Biblio

Physical layer (PHY) security in decode-and-forward (DF) relay systems is discussed. Based on the types of wiretap links, the secrecy performance of three typical secure DF relay models is analyzed. Different from conventional works in this field, rigorous derivations of the secrecy channel capacity are provided from an information-theoretic perspective. Meanwhile, closed-form expressions are derived to characterize the secrecy outage probability (SOP). For the sake of unveiling more system insights, asymptotic analyses are performed on the SOP for a sufficiently large signal-to-noise ratio (SNR). The analytical results are validated by computer simulations and are in excellent agreement.

Computer and Vehicular networks, both are prone to multiple information security breaches because of many reasons like lack of standard protocols for secure communication and authentication. Distributed Denial of Service (DDoS) is a threat that disrupts the communication in networks. Detection and prevention of DDoS attacks with accuracy is a necessity to make networks safe.In this paper, we have experimented two machine learning-based techniques one each for attack detection and attack prevention. These detection & prevention techniques are implemented in different environments including vehicular network environments and computer network environments. Three different datasets connected to heterogeneous environments are adopted for experimentation. The first dataset is the NSL-KDD dataset based on the traffic of the computer network. The second dataset is based on a simulation-based vehicular environment, and the third CIC-DDoS 2019 dataset is a computer network-based dataset. These datasets contain different number of attributes and instances of network traffic. For the purpose of attack detection AdaBoostM1 classification algorithm is used in WEKA and for attack prevention Logit Model is used in STATA. Results show that an accuracy of more than 99.9% is obtained from the simulation-based vehicular dataset. This is the highest accuracy rate among the three datasets and it is obtained within a very short period of time i.e., 0.5 seconds. In the same way, we use a Logit regression-based model to classify packets. This model shows an accuracy of 100%.

With the rapid development of general cloud services, more and more individuals or collectives use cloud platforms to store data. Assured data deletion deserves investigation in cloud storage. In time-sensitive data storage scenarios, it is necessary for cloud platforms to automatically destroy data after the data owner-specified expiration time. Therefore, assured time-sensitive data deletion should be sought. In this paper, a fine-grained assured time-sensitive data deletion (ATDD) scheme in cloud storage is proposed by embedding the time trapdoor in Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Time-sensitive data is self-destructed after the data owner-specified expiration time so that the authorized users cannot get access to the related data. In addition, a credential is returned to the data owner for data deletion verification. This proposed scheme provides solutions for fine-grained access control and verifiable data self-destruction. Detailed security and performance analysis demonstrate the security and the practicability of the proposed scheme.

The traditional ciphertext-policy attribute-based encryption (CP-ABE) has the problems of poor security of key distribution by a single attribute authorization center and too much calculation on the client in the process of encryption and decryption. A CP-ABE scheme that can outsource encryption and decryption and support multi-authorization centers is introduced to solve the above two problems. In the key generation stage, the user's private key is generated by the attribute authorization center and the key generation center jointly executing the two-party secure computing protocol; In the encryption and decryption stage, the cloud encryption server and cloud storage server are used to handle most of the computing work. Security proof and performance analysis show that the scheme not only can effectively make up for the defect of all key leakage when the attribute authorization center is broken, but also can enhance the security of the system; Moreover, after using the cloud server to process data, users only need to perform a simple calculation on the client to complete encryption or decryption, thus reducing the user's computing workload.

Java locking is an essential functionality and tool in the development of applications and systems, and this is mainly because several modules may run in a synchronized way inside an application and these modules need a good coordination manner in order for them to run properly and in order to make the whole application or system stable and normal. As such, this paper focuses on comparing various Java locking mechanisms in order to achieve a better understanding of how these locks work and how to conduct a proper locking mechanism. The comparison of locks is made according to CPU usage, memory consumption, and ease of implementation indicators, with the aim of providing guidance to developers in choosing locks for different scenarios. For example, if the Pessimistic Locks are used in any program execution environment, i.e., whenever a thread obtains resources, it needs to obtain the lock first, which can ensure a certain level of data security. However, it will bring great CPU overhead and reduce efficiency. Also, different locks have different memory consumption, and developers are sometimes faced with the need to choose locks rationally with limited memory, or they will cause a series of memory problems. In particular, the comparison of Java locks is able to lead to a systematic classification of these locks and can help improve the understanding of the taxonomy logic of the Java locks.

This paper investigates the physical layer security of a cognitive radio (CR) non-orthogonal multiple-access (NOMA) network supported by an intelligent reflecting surface (IRS). In a CR network, a secondary base station (BS) serves a couple of users, i.e., near and far users, via NOMA transmission under eavesdropping from a malicious attacker. It is assumed that the direct transmission link from the BS and far user is absent due to obstacles. Thus, an IRS is utilized to support far user communication, however, the communication links between the IRS and near/primary users are neglected because of heavy attenuation. The exact secrecy outage probability (SOP) for the near user and approximate SOP for the far user are then derived in closed-form by using the Gauss-Chebyshev approach. The accuracy of the derived analytical SOP is then verified through Monte Carlo simulations. The simulation results also provide useful insights on the impacts of the number of IRS reflecting elements and limited interference temperature on the system SOP.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

There is an increase in interest and necessity for an interoperable and efficient railway network across Europe, creating a key distribution problem between train and trackside entities’ key management centres (KMC). Train and trackside entities establish a secure session using symmetric keys (KMAC) loaded beforehand by their respective KMC using procedures that are not scalable and prone to operational mistakes. A single system would simplify the KMAC distribution between KMCs; nevertheless, it is difficult to place the responsibility for such a system for the whole European area within one central organization. A single system could also expose relationships between KMCs, revealing information, such as plans to use an alternative route or serve a new region, jeopardizing competitive advantage. This paper proposes a scalable and decentralised key management system that allows KMC to share cryptographic keys using transactions while keeping relationships anonymous. Using non-interactive proofs of knowledge and assigning each entity a private and public key, private key owners can issue valid transactions while all system actors can validate them. Our performance analysis shows that the proposed system is scalable when a proof of concept is implemented with settings close to the expected railway landscape in 2030.

The secure Internet of Things (loT) increasingly relies on digital cryptographic signatures which require a private signature and public verification key. By their intrinsic nature, public keys are meant to be accessible to any interested party willing to verify a given signature. Thus, the storing of such keys is of great concern, since an adversary shall not be able to tamper with the public keys, e.g., on a local filesystem. Commonly used public-key infrastructures (PKIs), which handle the key distribution and storage, are not feasible in most use-cases, due to their resource intensity and high complexity. Thus, the general storing of the public verification keys is of notable interest for low-resource loT networks. By using the Distributed Ledger Technology (DLT), this paper proposes a decentralized concept for storing public signature verification keys in a tamper-resistant, secure, and resilient manner. By combining lightweight public-key exchange protocols with the proposed approach, the storing of verification keys becomes scalable and especially suitable for low-resource loT devices. This paper provides a Proof-of-Concept implementation of the DLT public-key store by extending our previously proposed NFC-Key Exchange (NFC-KE) protocol with a decentralized Hyperledger Fabric public-key store. The provided performance analysis shows that by using the decentralized keystore, the NFC- KE protocol gains an increased tamper resistance and overall system resilience while also showing expected performance degradations with a low real-world impact.

The increasing use of Information Technology applications in the distributed environment is increasing security exploits. Information about vulnerabilities is also available on the open web in an unstructured format that developers can take advantage of to fix vulnerabilities in their IT applications. SQL injection (SQLi) attacks are frequently launched with the objective of exfiltration of data typically through targeting the back-end server organisations to compromise their customer databases. There have been a number of high profile attacks against large enterprises in recent years. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP. Various machine learning and deep learning algorithms have been applied to detect and prevent these attacks. However, such preventive attempts have not limited the incidence of cyber-attacks and the resulting compromised database as reported by (CVE) repository. In this paper, the potential of using data mining approaches is pursued in order to enhance the efficacy of SQL injection safeguarding measures by reducing the false-positive rates in SQLi detection. The proposed approach uses CountVectorizer to extract features and then apply various supervised machine-learning models to automate the classification of SQLi. The model that returns the highest accuracy has been chosen among available models. Also a new model has been created PALOSDM (Performance analysis and Iterative optimisation of the SQLI Detection Model) for reducing false-positive rate and false-negative rate. The detection rate accuracy has also been improved significantly from a baseline of 94% up to 99%.

Simulations have gained paramount importance in terms of software development for wireless sensor networks and have been a vital focus of the scientific community in this decade to provide efficient, secure, and safe communication in smart cities. Network Simulators are widely used for the development of safe and secure communication architectures in smart city. Therefore, in this technical survey report, we have conducted experimental comparisons among ten different simulation environments that can be used to simulate smart-city operations. We comprehensively analyze and compare simulators COOJA, NS-2 with framework Mannasim, NS-3, OMNeT++ with framework Castalia, WSNet, TOSSIM, J-Sim, GloMoSim, SENSE, and Avrora. These simulators have been run eight times each and comparison among them is critically scrutinized. The main objective behind this research paper is to assist developers and researchers in selecting the appropriate simulator against the scenario to provide safe and secure wired and wireless networks. In addition, we have discussed the supportive simulation environments, functions, and operating modes, wireless channel models, energy consumption models, physical, MAC, and network-layer protocols in detail. The selection of these simulation frameworks is based on features, literature, and important characteristics. Lastly, we conclude our work by providing a detailed comparison and describing the pros and cons of each simulator.

As the voucher for identity, digital certificates and the public key infrastructure (PKI) system have always played a vital role to provide the authentication services. In recent years, with the increase in attacks on traditional centralized PKIs and the extensive deployment of blockchains, researchers have tried to establish blockchain-based secure decentralized PKIs and have made significant progress. Although blockchain enhances security, it brings new problems in scalability due to the inherent limitations of blockchain’s data structure and consensus mechanism, which become much severe for the massive access in the era of 5G and B5G. In this paper, we propose ScalaCert to mitigate the scalability problems of blockchain-based PKIs by utilizing redactable blockchain for "on-cert" revocation. Specifically, we utilize the redactable blockchain to record revocation information directly on the original certificate ("on-cert") and remove additional data structures such as CRL, significantly reducing storage overhead. Moreover, the combination of redactable and consortium blockchains brings a new kind of attack called deception of versions (DoV) attack. To defend against it, we design a random-block-node-check (RBNC) based freshness check mechanism. Security and performance analyses show that ScalaCert has sufficient security and effectively solves the scalability problem of the blockchain-based PKI system.

The cloud provides storage for users to share their files in the cloud. Nowadays some shared data auditing schemes are proposed for protecting data integrity. However, preserving the identity privacy of group users and secure user revocation usually result in high computational overhead. Then a shared data auditing scheme supporting identity privacy preserving is proposed that enables users to be effectively revoked. To preserve identity privacy during the audit process, we develop an efficient authenticator generation mechanism that enables public auditing. Our solution supports efficient user revocation, where the authenticator of the revoked user does not need to be regenerated and integrity checking can be performed appropriately. At the same time, the group manager maintains two tables to ensure user traceability. When the user updates data, two tables are modified and updated by the group manager promptly. It shows that our scheme is secure by security analysis. Moreover, concrete experiments prove the performance of the system.

Food supply chain is a complex but necessary food production arrangement needed by the global community to maintain sustainability and food security. For the past few years, entities being a part of the food processing system have usually taken food supply chain for granted, they forget that just one disturbance in the chain can lead to poisoning, scarcity, or increased prices. This continually affects the vulnerable among society, including impoverished individuals and small restaurants/grocers. The food supply chain has been expanded across the globe involving many more entities, making the supply chain longer and more problematic making the traditional logistics pattern unable to match the expectations of customers. Food supply chains involve many challenges like lack of traceability and communication, supply of fraudulent food products and failure in monitoring warehouses. Therefore there is a need for a system that ensures authentic information about the product, a reliable trading mechanism. In this paper, we have proposed a comprehensive solution to make the supply chain consumer centric by using Blockchain. Blockchain technology in the food industry applies in a mindful and holistic manner to verify and certify the quality of food products by presenting authentic information about the products from the initial stages. The problem formulation, simulation and performance analysis are also discussed in this research work.

This paper addresses the issues in managing group key among clusters in Mobile Ad hoc Networks (MANETs). With the dynamic movement of the nodes, providing secure communication and managing secret keys in MANET is difficult to achieve. In this paper, we propose a distributed secure broadcast stateless groupkey management framework (DSBS-GKM) for efficient group key management. This scheme combines the benefits of hash function and Lagrange interpolation polynomial in managing MANET nodes. To provide a strong security mechanism, a revocation system that detects and revokes misbehaviour nodes is presented. The simulation results show that the proposed DSBS-GKM scheme attains betterments in terms of rekeying and revocation performance while comparing with other existing key management schemes.

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the software to a benign state. This manuscript presents CARE - the first secure boot framework that provides malicious code modification attack detection, resilience, and onboard recovery mechanism for the compromised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the integrity and authenticity of the software and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to provide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides promising resilience and recovery mechanism with very little (8%) performance and resource overhead.

We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.

Internet of Things (IoT) is a sophisticated concept of the traditional internet. In IoT, all things in our lives can be connected with the internet or with each other to exchange data and perform specific functions through the network. However, combining several devices-especially by unskilled users-may pose a number of security risks. In addition, some commonly used communication protocols in the IoT area are not secure. Security, on the other hand, increases overhead by definition, resulting in performance degradation. The Message Queuing Telemetry Transport (MQTT) protocol is a lightweight protocol and can be considered as one of the most popular IoT protocols, it is a publish/subscribe messaging transport protocol that uses a client-server architecture. MQTT is built to run over TCP protocol, thus it does not provide any level of security by default. Therefore, Transport Layer Security (TLS) can be used to ensure the security of the MQTT protocol. This paper analyzed the impact on the performance and security of the MQTT protocol in two cases. The first case, when using TLS protocol to support the security of the MQTT protocol. The second case, using the traditional MQTT without providing any level of security for the exchanged data. The results indicated that there is a tradeoff between the performance and the security when using MQTT protocol with and without the presence of TLS protocol.

A modular static analysis decomposes a program's analysis into analyses of its parts, or components. An intercomponent analysis instructs an intra-component analysis to analyse each component independently of the others. Additional analyses are scheduled for newly discovered components, and for dependent components that need to account for newly discovered component information. Modular static analyses are scalable, can be tuned to a high precision, and support the analysis of programs that are highly dynamic, featuring e.g., higher-order functions or dynamically allocated processes.In this paper, we present the engineering aspects of MAF, a static analysis framework for implementing modular analyses for higher-order languages. For any such modular analysis, the framework provides a reusable inter-component analysis and it suffices to implement its intra-component analysis. The intracomponent analysis can be composed from several interdependent and reusable Scala traits. This design facilitates changing the analysed language, as well as the analysis precision with minimal effort. We illustrate the use of MAF through its instantiation for several different analyses of Scheme programs.

With the popularization of mobile communication and sensing equipment, as well as the rapid development of location-aware technology and wireless communication technology, LBSs(Location-based services) bring convenience to people’s lives and enable people to arrange activities more efficiently and reasonably. It can provide more flexible LBS proximity detection query, which has attracted widespread attention in recent years. However, the development of proximity detection query still faces many severe challenges including query information privacy. For example, when users want to ensure their location privacy and data security, they can get more secure location-based services. In this article, we propose an efficient and privacy-protecting proximity detection framework based on location services: PD(Proximity Detection). Through PD, users can query the range of arbitrary polygons and obtain accurate LBS results. Specifically, based on homomorphic encryption technology, an efficient PRQ(polygon range query) algorithm is constructed. With the help of PRQ, PD, you can obtain accurate polygon range query results through the encryption request and the services provided by the LAS(LBS Agent Server) and the CS(Cloud Server). In addition, the query privacy of the queryer and the information of the data provider are protected. The correctness proof and performance analysis show that the scheme is safe and feasible. Therefore, our scheme is suitable for many practical applications.

Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizing differential privacy have frequently taken the form of programming languages and libraries. However, many existing programming languages designed for compositional verification of differential privacy impose significant burden on the programmer (in the form of complex type annotations). Supplementary library support for privacy analysis built on top of existing general-purpose languages has been more usable, but incapable of pervasive end-to-end enforcement of sensitivity analysis and privacy composition. We introduce DDuo, a dynamic analysis for enforcing differential privacy. DDuo is usable by non-experts: its analysis is automatic and it requires no additional type annotations. DDuo can be implemented as a library for existing programming languages; we present a reference implementation in Python which features moderate runtime overheads on realistic workloads. We include support for several data types, distance metrics and operations which are commonly used in modern machine learning programs. We also provide initial support for tracking the sensitivity of data transformations in popular Python libraries for data analysis. We formalize the novel core of the DDuo system and prove it sound for sensitivity analysis via a logical relation for metric preservation. We also illustrate DDuo's usability and flexibility through various case studies which implement state-of-the-art machine learning algorithms.

Power system security assessment and enhancement in grids with high penetration of renewables is critical for pragmatic power system planning. Static Security Assessment (SSA) is a fast response tool to assess system stability margins following considerable contingencies assuming post fault system reaches a steady state. This paper presents a contingency ranking methodology using static security indices to rank credible contingencies considering severity. A Modified IEEE 9 bus system integrating renewables was used to test the approach. The static security indices used independently provides accurate results in identifying severe contingencies but further assessment is needed to provide an accurate picture of static security assessment in an increased time frame of the steady state. The indices driven for static security assessment could accurately capture and rank contingencies with renewable sources but due to intermittency of the renewable source various contingency ranking lists are generated. This implies that using indices in future grids without consideration on intermittent nature of renewables will make it difficult for the grid operator to identify severe contingencies and assist the power system operator to make operational decisions. This makes it necessary to integrate the behaviour of renewables in security indices for practical application in real time security assessment.

Buffer overflow (BOF) vulnerability is one of the most dangerous security vulnerability which can be exploited by unwanted users. This vulnerability can be detected by both static and dynamic analysis techniques. For dynamic analysis, execution of the program is required in which the behavior of the program according to specifications is checked while in static analysis the source code is analyzed for security vulnerabilities without execution of code. Despite the fact that many open source and commercial security analysis tools employ static and dynamic methods but there is still a margin for improvement in BOF vulnerability detection capability of these tools. We propose an enhancement in Cppcheck tool for statically detecting BOF vulnerability using data flow analysis in C programs. We have used the Juliet Test Suite to test our approach. We selected two best tools cited in the literature for BOF detection (i.e. Frama-C and Splint) to compare the performance and accuracy of our approach. From the experiments, our proposed approach generated Youden Index of 0.45, Frama-C has only 0.1 Youden's score and Splint generated Youden score of -0.47. These results show that our technique performs better as compared to both Frama-C and Splint static analysis tools.

This paper for the first time investigate the security and reliability performance of ultra-reliable low-latency communication (URLLC) systems in the presence of randomly distributed eavesdroppers, where the impact of short blocklength codes and imperfect channel estimation are jointly considered. Based on the finite-blocklength information theory, we first derive a closed-form approximation of transmission error probability to describe the degree of reliability loss. Then, we also derive an asymptotic expression of intercept probability to characterize the security performance, where the impact of secrecy protected zone is also considered. Simulation and numerical results validate the accuracy of theoretical approximations, and illustrate the tradeoff between security and reliability. That is, the intercept probability of URLLC systems can be suppressed by loosening the reliability requirement, and vice versa. More importantly, the theoretical analysis and methodologies presented in this paper can offer some insights and design guidelines for supporting secure URLLC applications in the future 6G wireless networks.

Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]–[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUs and FPGAs, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.