed method that we call the access pattern hiding (APH) technique allows to respect the access pattern constraint. An experimental study is conducted to validate the APH technique.

To ensure a secure Cloud-based Electronic Health Record (EHR) system, we need to encrypt data and impose field-level access control to prevent malicious usage. Since the attributes of the Users will change with time, the encryption policies adopted may also vary. For large EHR systems, it is often necessary to search through the encrypted data in realtime and perform client-side computations without decrypting all patient records. This paper describes our novel cloud-based EHR system that uses Attribute Based Encryption (ABE) combined with Semantic Web technologies to facilitate differential access to an EHR, thereby ensuring only Users with valid attributes can access a particular field of the EHR. The system also includes searchable encryption using keyword index and search trapdoor, which allows querying EHR fields without decrypting the entire patient record. The attribute revocation feature is efficiently managed in our EHR by delegating the revision of the secret key and ciphertext to the Cloud Service Provider (CSP). Our methodology incorporates advanced security features that eliminate malicious use of EHR data and contributes significantly towards ensuring secure digital health systems on the Cloud.

Cloud computing has helped in managing big data and providing resources remotely and ubiquitously, but it has some latency and security concerns. Fog has provided tremendous advantages over cloud computing which include low latency rate, improved real-time interactions, reduced network traffic overcrowding, and improved reliability, however, security concerns need to be addressed separately. Another major issue in the cloud is Cloud Lock-in/Vendor Lock-in. Through this research, an effort has been made to extend fog computing and Searchable Encryption technologies. The proposed system can reduce the issue of cloud lock-in faced in traditional cloud computing. The SE schemes used in this paper are Symmetric Searchable Encryption (SSE) and Multi-keyword Ranked Searchable Encryption (MRSE) to achieve confidentiality, privacy, fine-grained access control, and efficient keyword search. This can help to achieve better access control and keyword search simultaneously. An important use of this technique is it helps to prevent the issue of cloud/vendor lock-in. This can shift some computation and storage of index tables over fog nodes that will reduce the dependency on Cloud Service Providers (CSPs).

Given that an increasingly larger part of an organization's activity is taking place online, especially in the current situation caused by the COVID-19 pandemic, network log data collected by organizations contain an accurate image of daily activity patterns. In some scenarios, it may be useful to share such data with other parties in order to improve collaboration, or to address situations such as cyber-security incidents that may affect multiple organizations. However, in doing so, serious privacy concerns emerge. One can uncover a lot of sensitive information when analyzing an organization's network logs, ranging from confidential business interests to personal details of individual employees (e.g., medical conditions, political orientation, etc). Our objective is to enable organizations to share information about their network logs, while at the same time preserving data privacy. Specifically, we focus on enabling encrypted search at network flow granularity. We consider several state-of-the-art searchable encryption flavors for this purpose (including hidden vector encryption and inner product encryption), and we propose several customized encoding techniques for network flow information in order to reduce the overhead of applying state-of-the-art searchable encryption techniques, which are notoriously expensive.

Cyberattacks have been the major concern with the growing advancement in technology. Complex security models have been developed to combat these attacks, yet none exhibit a full-proof performance. Recently, several machine learning (ML) methods have gained significant popularity in offering effective and efficient intrusion detection schemes which assist in proactive detection of multiple network intrusions, such as Denial of Service (DoS), Probe, Remote to User (R2L), User to Root attack (U2R). Multiple research works have been surveyed based on adopted ML methods (either signature-based or anomaly detection) and some of the useful observations, performance analysis and comparative study are highlighted in this paper. Among the different ML algorithms in survey, PSO-SVM algorithm has shown maximum accuracy. Using RBF-based classifier and C-means clustering algorithm, a new model i.e., combination of serial and parallel IDS is proposed in this paper. The detection rate to detect known and unknown intrusion is 99.5% and false positive rate is 1.3%. In PIDS (known intrusion classifier), the detection rate for DOS, probe, U2R and R2L is 99.7%, 98.8%, 99.4% and 98.5% and the False positive rate is 0.6%, 0.2%, 3% and 2.8% respectively. In SIDS (unknown intrusion classifier), the rate of intrusion detection is 99.1% and false positive rate is 1.62%. This proposed model has known intrusion detection accuracy similar to PSO - SVM and is better than all other models. Finally the future research directions relevant to this domain and contributions have been discussed.

Smart grid (SG) network is one of the recently improved networks of tangled entities, objects, and smart metering infrastructure (SMI). It plays a vital part in sensing, acquiring, observing, aggregating, controlling, and dealing with various kinds of fields in SG. The SMI or advanced metering infrastructure (AMI) is proposed to make available a real-time transmissions connection among users and services are Time of use (TOU), Real time pricing (RTP), Critical Peak Pricing (CPP). In adding to, additional benefit of SMs is which are capable to report back to the service control center in near real time nontechnical losses (for instance, tampering with meters, bypassing meters, and illicit tapping into distribution systems). SMI supports two-way transmission meters reading electrical utilization at superior frequency. This data is treated in real time and signals send to manage demand. This paper expresses a transitory impression of cyberattack instances in customary energy networks and SMI. This paper presents cyber security attacks and countermeasures in Smart Grid Metering Network (SGMN). Based on the existing survey threat models, a number of proposed ways have been planned to deal with all threats in the formulation of the secrecy and privacy necessities of SG measurement network.

The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate.

In federated learning, machine learning and deep learning models are trained globally on distributed devices. The state-of-the-art privacy-preserving technique in the context of federated learning is user-level differential privacy. However, such a mechanism is vulnerable to some specific model poisoning attacks such as Sybil attacks. A malicious adversary could create multiple fake clients or collude compromised devices in Sybil attacks to mount direct model updates manipulation. Recent works on novel defense against model poisoning attacks are difficult to detect Sybil attacks when differential privacy is utilized, as it masks clients' model updates with perturbation. In this work, we implement the first Sybil attacks on differential privacy based federated learning architectures and show their impacts on model convergence. We randomly compromise some clients by manipulating different noise levels reflected by the local privacy budget ε of differential privacy with Laplace mechanism on the local model updates of these Sybil clients. As a result, the global model convergence rates decrease or even leads to divergence. We apply our attacks to two recent aggregation defense mechanisms, called Krum and Trimmed Mean. Our evaluation results on the MNIST and CIFAR-10 datasets show that our attacks effectively slow down the convergence of the global models. We then propose a method to keep monitoring the average loss of all participants in each round for convergence anomaly detection and defend our Sybil attacks based on the training loss reported from randomly selected sets of clients as the judging panels. Our empirical study demonstrates that our defense effectively mitigates the impact of our Sybil attacks.

The Internet of Vehicles (IoV) is a network that considers vehicles as intelligent machines. They interact and communicate with each other to improve the performance and safety of traffic. IoV solves certain problems, but it has some issues such as response time, which prompted researchers to propose the integration of Fog Computing into vehicular networks. In Vehicular Fog Computing (VFC), the services are provided at the edge of the network to increase data rate and reduce response time. However, in order to satisfy network users, the security and privacy of sensitive data should be guaranteed. Using pseudonyms instead of real identities is one of the techniques considered to preserve the privacy of users, however, this can push malicious vehicles to exploit such a process and launch the Sybil attack by creating several pseudonyms in order to perform various malicious activities. In this paper, we describe the Sybil attack effects on VFC networks and compare them to those in conventional networks, as well as identify the various existing methods for detecting this attack and determine if they are applicable to VFC networks.

In this paper, we address the problem of privacy-preserving of the consumer's energy measurements in the context of the SG. To this end, we present a blockchain-based approach to preserve the privacy for smart grid users and to detect data forgery, replay attacks, and data injection attacks.

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.

Regular expression denial of service (ReDoS)— which exploits the super-linear running time of matching regular expressions against carefully crafted inputs—is an emerging class of DoS attacks to web services. One challenging question for a victim web service under ReDoS attacks is how to quickly recover its normal operation after ReDoS attacks, especially these zero-day ones exploiting previously unknown vulnerabilities.In this paper, we present RegexNet, the first payload-based, automated, reactive ReDoS recovery system for web services. RegexNet adopts a learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming requests including the request contents and database query responses. If detected as a cause leading to ReDoS, RegexNet migrates those requests to a sandbox and isolates their execution for a fast, first-measure recovery.We have implemented a RegexNet prototype and integrated it with HAProxy and Node.js. Evaluation results show that RegexNet is effective in recovering the performance of web services against zero-day ReDoS attacks, responsive on reacting to attacks in sub-minute, and resilient to different ReDoS attack types including adaptive ones that are designed to evade RegexNet on purpose.

Fog Computing was envisioned to solve problems like high latency, mobility, bandwidth, etc. that were introduced by Cloud Computing. Fog Computing has enabled remotely connected IoT devices and sensors to be managed efficiently. Nonetheless, the Fog-Cloud paradigm suffers from various security and privacy related problems. Blockchain ensures security in a trustless way and therefore its applications in various fields are increasing rapidly. In this work, we propose a Fog-Cloud architecture that enables Blockchain to ensure security, scalability, and privacy of remotely connected IoT devices. Furthermore, our proposed architecture also efficiently manages common problems like ever-increasing latency and energy consumption that comes with the integration of Blockchain in Fog-Cloud architecture.

Integrity verification of cloud data is of great importance for secure and effective cloud storage since attackers can change the data even though it is encrypted. Traditional integrity verification schemes only let the client know the integrity status of the remote data. When the data is corrupted, the system cannot hold the server accountable. Besides, almost all existing schemes assume that the users are credible. Instead, especially in a dynamic operation environment, users can deny their behaviors, and let the server bear the penalty of data loss. To address the issues above, we propose an accountable dynamic storage integrity verification (ADS-IV) scheme which provides means to detect or eliminate misbehavior of all participants. In the meanwhile, we modify the Invertible Bloom Filter (IBF) to recover the corrupted data and use the Mahalanobis distance to calculate the degree of damage. We prove that our scheme is secure under Computational Diffie-Hellman (CDH) assumption and Discrete Logarithm (DL) assumption and that the audit process is privacy-preserving. The experimental results demonstrate that the computational complexity of the audit is constant; the storage overhead is \$O(\textbackslashtextbackslashsqrt n )\$, which is only 1/400 of the size of the original data; and the whole communication overhead is O(1).As a result, the proposed scheme is not only suitable for large-scale cloud data storage systems, but also for systems with sensitive data, such as banking systems, medical systems, and so on.

Intelligent gadgets for example smartphones, tablet phones, and personal digital assistants play an increasingly important part in our lives and have become indispensable in our everyday routines. As a result, the market for mobile apps tends to grow at a rapid rate, and mobile app utilization has long eclipsed that of desktop software. The applications based on these smartphones are becoming vulnerable due to the use of open-source operating systems in these smart devices. These applications are vulnerable to smartphones because of memory leaks; they can steal personal data, hack our smartphones, and monitor our private activity, giving anyone significant financial loss. Because of these issues, smartphone security plays a vital role in our daily lives. The Play Store contains unrated applications which any unprofessional developer can develop, and these applications do not pass through the rigorous process of testing and analysis of code leaks. The existing developed system does not include a stringent procedure to examine and investigate source code to detect such vulnerabilities among mobile applications. This paper presented a dynamic analysis-based robust system for Source Code Analysis of Mobile Applications for Privacy Leaks using a machine learning algorithm. Furthermore, our framework is called Source Code Analysis of Mobile Applications (SCA-MA), which combines DynaLog and our machine learning-based classifier for Source Code Analysis of Mobile Applications. Our dataset will contain around 20000 applications to test and analyze vulnerabilities. We will perform dynamic analysis and separate the classification of vulnerable applications and safe applications. Our results show that we can detect vulnerabilities through our proposed system while reviewing code and provide better results than other existing frameworks. We have evaluated our large dataset with the pervasive way so we can detect even small privacy leak which can harm our app. Finally, we have compared results with existing methods, and framework performance is better than other methods.

Zero- Knowledge Proof is a cryptographic protocol exercised to render privacy and data security by securing the identity of users and using services anonymously. It finds numerous applications; authentication is one of them. A Zero-Knowledge Proof-based authentication system is discussed in this paper. Advanced Encryption Standard (AES) and Secure Remote Password (SRP) protocol have been used to design and build the ZKP based authentication system. SRP is a broadly used Password Authenticated Key Exchange (PAKE) protocol. The proposed method overcomes several drawbacks of traditional and commonly used authentication systems such as a simple username and plaintext password-based system, multi-factor authentication system and others.

The Session Initiation Protocol (SIP) is the principal signalling protocol in Voice over IP (VoIP) systems, responsible for initialising, terminating, and maintaining sessions amongst call parties. However, the problem with the SIP protocol is that it was not designed to be secure by nature as the HTTP digest authentication used in SIP is insecure, making it vulnerable to a variety of attacks. The current solutions rely on several standardised encryption protocols, such as TLS and IPsec, to protect SIP registration messages. However, the current centralised solutions do not scale well and cause algorithm overload when encoding and decoding SIP messages. In trying to rectify this issue, we propose in this paper a blockchain-based lightweight authentication mechanism, which involves a decentralised identity model to authenticate the SIP client to the SIP server. Our mechanism uses a smart contract on the Ethereum blockchain to ensure trust, accountability and preserves user privacy. We provided a proof-of-concept implementation to demonstrate our work. Further analysis of this approach's usability, mainly CPU and memory usage, was conducted comparing to IPsec and TLS. Then we discussed our system's security and presented a security analysis. Our analysis proves that our approach satisfies the SIP protocol security requirements.