Biblio

Session management is a fundamental component of web applications: despite the apparent simplicity, correctly implementing web sessions is extremely tricky, as witnessed by the large number of existing attacks. This motivated the design of formal methods to rigorously reason about web session security which, however, are not supported at present by suitable automated verification techniques. In this paper we introduce the first security type system that enforces session security on a core model of web applications, focusing in particular on server-side code. We showcase the expressiveness of our type system by analyzing the session management logic of HotCRP, Moodle, and phpMyAdmin, unveiling novel security flaws that have been acknowledged by software developers.

Latest forecasts show that 50 billion devices will be connected to the Internet by 2020. These devices will provide ubiquitous data access and enable smarter interactions in all aspects of our everyday life, including vital domains such as healthcare and battlefields, where privacy is a key requirement. With the increasing adoption of IoT and the explosion of these resource-constrained devices, manual discovery and configuration become significantly challenging. Despite there is a number of resource discovery protocols that can be efficiently used in IoT deployments, none of these protocols provides any privacy consideration. This paper presents LPA-SDT, a novel technique for service discovery that builds privacy into the design from the ground up. Performance evaluation demonstrates that LPA-SDT outperforms state-of-the-art discovery techniques for resource-constrained environments while preserving user and data privacy.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

Formal languages are ubiquitous wherever software systems need to exchange or store data. Unparsing into and parsing from such languages is an error-prone process that has spawned an entire class of security vulnerabilities. There has been ample research into finding vulnerabilities on the parser side, but outside of language specific approaches, few techniques targeting unparser vulnerabilities exist. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software.

With the rapid technological advancement of the universal internet of vehicles (UIoV), it becomes crucial to ensure safe and secure communication over the network, in an effort to achieve the implementation objective of UIoV effectively. A UIoV is characterized by highly dynamic topology, scalability, and thus vulnerable to various types of security and privacy attacks (i.e., replay attack, impersonation attack, man-in-middle attack, non-repudiation, and modification). Since the components of UIoV are constrained by numerous factors (e.g., low memory devices, low power), which makes UIoV highly susceptible. Therefore, existing schemes to address the privacy and security facets of UIoV exhibit an enormous scope of improvement in terms of time complexity and efficiency. This paper presents a lightweight authentication and batch verification scheme (LABVS) for UIoV using a bilinear map and cryptographic operations (i.e., one-way hash function, concatenation, XOR) to minimize the rate of message loss occurred due to delay in response time as in single message verification scheme. Subsequently, the scheme results in a high level of security and privacy. Moreover, the performance analysis substantiates that LABVS minimizes the computational delay and has better performance in the delay-sensitive network in terms of security and privacy as compared to the existing schemes.

Today, Linux is one of the main operating systems (OS) used both on desktop computers and various mobile devices. This OS is also widely applied in state and municipal structures, including law enforcement agencies and automated control systems used in the Armed Forces of the Russian Federation. It's worth noting that the process of replacing the Linux OS with domestic protected OSs that use the Linux kernel has now begun. In this regard, the analysis of threats to information security of the Linux OS is highly relevant. In this article, the authors discuss the security problems of Linux OS associated with unauthorized user privileges increase, as a result of which an attacker can gain full control over the OS. The approaches to differentiating user privileges in Linux are analyzed and their advantages and disadvantages are considered. As an example, the causes of the vulnerability CVE-2018-14665 were identified and measures to eliminate it were proposed.

With the development of augmented reality(AR) technology and location-based service (LBS) technology, combining AR with LBS will create a new way of life and socializing. In AR, users may consider the privacy and security of data. In LBS, the leakage of user location privacy is an important threat to LBS users. Therefore, it is very important for privacy management of positioning information and user location privacy to avoid loopholes and abuse. In this review, the concepts and principles of AR technology and LBS would be introduced. The existing privacy measurement and privacy protection framework would be analyzed and summarized. Also future research direction of location privacy protection would be discussed.

A continually growing number of sensors is required for monitoring industrial processes and for continuous data acquisition from industrial plants and devices. The cabling of sensors represent a considerable effort and potential source of error, which can be avoided by using wireless sensor nodes. These wireless sensor nodes form a wireless sensor network (WSN) to efficiently transmit data to the destination. For the acceptance of WSNs in industry, it is important to build up networks with high trustworthiness. The trustworthiness of the WSN depends not only on a secure wireless communication but also on the ability to detect modifications at the wireless sensor nodes itself. This paper presents the enhancement of the WSN's trustworthiness using an optical localization system. It can be used for the preparation phase of the WSN and also during operation to track the positions of the wireless sensor nodes and detect spatial modification. The location information of the sensor nodes can also be used to rate their trustworthiness.

The evolving of context-aware applications are becoming more readily available as a major driver of the growth of future connected smart, autonomous environments. However, with the increasing of security risks in critical shared massive data capabilities and the increasing regulation requirements on privacy, there is a significant need for new paradigms to manage security and privacy compliances. These challenges call for context-aware and fine-grained security policies to be enforced in such dynamic environments in order to achieve efficient real-time authorization between applications and connected devices. We propose in this work a novel solution that aims to provide context-aware security model for Android applications. Specifically, our proposition provides automated context-aware access control model and leverages Attribute-Based Encryption (ABE) to secure data communications. Thorough experiments have been performed and the evaluation results demonstrate that the proposed solution provides an effective lightweight adaptable context-aware encryption model.

As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach.

Local flexibility markets are a concept to integrate distributed flexibilities such as power-to-gas, power-to-heat systems, electric vehicles, and battery storage systems into congestion management of distribution and transmission grids. However, the activation of the flexibility of storage systems changes their state-of-charge and thus reduces their available flexibility. Counter-trading or compensation of activated flexibility in later points of time lets storage operators regain flexibility. However, this compensation might create or amplify congestions when not supervised by system operators. Therefore, we propose the inclusion of compensation within the flexibility market clearing process by system operators. We further develop a linear formulation of flexibility market bids by storage systems that takes the need for compensation into account. For this, first, the operational planning formulation of a storage system is expanded by flexibility market participation. Subsequently, the linear formulation of bids is derived and demonstrated.

Multi-party and multi-layer nature of 5G networks implies the inherent distribution of management and orchestration decisions across multiple entities. Therefore, responsibility for management decisions concerning end-to-end services become blurred if no efficient liability and accountability mechanism is used. In this paper, we present the design, building blocks and challenges of a Liability-Aware Security Management (LASM) system for 5G. We describe how existing security concepts such as manifests and Security-by-Contract, root cause analysis, remote attestation, proof of transit, and trust and reputation models can be composed and enhanced to take risk and responsibilities into account for security and liability management.

With the development of 5G communication technology, the status of the Internet of Vehicles in people's lives is greatly improved in the general trend of intelligent transportation. The combination of vehicles and Radio Frequency Identification (RFID) makes the application prospects of vehicle networking gradually expand. However, the wireless network of the Internet of Vehicles is open and mobile, so it can be easily stolen or tampered with by attackers. Moreover, it will cause serious traffic security problems and even threat people's lives. In this paper, we propose a lightweight authentication protocol for the Internet of Vehicles based on a mobile RFID system and give corresponding security requirements for modeling potential attacks. The protocol is based on the three-party mutual authentication, and uses bit-operated left-cycle shift operations and hetero-oriented operations to generate encrypted data. The simultaneous inclusion of triparty shared key information and random numbers makes the protocol resistant to counterfeit attacks, violent attacks, replay attacks and desynchronization attacks. Finally, a simulation analysis of the security protocol using the ProVerif tool shows that the protocol secures is not accessible to attackers during the data transfer, and achieve the three-party authentication between sensor nodes (SN), vehicle nodes (Veh) and backend servers.

A rapid rise in cyber-attacks on Cyber Physical Systems (CPS) has been observed in the last decade. It becomes even more concerning that several of these attacks were on critical infrastructures that indeed succeeded and resulted into significant physical and financial damages. Experimental testbeds capable of providing flexible, scalable and interoperable platform for executing various cybersecurity experiments is highly in need by all stakeholders. A container-based SCADA testbed is presented in this work as a potential platform for executing cybersecurity experiments. Through this testbed, a network traffic containing ARP spoofing is generated that represents a Man in the middle (MITM) attack. While doing so, scanning of different systems within the network is performed which represents a reconnaissance attack. The network traffic generated by both ARP spoofing and network scanning are captured and further used for preparing a dataset. The dataset is utilized for training a network classification model through a machine learning algorithm. Performance of the trained model is evaluated through a series of tests where promising results are obtained.

This paper focuses on the issue of designing L2 state feedback controller for networked control systems subject to unknown periodic denial-of-service (DoS) jamming attacks. Primarily, a resilient event-triggering mechanism is introduced to counteract the influence of DoS jamming attacks. Secondly, a switching system model of NCSs is set up. Then, the criteria of the exponential stability analysis is obtained by the piecewise Lyapunov functional approach based on the model. Thirdly, a co-design approach of the trigger parameters and L2 controller is developed. Lastly, a practical system is used for proving the efficiency of the proposed approach.

Deterministic latency is the key challenge that must be addressed in numerous 5G applications such as AR/VR. However, it is difficult to make customized end-to-end resource reservation across multiple ISPs using IP-based QoS mechanisms. Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to its in-network caching and native multicast capabilities, and the deterministic latency can promisingly be guaranteed by caching the relevant content objects in appropriate locations. Existing proposals formulate the ICN cache placement problem into numerous theoretical models. However, the underlying mechanisms to support such cache coordination are not discussed in detail. Especially, how to efficiently make cache reservation, how to avoid route oscillation when content cache is updated and how to conduct the real-time latency measurement? In this work, we propose Latency Deterministic Information-Centric Networking (LD-ICN). LD-ICN relies on source routing-based latency telemetry and leverages an on-path caching technique to avoid frequent route oscillation while still achieve the optimal cache placement under the SDN architecture. Extensive evaluation shows that under LD-ICN, 90.04% of the content requests are satisfied within the hard latency requirements.

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.

Understanding the IP address depletion and the importance of handling security issues in IPv6 deployment can make IT personnel becomes more functional and helpful to the organization. It also applied to the management people who are responsible for approving the budget or organization policy related to network security. Unfortunately, new employees or fresh graduates may not really understand the challenge related to IPv6 deployment. In order to be equipped with appropriate knowledge and skills, these people may require a few weeks of attending workshops or training. Thus, of course involving some implementation cost as well as sacrificing allocated working hours. As an alternative to save cost and to help new IT personnel become quickly educated and familiar with IPv6 deployment issues, this paper presented a learning kit that has been designed to include self-learning features that can help neophytes to learn about IPv6 at their own pace. The kit contains some compact notes, brief security model and framework as well as a guided module with supporting quizzes to maintain a better understanding of the topics. Since IPv6 is still in the early phase of implementation in most of developing countries, this kit can be an additional assisting tool to accelerate the deployment of IPv6 environment in any organization. The kit also can be used by teachers and trainers as a supporting tool in the classroom. The pre-alpha testing has attracted some potential users and the findings proved their acceptance. The kit has prospective to be further enhanced and commercialized.

Underwater sensor networks (UWSN) are a division of classical wireless sensor networks (WSN), which are designed to accomplish both military and civil operations, such as invasion detection and underwater life monitoring. Underwater sensor nodes operate using the energy provided by integrated limited batteries, and it is a serious challenge to replace the battery under the water especially in harsh conditions with a high number of sensor nodes. Here, energy efficiency confronts as a very important issue. Besides energy efficiency, data privacy is another essential topic since UWSN typically generate delicate sensing data. UWSN can be vulnerable to silent positioning and listening, which is injecting similar adversary nodes into close locations to the network to sniff transmitted data. In this paper, we discuss the usage of compressive sensing (CS) and energy harvesting (EH) to improve the lifetime of the network whilst we suggest a novel encryption decision method to maintain privacy of UWSN. We also deploy a Mixed Integer Programming (MIP) model to optimize the encryption decision cases which leads to an improved network lifetime.

The sheer size of IoT networks being deployed today presents an "attack surface" and poses significant security risks at a scale never before encountered. In other words, a single device/node in a network that becomes infected with malware has the potential to spread malware across the network, eventually ceasing the network functionality. Simply detecting and quarantining the malware in IoT networks does not guarantee to prevent malware propagation. On the other hand, use of traditional control theory for malware confinement is not effective, as most of the existing works do not consider real-time malware control strategies that can be implemented using uncertain infection information of the nodes in the network or have the containment problem decoupled from network performance. In this work, we propose a two-pronged approach, where a runtime malware detector (HaRM) that employs Hardware Performance Counter (HPC) values to detect the malware and benign applications is devised. This information is fed during runtime to a stochastic model predictive controller to confine the malware propagation without hampering the network performance. With the proposed solution, a runtime malware detection accuracy of 92.21% with a runtime of 10ns is achieved, which is an order of magnitude faster than existing malware detection solutions. Synthesizing this output with the model predictive containment strategy lead to achieving an average network throughput of nearly 200% of that of IoT networks without any embedded defense.

Searchable encryption technology can guarantee the confidentiality of cloud data and the searchability of ciphertext data, which has a very broad application prospect in cloud storage environments. However, most existing searchable encryption schemes have problems, such as excessive computational overhead and low security. In order to solve these problems, a lightweight searchable encryption scheme based on certificateless cryptosystem is proposed. The user's final private key consists of partial private key and secret value, which effectively solves the certificate management problem of the traditional cryptosystem and the key escrow problem of identity-based cryptosystem. At the same time, the introduction of third-party manager has significantly reduced the burden in the cloud server and achieved lightweight multi-user ciphertext retrieval. In addition, the data owner stores the file index in the third-party manager, while the file ciphertext is stored in the cloud server. This ensures that the file index is not known by the cloud server. The analysis results show that the scheme satisfies trapdoor indistinguishability and can resist keyword guessing attacks. Compared with similar certificateless encryption schemes, it has higher computational performance in key generation, keyword encryption, trapdoor generation and keyword search.

The Internet of Things (IoT), smart sensors and mobile wearable devices are helping to provide services that are more ubiquitous, smarter, faster and easily accessible to users. However, security is a significant concern for the IoT, with access control and identity management are being two major issues. With the growing size and presence of these systems and the resource constrained nature of the IoT devices, an important question is how to manage policies in a manner that is both scalable and flexible. In this research, we aim at proposing a fine-grained and flexible access control architecture, and to examine an identity model for constrained IoT resources. To achieve this, first, we outline some key limitations in the state of the art access control and identity management for IoT. Then we devise our approach to address those limitations in a systematic way.

Edge caching has received much attention as a promising technique to overcome the stringent latency and data hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit simultaneously. In this paper, we study a cache-aided FD system via delivery time analysis and optimization. In the considered system, an edge node (EN) operates in FD mode and serves users via wireless channels. Two optimization problems are formulated to minimize the largest delivery time based on the two popular linear beamforming zero-forcing and minimum mean square error designs. Since the formulated problems are non-convex due to the self-interference at the EN, we propose two iterative optimization algorithms based on the inner approximation method. The convergence of the proposed iterative algorithms is analytically guaranteed. Finally, the impacts of caching and the advantages of the FD system over the half-duplex (HD) counterpart are demonstrated via numerical results.

Nearest neighbor search (NNS) is one of the current popular research directions, which widely used in machine learning, pattern recognition, image detection and so on. In the low dimension data, based on tree search method can get good results. But when the data dimension goes up, that will produce a curse of dimensional. The proposed Locality-Sensitive Hashing algorithm (LSH) greatly improves the efficiency of nearest neighbor query for high dimensional data. But the algorithm relies on the building a large number of hash table, which makes the space complexity very high. C2LSH based on dynamic collision improves the disadvantage of LSH, but its disadvantage is that it needs to detect the collision times of a large number of data points which Increased query time. Therefore, Based on LSH algorithm, later researchers put forward many improved algorithms, but still not ideal.In this paper, we put forward Locality-Sensitive Hashing Scheme Based on Heap Sort of Hash Bucket (HSLSH) algorithm aiming at the shortcomings of LSH and C2LSH. Its main idea is to take advantage of the efficiency of heapsort in massive data sorting to improve the efficiency of nearest neighbor query. It only needs to rely on a small number of hash functions can not only overcome the shortcoming of LSH need to build a large number of hash table, and avoids defects of C2LSH. Experiments show that our algorithm is more than 20% better than C2LSH in query accuracy and 40% percent lower in query time.

Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.