Biblio

The Internet is evolving everywhere and expanding its entity globally. The IoT(Internet of things) is a new and interesting concept introduced in this world of internet. Generally it is interconnected computing device which can be embedded in our daily routine objects through which we can send and receive data. It is beyond connecting computers and laptops only although it can connect billion of devices. It can be described as reliable method of communication that also make use of other technologies like wireless sensor, QR code etc. IoT (Internet of Things) is making everything smart with use of technology like smart homes, smart cities, smart watches. In this chapter, we will study the security algorithms in IoT (Internet of Things) which can be achieved with encryption process. In the world of IoT, data is more vulnerable to threats. So as to protect data integrity, data confidentiality, we have Light weight Encryption Algorithms like symmetric key cryptography and public key cryptography for secure IoT (Internet of Things) named as Secure IoT. Because it is not convenient to use full encryption algorithms that require large memory size, large program code and larger execution time. Light weight algorithms meet all resource constraints of small memory size, less execution time and efficiency. The algorithms can be measured in terms of key size, no of blocks and algorithm structure, chip size and energy consumption. Light Weight Techniques provides security to smart object networks and also provides efficiency. In Symmetric Key Cryptography, two parties can have identical keys but has some practical difficulty. Public Key Cryptography uses both private and public key which are related to each other. Public key is known to everyone while private key is kept secret. Public Key cryptography method is based on mathematical problems. So, to implement this method, one should have a great expertise.

The article proposes a general approach to the implementation of encryption schemes based on the group of automorphisms of the Hermitian functional field. The three-parameter group is used with logarithmic captions outside the center of the group. This time we applied for an encryption scheme based on a Hermitian function field with homomorphic encryption. The use of homomorphic encryption is an advantage of this implementation. The complexity of the attack and the size of the encrypted message depends on the strength of the group.

IP Identification (IP ID) is an IP header field that identifies a data packet in the network to distinguish its fragments from others during the reassembly process. Random generated IP ID field could be used as a covert channel by embedding hidden bits within it. This paper uses the support vector machine (SVM) while enabling a features reduction procedure for investigating to what extend could the entropy feature of the IP ID covert channel affect the detection. Then, an entropy-based SVM is employed to evaluate the roles of the IP ID covert channel hidden bits on detection. Results show that, entropy is a distinct discrimination feature in classifying and detecting the IP ID covert channel with high accuracy. Additionally, it is found that each of the type, the number and the position of the hidden bits within the IP ID field has a specified influence on the IP ID covert channel detection accuracy.

Information security has become a crucial issue not only from the technical standpoint, but also from the managerial standpoint. The necessity for organizations to understand and manage cyber risk has led to the rise of a plethora of risk assessment methods and tools. These approaches are often difficult to interpret and complex to manage for organizations. In this paper, we propose a simple and quantitative method for the estimation of the likelihood of occurrence of a cyber incident. Our approach uses a generalized logistic function and a cumulative geometric distribution to combine the maturity and the complexity of the technical infrastructure of an organization with its attractiveness towards cyber criminals.

Considered is a network of parallel wireless channels in which individual parties are engaged in secret communication under the protection of cooperative jamming. A strategic eavesdropper selects the most vulnerable channels to attack. Existing works usually suggest the defender allocate limited cooperative jamming power to various channels. However, it usually requires some strong assumptions and complex computation to find such an optimal power control policy. This paper proposes a probabilistic cooperative jamming scheme such that the defender focuses on protecting randomly selected channels. Two different cases regarding each channel’s eavesdropping capacity are discussed. The first case studies the general scenario where each channel has different eavesdropping capacity. The second case analyzes an extreme scenario where all channels have the same eavesdropping capacity. Two non-zero-sum Nash games model the competition between the network defender and an eavesdropper in each case. Furthermore, considering the case that the defender does not know the eavesdropper’s channel state information (CSI) leads to a Bayesian game. For all three games, we derive conditions for the existence of a unique Nash equilibrium (NE), and obtain the equilibria and the value functions in closed form.

Numerous prevalent techniques build a Multi-Modal Biometric (MMB) system that struggles in offering security and also revocability onto the templates. This work proffered a MMB system centred on the Modulus Fuzzy Vault (MFV) aimed at resolving these issues. The methodology proposed includes Fingerprint (FP), Palmprint (PP), Ear and also Retina images. Utilizing the Boosted Double Plateau Histogram Equalization (BDPHE) technique, all images are improved. Aimed at removing the unnecessary things as of the ear and the blood vessels are segmented as of the retina images utilizing the Modified Balanced Iterative Reducing and Clustering using Hierarchy (MBIRCH) technique. Next, the input traits features are extracted; then the essential features are chosen as of the features extracted utilizing the Bidirectional Deer Hunting optimization Algorithm (BDHOA). The features chosen are merged utilizing the Normalized Feature Level and Score Level (NFLSL) fusion. The features fused are saved securely utilizing Modulus Fuzzy Vault. Upto fusion, the procedure is repeated aimed at the query image template. Next, the de-Fuzzy Vault procedure is executed aimed at the query template, and then the key is detached by matching the query template’s and input biometric template features. The key separated is analogized with the threshold that categorizes the user as genuine or else imposter. The proposed BDPHE and also MFV techniques function efficiently than the existent techniques.

Insider threat makes enterprises or organizations suffer from the loss of property and the negative influence of reputation. User behavior analysis is the mainstream method of insider threat detection, but due to the lack of fine-grained detection and the inability to effectively capture the behavior patterns of individual users, the accuracy and precision of detection are insufficient. To solve this problem, this paper designs an insider threat detection method based on user historical behavior and attention mechanism, including using Long Short Term Memory (LSTM) to extract user behavior sequence information, using Attention-based on user history behavior (ABUHB) learns the differences between different user behaviors, uses Bidirectional-LSTM (Bi-LSTM) to learn the evolution of different user behavior patterns, and finally realizes fine-grained user abnormal behavior detection. To evaluate the effectiveness of this method, experiments are conducted on the CMU-CERT Insider Threat Dataset. The experimental results show that the effectiveness of this method is 3.1% to 6.3% higher than that of other comparative model methods, and it can detect insider threats in different user behaviors with fine granularity.

With the increasing use of information and communication technology in electrical power grids, the security of energy supply is increasingly threatened by cyber-attacks. Traditional cyber-security measures, such as firewalls or intrusion detection/prevention systems, can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energy information systems. Given the complexity and lack of detailed knowledge of coordinated, timed attacks in smart grid applications, we need information and insight into realistic attack scenarios in an appropriate and practical setting. In this paper, we present a man-in-the-middle-based attack scenario that intercepts process communication between control systems and field devices, employs false data injection techniques, and performs data corruption such as sending false commands to field devices. We demonstrate the applicability of the presented attack scenario in a physical smart grid laboratory environment and analyze the generated data under normal and attack conditions to extract domain-specific knowledge for detection mechanisms.

This paper emphasizes the implementation of a conditioning circuit specific for an inductive or capacitive sensor. There are some inductive sensors, such as the proximity sensor, for which the inductance is dependent with the distance, or capacitive sensors strongly dependent with the humidity, distance, etc. This category of sensors is suitable for AC domain excitation from the measurement procedure point of view. Taking into consideration the fabrication technology, the measured physical quantity is being encoded as frequency or amplitude. To generate a sinusoidal signal with constant frequency and amplitude, the Colpitts or Hartley oscillators can be used [1], [2]. But the novelty of this paper is a different approach which reveals a microcontroller-based technology where the LC circuit works in an oscillating regime even though there is an underdamped oscillation behavior. For the oscillations’ occurrence, there will be a periodical energy injection using a driving source. One of the main advantages of the mentioned circuit is the small component number. The central unit of the embedded system will fulfil two functions: maintains the oscillating regime and measures the amplitude or frequency of the output signal. In this way, the built embedded system will be robust and easy to use due to its software configuration capabilities. As a plus, such a system can measure additional sensors used in environment parameters’ compensating procedure.

We propose a localized oscillation amplitude monitoring (OAM) method for the mitigation of cyber threats directed at the wide area control (WAC) system used to coordinate control of Flexible AC Transmission Systems (FACTS) for power oscillation damping (POD) of active power flow on inter-area tie lines. The method involves monitoring the inter-area tie line active power oscillation amplitude over a sliding window. We use system instability - inferred from oscillation amplitudes growing instead of damping - as evidence of an indication of a malfunction in the WAC of FACTS, possibly indicative of a cyber attack. Monitoring the presence of such a growth allows us to determine whether any destabilizing behaviors appear after the WAC system engages to control the POD. If the WAC signal increases the oscillation amplitude over time, thereby diminishing the POD performance, the FACTS falls back to POD using local measurements. The proposed method does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the OAM method for mitigation.

Deepfake detection techniques have been widely studied to resolve security issues. However, existing techniques mainly focused on RGB channel-based analysis, which still shows incomplete detection accuracy. In this paper, we validate the performance of Gray channel-based deepfake detection. To compare RGB channel-based analysis and Gray channel-based analysis in deepfake detection, we quantitatively measured the performance by using popular CNN models, deepfake datasets, and evaluation indicators. Our experimental results confirm that Gray channel-based deepfake detection outperforms RGB channel-based deepfake detection in terms of accuracy and analysis time.

This paper considers methods of fingerprint protection in biometric authentication systems. Including methods of protecting fingerprint templates using zero digital watermarks and cryptography techniques. The paper considers a secure authentication model using cryptography and digital watermarks.

Human-machine teaming (HMT) operates in a context defined by the mission. Varying from the complexity and disturbance in the cooperation between humans and machines, a single machine has difficulty handling work with humans in the scales of efficiency and workload. Swarm of machines provides a more feasible solution in such a mission. Human-swarm teaming (HST) extends the concept of HMT in the mission, such as persistent surveillance, search-and-rescue, warfare. Bringing the concept of HST faces several scientific challenges. For example, the strategies of allocation on the high-level decision making. Here, human usually plays the supervisory or decision making role. Performance of such fixed structure of HST in actual mission operation could be affected by the supervisor’s status from many aspects, which could be considered in three general parts: workload, situational awareness, and trust towards the robot swarm teammate and mission performance. Besides, the complexity of a single human operator in accessing multiple machine agents increases the work burdens. An interface between swarm teammates and human operators to simplify the interaction process is desired in the HST.In this paper, instead of purely considering the workload of human teammates, we propose the computational model of human swarm interaction (HSI) in the simulated map surveillance mission. UAV swarm and human supervisor are both assigned in searching a predefined area of interest (AOI). The workload allocation of map monitoring is adjusted based on the status of the human worker and swarm teammate. Workload, situation awareness ability, trust are formulated as independent models, which affect each other. A communication-aware UAV swarm persistent surveillance algorithm is assigned in the swarm autonomy portion. With the different surveillance task loads, the swarm agent’s thrust parameter adjusts the autonomy level to fit the human operator’s needs. Reinforcement learning is applied in seeking the relative balance of workload in both human and swarm sides. Metrics such as mission accomplishment rate, human supervisor performance, mission performance of UAV swarm are evaluated in the end. The simulation results show that the algorithm could learn the human-machine trust interaction to seek the workload balance to reach better mission execution performance. This work inspires us to leverage a more comprehensive HST model in more practical HMT application scenarios.

As a classic quantum computing implementation, the Deustch-Jozsa (DJ) algorithm is taught in many courses pertaining to quantum information science and technology (QIST). We exploit the DJ framework as an educational testbed, illustrating fundamental qubit concepts while identifying associated algorithmic challenges. In this work, we present a self-contained exploration which may be beneficial in educating the future quantum workforce. Quantum Key Distribution (QKD), an improvement over the classical Public Key Infrastructure (PKI), allows two parties, Alice and Bob, to share a secret key by using the quantum physical properties. For QKD the DJ-packets, consisting of the input qubits and the target qubit for the DJ algorithm, carry the secret information between Alice and Bob. Previous research from Nagata and Nakamura discovered in 2015 that the DJ algorithm for QKD allows an attacker to successfully intercept and remain undetected. Improving upon the past research we increased the entropy of DJ-packets through: (i) size hopping (H), where the number of qubits in consecutive DJ-packets keeps on changing and (ii) reordering (R) the qubits within the DJ-packets. These concepts together illustrate the multiple scales where entropy may increase in a DJ algorithm to make for a more robust QKD framework, and therefore significantly decrease Eve’s chance of success. The proof of concept of the new schemes is tested on Google’s Cirq quantum simulator, and detailed python simulations show that attacker’s interception success rate can be drastically reduced.

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting.

Recently, most of the processes are being computerized, due to the development of information and communication technology. In proportion to this, cyber-attacks are also increasing, and state-sponsored cyber-attacks are becoming a great threat to the country. These attacks are often composed of stages and proceed step-by-step, so for defense, it is necessary to predict the next action and perform appropriate mitigation. To this end, the paper proposes a mitigation-based performance evaluation method. We developed the new true positive which can have a value between 0 and 1 according to the mitigation. The experiment result and case studies show that the proposed method can effectively measure forecasting results under cyber security defense system.

With the rapid development of communication net-work, the types and quantities of network traffic data have in-creased substantially. What followed was the frequent occurrence of versatile cyber attacks. As an important part of network security, the network-based intrusion detection system (NIDS) can monitor and protect the network equippments and terminals in real time. The traditional detection methods based on deep learning (DL) are always in supervised manners in NIDS, which can automatically build end-to-end detection model without man-ual feature extraction and selection by domain experts. However, supervised learning methods require large-scale labeled data, yet capturing large labeled datasets is a very cubersome, tedious and time-consuming manual task. Instead, unsupervised learning is an effective way to overcome this problem. Nonetheless, the ex-isting unsupervised methods are prone to low detection efficiency and are difficult to train. In this paper we propose a novel NIDS method called PGAN based on generative adversarial network (GAN) to detect the abnormal traffic from the perspective of Anomaly Detection, which leverage the competitive speciality of adversarial training to learn the normal traffic. Based on the public dataset CICIDS2017, three experimental results show that PGAN can significantly outperform other unsupervised methods like stacked autoencoder (SAE) and isolation forest (IF).

In this paper, we propose a zero-distortion blind reversible robust scheme for watermarking relational databases called PKMark. Data owner can declare the copyright of the databases or pursue the infringement by extracting the water-mark information embedded in the database. PKMark is mainly based on the primary key attribute of the tuple. So it does not depend on the type of the attribute, and can provide high-precision numerical attributes. PKMark uses RSA encryption on the watermark before embedding the watermark to ensure the security of the watermark information. Then we use RSA to sign the watermark cipher text so that the owner can verify the ownership of the watermark without disclosing the watermark. The watermark embedding and extraction are based on the hash value of the primary key, so the scheme has blindness and reversibility. In other words, the user can obtain the watermark information or restore the original database without comparing it to the original database. Our scheme also has almost excellent robustness against addition attacks, deletion attacks and alteration attacks. In addition, PKMark is resistant to additive attacks, allowing different users to embed multiple watermarks without interfering with each other, and it can indicate the sequence of watermark embedding so as to indicate the original copyright owner of the database. This watermarking scheme also allows data owners to detect whether the data has been tampered with.

Multi-processors System-on-Chip (MPSoC) are a key enabling technology for different applications characterized by hyper-connectivity and multi-tenant requirements, where resources are shared and communication is ubiquitous. In such an environment, security plays a major role. To cope with these security needs, MPSoCs usually integrate cryptographic functionalities deployed as software and/or hardware solutions. Quantum computing represents a threat for the current cryptography. To overcome such a threat, Post-quantum cryptography (PQC) can be used, thus ensuring the long term security of different applications. Since 2017, NIST is running a PQC standardization process. While the focus has been the security analysis of the different PQC candidates and the software implementation, the MPSoC PQC implementation has been neglected. To this end, this work presents two contributions. First, the exploration of the multicore capabilities for developing optimized PQC implementations. As a use case, NTRU lattice-based PQC, finalist for the NIST standardization process, is discussed. Second, NTRU was deployed on an AURIX microcontroller of Infineon Technologies AG with the Real-Time Operating System PXROS-HR from HighTec EDV-Systeme GmbH. Results show that NTRU can be efficiently implemented and optimized on a multicore architecture, improving the performance up to 43% when compared to single core solutions.

The power grid has high requirements for data security, and data security audit technology is facing challenges. Because the server in the power grid operating environment is considered untrustworthy and does not have the authority to obtain the secret key, the encrypted data cannot be parsed and the data processing ability of the data center is restricted. In response to the above problems, the power grid database encryption system was designed, and the access control module and the encryption module that should be written based on SQL statements were explained. The database encryption system was developed using the Java language and deployed in the cloud environment. Finally, the method was proved by experiments. feasibility.

With the widespread deployment of Internet of Things (IoT) devices, hackers can use IoT devices to launch large-scale distributed denial of service (DDoS) attacks, which bring great harm to the Internet. However, how to defend against these attacks remains to be an open challenge. In this paper, we propose a novel prevention method for IoT DDoS attacks based on blockchain and obfuscation of IP addresses. Our observation is that IoT devices are usually resource-constrained and cannot support complicated cryptographic algorithms such as RSA. Based on the observation, we employ a novel authentication then communication mechanism for IoT DDoS attack prevention. In this mechanism, the attack targets' IP addresses are encrypted by a random security parameter. Clients need to be authenticated to obtain the random security parameter and decrypt the IP addresses. In particular, we propose to authenticate clients with public-key cryptography and a blockchain system. The complex authentication and IP address decryption operations disable IoT devices and thus block IoT DDoS attacks. The effectiveness of the proposed method is analyzed and validated by theoretical analysis and simulation experiments.

Quantum entanglement gives rise to a range of non-classical effects, which are extensively exploited in quantum computing and quantum communication. Therefore, detection and quantification of entanglement as well as preparation of highly entangled quantum states remain the fundamental objectives in these fields. Much attention has been devoted to the studies of graph states, which play a role of a central resource in quantum error correction, quantum cryptography and practical quantum metrology in the presence of noise.We examine multi-qubit graph states generated by the action of controlled phase shift operators on a separable quantum state of a system, in which all the qubits are in arbitrary identical states. Analytical expression is obtained for the geometric measure of entanglement of a qubit with other qubits in graph states represented by arbitrary graphs. We conclude that this quantity depends on the degree of the vertex corresponding to the qubit, the absolute values of the parameter of the phase shift gate and the parameter of the initial state the gate is acting on. Moreover, the geometric measure of entanglement of certain types of graph states is quantified on the IBM’s quantum computer ibmq\_athens based on the measurements of the mean spin. Namely, we consider states associated with the native connectivity of ibmq\_athens, the claw and the complete graphs. Appropriate protocols are proposed to prepare these states on the quantum computer. The results of quantum computations verify our theoretical findings [1].

We tackle the problem where a server owns a trained Machine Learning (ML) model and a client/user has an unclassified query that he wishes to classify in secure and private fashion using the server’s model. During the process the server learns nothing, while the user learns only his final classification and nothing else. Since several ML classification algorithms, such as deep neural networks, support vector machines-SVM (and hyperplane decisions in general), Logistic Regression, Naïve Bayes, etc., can be expressed in terms of matrix operations, initially we propose novel secure matrix operations as our building blocks. On top of them we build our secure and private ML classification algorithms under strict security and privacy requirements. As our underlying cryptographic primitives are shown to be resilient to quantum computer attacks, our algorithms are also suitable for the post-quantum world. Our theoretical analysis and extensive experimental evaluations show that our secure matrix operations, hence our secure ML algorithms build on top of them as well, outperform the state of the art schemes in terms of computation and communication costs. This makes our algorithms suitable for devices with limited resources that are often found in Industrial IoT (Internet of Things)

The critical nature of smart grids (SGs) attracts various network attacks and malicious manipulations. Existent SG solutions are less capable of ensuring secure and trustworthy operation. This is due to the large-scale nature of SGs and reliance on network protocols for trust management. A particular example of such severe attacks is the false data injection (FDI). FDI refers to a network attack, where meters' measurements are manipulated before being reported in such a way that the energy system takes flawed decisions. In this paper, we exploit the secure nature of blockchains to construct a data management framework based on public blockchain. Our framework enables trustworthy data storage, verification, and exchange between SG components and decision-makers. Our proposed system enables miners to invest their computational power to verify blockchain transactions in a fully distributed manner. The mining logic employs machine learning (ML) techniques to identify the locations of compromised meters in the network, which are responsible for generating FDI attacks. In return, miners receive virtual credit, which may be used to pay their electric bills. Our design circumvents single points of failure and intentional FDI attempts. Our numerical results compare the accuracy of three different ML-based mining logic techniques in two scenarios: focused and distributed FDI attacks for different attack levels. Finally, we proposed a majority-decision mining technique for the practical case of an unknown FDI attack level.

Payment systems are a critical component of everyday life in our society. While in many situations payments are still slow, opaque, siloed, expensive or even fail, users expect them to be fast, transparent, cheap, reliable and global. Recent technologies such as distributed ledgers create opportunities for near-real-time, cheaper and more transparent payments. However, in order to achieve a global payment system, payments should be possible not only within one ledger, but also across different ledgers and geographies.In this paper we propose Secure Payments with Overlay Networks (SPON), a service that enables global payments across multiple ledgers by combining the transaction exchange provided by the Interledger protocol with an intrusion-tolerant overlay of relay nodes to achieve (1) improved payment latency, (2) fault-tolerance to benign failures such as node failures and network partitions, and (3) resilience to BGP hijacking attacks. We discuss the design goals and present an implementation based on the Interledger protocol and Spines overlay network. We analyze the resilience of SPON and demonstrate through experimental evaluation that it is able to improve payment latency, recover from path outages, withstand network partition attacks, and disseminate payments fairly across multiple ledgers. We also show how SPON can be deployed to make the communication between different ledgers resilient to BGP hijacking attacks.