Biblio

This Innovative Practice full paper describes a technical innovation for scalable teaching of cybersecurity hands-on classes using interactive learning environments. Hands-on experience significantly improves the practical skills of learners. However, the preparation and delivery of hands-on classes usually do not scale. Teaching even small groups of students requires a substantial effort to prepare the class environment and practical assignments. Further issues are associated with teaching large classes, providing feedback, and analyzing learning gains. We present our research effort and practical experience in designing and using learning environments that scale up hands-on cybersecurity classes. The environments support virtual networks with full-fledged operating systems and devices that emulate realworld systems. The classes are organized as simultaneous training sessions with cybersecurity assignments and learners' assessment. For big classes, with the goal of developing learners' skills and providing formative assessment, we run the environment locally, either in a computer lab or at learners' own desktops or laptops. For classes that exercise the developed skills and feature summative assessment, we use an on-premises cloud environment. Our approach is unique in supporting both types of deployment. The environment is described as code using open and standard formats, defining individual hosts and their networking, configuration of the hosts, and tasks that the students have to solve. The environment can be repeatedly created for different classes on a massive scale or for each student on-demand. Moreover, the approach enables learning analytics and educational data mining of learners' interactions with the environment. These analyses inform the instructor about the student's progress during the class and enable the learner to reflect on a finished training. Thanks to this, we can improve the student class experience and motivation for further learning. Using the presented environments KYPO Cyber Range Platform and Cyber Sandbox Creator, we delivered the classes on-site or remotely for various target groups of learners (K-12, university students, and professional learners). The learners value the realistic nature of the environments that enable exercising theoretical concepts and tools. The instructors value time-efficiency when preparing and deploying the hands-on activities. Engineering and computing educators can freely use our software, which we have released under an open-source license. We also provide detailed documentation and exemplary hands-on training to help other educators adopt our teaching innovations and enable sharing of reusable components within the community.

The IFDS algorithm can be memory-intensive, requiring a memory budget of more than 100 GB of RAM for some applications. The large memory requirements significantly restrict the deployment of IFDS-based tools in practise. To improve this, we propose a disk-assisted solution that drastically reduces the memory requirements of traditional IFDS solvers. Our solution saves memory by 1) recomputing instead of memorizing intermediate analysis data, and 2) swapping in-memory data to disk when memory usages reach a threshold. We implement sophisticated scheduling schemes to swap data between memory and disks efficiently. We have developed a new taint analysis tool, DiskDroid, based on our disk-assisted IFDS solver. Compared to FlowDroid, a state-of-the-art IFDS-based taint analysis tool, for a set of 19 apps which take from 10 to 128 GB of RAM by FlowDroid, DiskDroid can analyze them with less than 10GB of RAM at a slight performance improvement of 8.6%. In addition, for 21 apps requiring more than 128GB of RAM by FlowDroid, DiskDroid can analyze each app in 3 hours, under the same memory budget of 10GB. This makes the tool deployable to normal desktop environments. We make the tool publicly available at https://github.com/HaofLi/DiskDroid.

Since a training system using VR can reproduce an actual training environment, training systems have been studied in commercial fields such as medical care and construction. This immersive experience in a virtual space can have a great effect on learning a second language. In this paper, we propose an immersive learning system that learns phrases used in the customer service industry in the customer service experience. We asked the subjects to experience the system, measured the effects of learning, and evaluated the system. Evaluating the learning effect of phrases used in customer service English on 8 students, all student achieved good learning results. Besides, to evaluate the usability of the system, the VR system was evaluated by performing SSQ to measure VR sickness shows this system doesn't cause virtual sickness, SUS to measure usability shows this system evaluation is higher than average system, and IPQ to measure presence in an immersive space shows this system gives average virtual reality experience.

Due to its ability to detect many frequently occurring security vulnerabilities, taint analysis is one of the core static analyses used by many static application security testing (SAST) tools. Previous studies have identified issues that software developers face with SAST tools. This paper reports on our experience in building a configurable taint analysis tool, named SecuCheck, that runs in multiple integrated development environments. SecuCheck is built on top of multiple existing components and comes with a Java-internal domain-specific language fluentTQL for specifying taint-flows, designed for software developers. We evaluate the applicability of SecuCheck in detecting eleven taint-style vulnerabilities in microbench programs and three real-world Java applications with known vulnerabilities. Empirically, we identify factors that impact the runtime of SecuCheck.

Named Data Networking (NDN) is an emerging network architecture, which is built by keeping data as its pivotal point. The in-network cache, one of the important characteristics, makes data packets to be available from multiple locations on the Internet. Hence data access control and their enforcement mechanisms become even more critical in the NDNs. In this paper, we propose a novel encryption-based data access control scheme using Role-Based Encryption (RBE). The inheritance property of our scheme provides a natural way to achieve efficient data access control over hierarchical content. This in turn makes our scheme suitable for large scale real world content-centric applications and services such as Netflix. Further, the proposed scheme introduces an anonymous signature-based authentication mechanism to reject bogus data requests nearer to the source, thereby preventing them from entering the network. This in turn helps to mitigate better denial of service attacks. In addition, the signature mechanism supports unlinkability, which is essential to prevent leakages of individual user's access patterns. Another major feature of the proposed scheme is that it provides accountability of the Internet Service Providers (ISPs) using batch signature verification. Moreover, we have developed a transparent and secure dispute resolution and payment mechanism using smart-contract and blockchain technologies. We present a formal security analysis of our scheme to show it is provably secure against Chosen Plaintext Attacks. We also demonstrate that our scheme supports more functionalities than the existing schemes and its performance is better in terms of computation, communication and storage.

Most present reconfiguration methods in sharding blockchains rely on a secure randomness, whose generation might be complicated. Besides, a reference committee is usually in charge of the reconfiguration, making the process not decentralized. To address the above issues, this paper proposes a secure and decentralized shard reconfiguration protocol, which allows each shard to complete the selection and confirmation of its own shard members in turn. The PoW mining puzzle is calculated using the public key hash value in the member list confirmed by the last shard. Through the mining and shard member list commitment process, each shard can update its members safely and efficiently once in a while. Furthermore, it is proved that our protocol satisfies the safety, consistency, liveness, and decentralization properties. The honest member proportion in each confirmed shard member list is guaranteed to exceed a certain safety threshold, and all honest nodes have an identical view on the list. The reconfiguration is ensured to make progress, and each node has the same right to participate in the process. Our secure and decentralized shard reconfiguration protocol could be applied to all committee-based sharding blockchains.

In this paper, a blockchain based scheme is proposed to provide registration, mutual authentication and data sharing in wireless sensor network. The proposed model consists of three types of nodes: coordinators, cluster heads and sensor nodes. A consortium blockchain is deployed on coordinator nodes. The smart contracts execute on coordinators to record the identities of legitimate nodes. Moreover, they authenticate nodes and facilitate in data sharing. When a sensor node communicate and accesses data of any other sensor node, both nodes mutually authenticate each other. The smart contract of data sharing is used to provide a secure communication and data exchange between sensor nodes. Moreover, the data of all the nodes is stored on the decentralized storage called interplanetary file system. The simulation results show the response time of IPFS and message size during authentication and registration.

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.

Underwater Acoustic Networks (UANs) have long been recognized as an instrumental technology in various fields, from ocean monitoring to defense settings. Their security, though, has been scarcely investigated despite the strategic areas involved and the intrinsic vulnerability due to the broadcast nature of the wireless medium. In this work, we focus on attacks for which the attacker has partial or total knowledge of the network protocol stack. Our strategy uses a watchdog layer that allows upper layers to gather knowledge of overheard packets. In addition, a reputation system that is able to label nodes as trustful or suspicious is analyzed and evaluated via simulations. The proposed security mechanism has been implemented in the DESERT Underwater framework and a simulation study is conducted to validate the effectiveness of the proposed solution against resource exhaustion and sinkhole attacks.

People are dependent on Trusted Third Party (TTP) administration based Centralized systems for content sharing having a deficit of security, faith, immutability, and clearness. This work has proposed a file-sharing environment based on Blockchain by clouting the Interplanetary File System (IPFS) and Public Key Infrastructure (PKI) systems, advantages for overcoming these troubles. The smart contract is implemented to control the access privilege and the modified version of IPFS software is utilized to enforce the predefined access-control list. An application framework on a secure decentralized file sharing system is presented in combination with IPFS and PKI to secure file sharing. PKI having public and private keys is used to enable encryption and decryption of every file transaction and authentication of identities through Metamask to cryptographically recognize account ownership in the Blockchain system. A gas consumption-based result analysis is done in the private Ethereum network and it attains transparency, security managed access, and quality of data indicating better efficacy of this work.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. What is “hardware” security? The network designer relies on the security of the router box. The software developer relies on the TPM (Trusted Platform Module). The circuit designer worries about side-channel attacks. At the same time, electronics shrink: sensor nodes, IOT devices, smart devices are becoming more and more available. Adding security and cryptography to these often very resource constraint devices is a challenge. This presentation will focus on Physically Unclonable Functions and True Random Number Generators, two roots of trust, and their security testing.

Mobile Ad-hoc Networks (MANET) is an autonomous network consisting of movable devices that can form a network using wireless media. MANET routing protocols can be used for selecting an efficient and shortest path for data transmission between nodes in a smart environment formed by the Internet of Things (IoT). Networking in such MANET-enabled IoT system is based on the routing protocols of MANET, data sensing from things, and data handling and processing using IoT. This paper studies proactive approach-based secure routing protocols for MANET-enabled IoT and analyses these protocols to identify security issues in it. Since this fusion network is resource-constrained in nature, each of the studied protocol is evaluated to check if it is lightweight or not. Also, the solution to defend against active attacks in this network is discussed.

Based on the structure of turbo-polar codes, a secure symmetric encryption scheme is proposed to enhance information transmission security in this paper. This scheme utilizes interleaving at information bits and puncturing at parity bits for several times in the encoder. Correspondingly, we need to do the converse interleaving and fill zeros accurately at punctured position. The way of interleaving and puncturing is controlled by the private key of symmetric encryption, making sure the security of the system. The security of Secure Turbo-Polar Codes (STPC) is analyzed at the end of this paper. Simulation results are given to shown that the performance and complexity of Turbo-Polar Codes have little change after symmetric encryption. We also investigate in depth the influence of different remaining parity bit ratios on Frame Error Rate (FER). At low Signal to Noise Rate (SNR), we find it have about 0.6dB advantage when remaining parity bit ratio is between 1/20 and 1/4.

The energy sector is facing increasing risks, mainly concerning fraudulent activities and cyberattacks. This paradigm shift in risks would require innovative solutions. This paper proposes an innovative architecture based on Distributed Ledger Technologies (Blockchain) and Triple Entry Accounting (X-Accounting). The proposed architecture focusing on new applications of payment and billing would improve accountability and compliance as well as security and reliability. Future research can extend this architecture to other energy technologies and systems like EMS/SCADA and associated applications.

Integrating blockchain into Internet of Things (IoT) systems can offer many advantages to users and organizations. It provides the IoT network with the capability to distribute computation over many devices and improves the network's security by enhancing information integrity, ensuring accountability, and providing a way to implement better access control. The consensus mechanism is an essential part of any IoT-blockchain platform. In this paper, a novel consensus mechanism based on Proof-of-Authority (PoA) and Proof-of-Work (PoW) is proposed. The security advantages provided by PoW have been realized, and its long confirmation time can be mitigated by combining it with PoA in a single consensus mechanism called Honesty-based Distributed Proof-of-Authority (HDPoA) via scalable work. The measured results of transaction confirmation time and power consumption, and the analyses of security aspects have shown that HDPoA is a suitable and secure protocol for deployment within blockchain-based IoT applications.

Signatures are used on documents as written proof that the document was verified by the person indicated. Signature also indicated that the document originated from the signer if the document is transferred to another party. A document maybe in physical print form but may also be a digital print. A digital print requires additional security since a digital document may easily be altered by anyone although the said document is signed using a photographed or scanned signature. One of the means of security is by using the RSA Digital Signature method which is a combination of the RSA algorithm with Digital Signature. RSA algorithm is one of the public key cryptography algorithms, while Digital Signature is a security scheme which may guarantee the authenticity, non-repudiation, and integrity of a file by means of a hash function. This research implemented a web-based combination of RSA Digital Signature with SHA-3 hash function to secure the integrity of PDF files using PHP programming language. The result is a web-based system which could guarantee the authenticity, non repudiation and integrity of PDF files. Testing were carried out on six different sizes of PDF files ranging from 6 KB, up to 23285 KB on three different web browsers: Google Chrome, Microsoft Edge, and Mozilla Firefox. Average processing times of signing and verifying on each browsers were 1.3309 seconds, 1.2565 seconds, and 1.2667 seconds.

The Internet of Things (IoT) paradigm, with its highly distributed and interconnected architecture, is gaining ground in Industry 4.0 and in critical infrastructures like the eHealth sector, the Smart Grid, Intelligent Power Plants and Smart Mobility. In these critical sectors, the preservation of metrological characteristics and their traceability is a strong legal requirement, just like cyber-security, since it offers the ground for liability. Any vulnerability in the system in which the metrological network is embedded can endanger human lives, the environment or entire economies. This paper presents a framework comprised of a methodology and some tools for the governance of the metrological chain. The proposed methodology combines the RAMI 4.0 model, which is a Reference Architecture used in the field of Industrial Internet of Things (IIoT), with the the Reference Model for Information Assurance & Security (RMIAS), a framework employed to guarantee information assurance and security, merging them with the well established paradigms to preserve calibration and referability of metrological instruments. Thus, metrological traceability and cyber-security are taken into account straight from design time, providing a conceptual space to achieve security by design and to support the maintenance of the metrological chain over the entire system lifecycle. The framework lends itself to be completely automatized with Model Checking to support automatic detection of non conformity and anomalies at run time.

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, it is crucial to ensure IoT devices follow strict security requirements. At the same time, the prevalence of IoT devices offers developers a chance to design and develop Machine Learning (ML)-based intelligent software systems using their IoT devices. However, given the diversity of IoT devices, IoT developers may find it challenging to introduce appropriate security and ML techniques into their devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers’ security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. First, we collect around 53K IoT posts (questions + accepted answers) from SO. Second, we tokenize each post into sentences. Third, we automatically identify sentences containing security and ML-related discussions. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML-related discussions. There is no overlap between security and ML-related discussions, i.e., IoT developers discussing security requirements did not discuss ML requirements and vice versa. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.

The advancement of technology in the creation of new tools to solve problems such as information storage generates proportionally developing methods that search for security flaws or breaches that compromise said information. The need to periodically generate security reports on database managers is given by the complexity and number of attacks that can be carried out today. This project seeks to carry out an evaluation of the security of NoSQL database managers. The work methodology is developed according to the order of the objectives, it begins by synthesizing the types of vulnerabilities, attacks and protection schemes limited to MongoDB, Redis and Apache Cassandra. Once established, a prototype of a web system that stores information with a non-relational database will be designed on which a series of attacks defined by a test plan will be applied seeking to add, consult, modify or eliminate information. Finally, a report will be presented that sets out the attacks carried out, the way in which they were applied, the results, possible countermeasures, security advantages and disadvantages for each manager and the conclusions obtained. Thus, it is possible to select which tool is more convenient to use for a person or organization in a particular case. The results showed that MongoDB is more vulnerable to NoSQL injection attacks, Redis is more vulnerable to attacks registered in the CVE and that Cassandra is more complex to use but is less vulnerable.

As edge computing has been widely used in IoT (Internet of Things) systems, the security has become one of important issues for IoT. Because of a large amount of private information stored in edge computing devices, it makes edge computing devices attractive to various kinds attacks. To deal with this challenge, this paper proposes a security awareness scheme for edge computing devices in IoT system. Test results show that the proposed approach can improve services-oriented security situation of IoT systems based on edge computing.

In the security platform of Internet of Things (IoT), a licensed Low Power Wide Area Network (LPWAN) technology, named Narrowband Internet of Things (NB-IoT) is playing a vital role in transferring the information between objects. This technology is preferable for applications having a low data rate. As the number of subscribers increases, attack possibilities raise simultaneously. So securing the transmission between the objects becomes a big task. Bandwidth spoofing is one of the most sensitive attack that can be performed on the communication channel that lies between the access point and user equipment. This research proposal objective is to secure the system from the attack based on Unmanned Aerial vehicles (UAVs) enabled Small Cell Access (SCA) device which acts as an intruder between the user and valid SCA and investigating the scenario when any intruder device comes within the communication range of the NB-IoT enabled device. Here, this article also proposed a mathematical solution for the proposed scenario.

Most common user authentication methods use some form of password or a combination of passwords. However, encryption schemes are generally not directly compatible with user passwords and thus, Password-Based Key Derivation Functions (PBKDFs) are used to convert user passwords into cryptographic keys. In this paper, we analyze the theoretical security of PBKDF2 and present two vulnerabilities, γ-collision and δ-collision. Using AES-128 as our exemplar, we show that due to γ-collision, text encrypted with one user password can be decrypted with γ 1 different passwords. We also provide a proof that finding− a collision in the derived key for AES-128 requires δ lesser calls to PBKDF2 than the known Birthday attack. Due to this, it is possible to break password-based AES-128 in O(264) calls, which is equivalent to brute-forcing DES.

This position paper presents and illustrates the concept of security requirements as code – a novel approach to security requirements specification. The aspiration to minimize code duplication and maximize its reuse has always been driving the evolution of software development approaches. Object-Oriented programming (OOP) takes these approaches to the state in which the resulting code conceptually maps to the problem that the code is supposed to solve. People nowadays start learning to program in the primary school. On the other hand, requirements engineers still heavily rely on natural language based techniques to specify requirements. The key idea of this paper is: artifacts produced by the requirements process should be treated as input to the regular object-oriented analysis. Therefore, the contribution of this paper is the presentation of the major concepts for the security requirements as the code method that is illustrated with a real industry example from the VeriDevOps project.

The semaphore mechanism is an important part of the embedded operating system. Therefore, it is very necessary to ensure its safety. Traditional software testing methods are difficult to ensure 100% coverage of the program. Therefore, it is necessary to adopt a formal verfication method which proves the correctness of the program theoretically. This paper proposes a proof framework based on the theorem proof tool Coq: modeling the semaphore mechanism, extracting important properties from the requirement documents, and finally verifying that the semaphore mechanism can meet these properties, which means the correctness of the semaphore mechanism is proved and also illustrates the feasibility of the verification framework proposed in this paper, which lays a foundation for the verification of other modules of operating systems.

In recent years, cyber attackers are targeting the power system and imposing different damages to the national economy and public safety. False Data Injection Attack (FDIA) is one of the main types of Cyber-Physical attacks that adversaries can manipulate power system measurements and modify system data. Consequently, it may result in incorrect decision-making and control operations and lead to devastating effects. In this paper, we propose a two-stage detection method. In the first step, Gated Recurrent Unit (GRU), as a deep learning algorithm, is employed to forecast the data for the future horizon. Meanwhile, hyperparameter optimization is implemented to find the optimum parameters (i.e., number of layers, epoch, batch size, β1, β2, etc.) in the supervised learning process. In the second step, an unsupervised scoring algorithm is employed to find the sequences of false data. Furthermore, two penalty factors are defined to prevent the objective function from greedy behavior. We assess the capability of the proposed false data detection method through simulation studies on a real-world data set (ComEd. dataset, Northern Illinois, USA). The results demonstrate that the proposed method can detect different types of attacks, i.e., scaling, simple ramp, professional ramp, and random attacks, with good performance metrics (i.e., recall, precision, F1 Score). Furthermore, the proposed deep learning method can mitigate false data with the estimated true values.