Biblio

Information leakage estimation for practical wiretap codes is a challenging task for which existing solutions are either too complex or suboptimal, and don't scale for large blocklengths. In this paper we present a new method, based on a modified version of the successive cancellation decoder in order to compute the information leakage for the wiretap polar code which improves upon existing methods in terms of complexity and accuracy. Results are presented for classical binary-input symmetric channels alike the Binary Erasure Channel (BEC), the Binary Symmetric Channel (BSC) and Binary Input Additive White Gaussian Noise channel (BI-AWGN).

In existing Communication Based Train Control (CBTC) system, information security threats are analyzed, then information security demands of CBTC system are put forward. To protect information security, three security domains are divided according the Safety Integrity Level (SIL)) of CBTC system. Information security architecture of CBTC system is designed, special use firewalls and intrusion detection system are adopted. Through this CBTC system security are enhanced and operation safety is ensured.

With the rapid construction and popularization of smart grid, the security of data in smart grid has become the basis for the safe and stable operation of smart grid. This paper proposes a data security threat discovery model for smart grid. Based on the prediction data analysis method, combined with migration learning technology, it analyzes different data, uses data matching process to classify the losses, and accurately predicts the analysis results, finds the security risks in the data, and prevents the illegal acquisition of data. The reinforcement learning and training process of this method distinguish the effective authentication and illegal access to data.

This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.

Batched network coding (BNC) is a low-complexity solution to network transmission in feedbackless multi-hop packet networks with packet loss. BNC encodes the source data into batches of packets. As a network coding scheme, the intermediate nodes perform recoding on the received packets instead of just forwarding them. Blockwise adaptive recoding (BAR) is a recoding strategy which can enhance the throughput and adapt real-time changes in the incoming channel condition. In wireless applications, in order to combat burst packet loss, interleavers can be applied for BNC in a hop-by-hop manner. In particular, a batch-stream interleaver that permutes packets across blocks can be applied with BAR to further boost the throughput. However, the previously proposed minimal communication protocol for BNC only supports permutation of packets within a block, called intrablock interleaving, and so it is not compatible with the batch-stream interleaver. In this paper, we design an intrablock interleaver for BAR that is backward compatible with the aforementioned minimal protocol, so that the throughput can be enhanced without upgrading all the existing devices.

Underwater networks have the potential to allow previously unexplored applications as well as improve our ability to observe and forecast the ocean. Underwater acoustic sensor networks (UASNs) are often deployed in unprecedented and hostile waters and face many security threats. Applications based on UASNs such as coastal defense, pollution monitoring, assisted navigation to name a few, require secure communication. A new set of communication protocols and cooperative coordination algorithms have been proposed to enable collaborative monitoring tasks. However, such protocols overlook security as a key performance indicator. Spoofing, altering, or replaying routing information can affect the entire network, making UASN vulnerable to routing attacks such as selective forwarding, sinkhole attack, Sybil attack, acknowledgement spoofing and HELLO flood attack. The lack of security against such threats is startling if it is observed that security is indeed an important requirement in many emerging civilian and military applications. In this work, the sinkhole attack prevalent among UASNs is looked at and discuss mitigation approaches that can feasibly be implemented in UnetStack3.

Learning a disentangled representation of the latent space has become one of the most fundamental problems studied in computer vision. Recently, many Generative Adversarial Networks (GANs) have shown promising results in generating high fidelity images. However, studies to understand the semantic layout of the latent space of pre-trained models are still limited. Several works train conditional GANs to generate faces with required semantic attributes. Unfortunately, in these attempts, the generated output is often not as photo-realistic as the unconditional state-of-the-art models. Besides, they also require large computational resources and specific datasets to generate high fidelity images. In our work, we have formulated a Markov Decision Process (MDP) over the latent space of a pre-trained GAN model to learn a conditional policy for semantic manipulation along specific attributes under defined identity bounds. Further, we have defined a semantic age manipulation scheme using a locally linear approximation over the latent space. Results show that our learned policy samples high fidelity images with required age alterations, while preserving the identity of the person.

Underwater wireless sensor network(UWSN) is an emerging technology for exploring and research inside the ocean. Since it is somehow similar to the normal wireless network, which uses radio signals for communication purposes, while UWSN uses acoustic for communication between nodes inside the ocean and sink nodes. Due to unattended areas and the vulnerability of acoustic medium, UWNS are more prone to various malicious attacks like Sybil attack, Black-hole attack, Wormhole attack, etc. This paper analyzes blackhole attacks in UWSN and proposes an algorithm to mitigate blackhole attacks by forming clusters of nodes and selecting coordinator nodes from each cluster to identify the presence of blackholes in its cluster. We used public-key cryptography and the challenge-response method to authenticate and verify nodes.

To improve communication underwater, the underwater sensor networks (UWSN) provide gains for many different underwater applications, like Underwater Data-centers, Aquatic Monitoring, Tsunami Monitoring Systems, Aquatic Monitoring, Underwater Oil Field Discovery, Submarine Target Localization, Surveilling Water Territory of the Country via UWSN, Submarine Target Localization and many more. underwater applications are dependent on secure data communication in an underwater environment, so Data transmission in Underwater Sensor Network is a need of the future. Underwater data transmission itself is a big challenge due to various limitations of underwater communication mediums like lower bandwidth, multipath effect, path loss, propagation delay, noise, Doppler spread, and so on. These challenges make the underwater networks one of the most vulnerable networks for many different security attacks like sinkhole, spoofing, wormhole, misdirection, etc. It causes packets unable to be delivered to the destination, and even worse forward them to malicious nodes. A compromised node, which may be a router, intercepts packets going through it, and selectively drops them or can perform some malicious activity. This paper presents a solution to Mitigate unsecured data forwarding related attacks of an underwater sensor network, our solution uses a pre-shared key to secure communication and hashing algorithm to maintain the integrity of stored locations at head node and demonstration of attack and its mitigation done on Unetstack software.

Federated learning is a privacy-preserving learning system where participants locally update a shared model with their own training data. Despite the advantage that training data are not sent to a server, there is still a risk that a state-of-the-art model inversion attack, which may be conducted by the server, infers training data from the models updated by the participants, referred to as individual models. A solution to prevent such attacks is differential privacy, where each participant adds noise to the individual model before sending it to the server. Differential privacy, however, sacrifices the quality of the shared model in compensation for the fact that participants' training data are not leaked. This paper proposes a federated learning system that is resistant to model inversion attacks without sacrificing the quality of the shared model. The core idea is that each participant divides the individual model into model fragments, shuffles, and aggregates them to prevent adversaries from inferring training data. The other benefit of the proposed system is that the resulting shared model is identical to the shared model generated with the naive federated learning.

With the gradually popular high-speed wireless networks and 5G environments, the quality and reliability of network services will be suited for mobile vehicles. In addition to communicating information between vehicles, they can also communicate information with surrounding roadside equipment, pedestrians or traffic signs, and thus improve the road safety of passers-by.Recently, various countries have continuously invested in research on autonomous driving and unmanned vehicles. The open communication environment of the Internet of Vehicles in 5G will expose all personal information in the field of wireless networks. This research is based on the consideration of information security and personal data protection. We will focus on how to protect the real-time transmission of information between mobile vehicles to prevent from imbedding or altering important transmission information by unauthorized vehicles, drivers or passers-by participating in communications. Moreover, this research proposes a multi-level security key management agreement based on a dynamic M-tree structure for Internet of Vehicles to achieve flexible and scalable key management on large-scale Internet of Vehicles.

The spread of Internet of Things (IoT) devices and high-speed communications, such as 5G, makes their services rich and diverse. Therefore, it is desirable to perform functions of rich services transparently and use edge computing environments flexibly at intermediate locations on the Internet, from the perspective of a network system. When this type of edge computing environment is achieved, IoT nodes as end devices of the Internet can fully utilize edge computing systems and cloud systems without any change, such as switching destination IP addresses between them, along with protocol maintenance for the switching. However, when the data transfer in the communication is encrypted, a decryption method is necessary at the edge, to realize these transparent edge services. In this study, a transparent common key-exchanging method with cloud service has been proposed as the destination node of a communication pair, to transparently decrypt a secure sockets layer-encrypted communication stream at the edge area. This enables end devices to be free from any changes and updates to communicate with the destination node.

The widely adopted dynamic line rating (DLR) mechanism can improve the operation efficiency for industrial and commercial power systems. However, the predicted environmental parameters used in DLR bring great uncertainty to transmission line capacity estimation and may introduce system security risk if over-optimistic estimation is adopted in the operation process, which could affect the electrical safety of industrial and commercial power systems in multiple cases. Therefore, it becomes necessary to establish a system operation security assessment model to reduce the risk and provide operational guidance to enhance electrical safety. This paper aims to solve the electrical safety problems caused by the transmission line under DLR mechanism. An operation security assessment method of transmission lines considering DLR uncertainty is proposed to visualize the safety margin under the given operation strategy and optimally setting transmission line capacity while taking system safety into account. With the help of robust optimization (RO) techniques, the uncertainty is characterized and a risk-averse transmission line rating guidance can be established to determine the safety margin of line capacity for system operation. In this way, the operational security for industrial and commercial power systems can be enhanced by reducing the unsafe conditions while the operational efficiency benefit provided by DLR mechanism still exist.

Ben-Or and Linial (1985) introduced the full information model for coin-tossing protocols involving \$n\$ processors with unbounded computational power using a common broadcast channel for all their communications. For most adversarial settings, the characterization of the exact or asymptotically optimal protocols remains open. Furthermore, even for the settings where near-optimal asymptotic constructions are known, the exact constants or poly-logarithmic multiplicative factors involved are not entirely well-understood. This work studies \$n\$-processor coin-tossing protocols where every processor broadcasts an arbitrary-length message once. An adaptive Byzantine adversary, based on the messages broadcast so far, can corrupt \$k=1\$ processor. A bias-\$X\$ coin-tossing protocol outputs 1 with probability \$X\$; otherwise, it outputs 0 with probability (\$1-X\$). A coin-tossing protocol's insecurity is the maximum change in the output distribution (in the statistical distance) that a Byzantine adversary can cause. Our objective is to identify bias-\$X\$ coin-tossing protocols achieving near-optimal minimum insecurity for every \$Xın[0,1]\$. Lichtenstein, Linial, and Saks (1989) studied bias-\$X\$ coin-tossing protocols in this adversarial model where each party broadcasts an independent and uniformly random bit. They proved that the elegant “threshold coin-tossing protocols” are optimal for all \$n\$ and \$k\$. Furthermore, Goldwasser, Kalai, and Park (2015), Kalai, Komargodski, and Raz (2018), and Haitner and Karidi-Heller (2020) prove that \$k=\textbackslashtextbackslashmathcalO(\textbackslashtextbackslashsqrtn \textbackslashtextbackslashcdot \textbackslashtextbackslashmathsfpolylog(n)\$) corruptions suffice to fix the output of any bias-\$X\$ coin-tossing protocol. These results encompass parties who send arbitrary-length messages, and each processor has multiple turns to reveal its entire message. We use an inductive approach to constructing coin-tossing protocols using a potential function as a proxy for measuring any bias-\$X\$ coin-tossing protocol's susceptibility to attacks in our adversarial model. Our technique is inherently constructive and yields protocols that minimize the potential function. It is incidentally the case that the threshold protocols minimize the potential function, even for arbitrary-length messages. We demonstrate that these coin-tossing protocols' insecurity is a 2-approximation of the optimal protocol in our adversarial model. For any other \$Xın[0,1]\$ that threshold protocols cannot realize, we prove that an appropriate (convex) combination of the threshold protocols is a 4-approximation of the optimal protocol. Finally, these results entail new (vertex) isoperimetric inequalities for density-\$X\$ subsets of product spaces of arbitrary-size alphabets.

With the development of artificial intelligence and cryptography, intelligent cryptography will be the trend of encrypted communications in the future. Abadi designed an encrypted communication model based on a generative adversarial network, which can communicate securely when the adversary knows the ciphertext. The communication party and the adversary fight against each other to continuously improve their own capabilities to achieve a state of secure communication. However, this model can only have a better communication effect under the 16 bits communication length, and cannot adapt to the length of modern encrypted communication. Combine the neural network structure in DCGAN to optimize the neural network of the original model, and at the same time increase the batch normalization process, and optimize the loss function in the original model. Experiments show that under the condition of the maximum 2048-bit communication length, the decryption success rate of communication reaches about 0.97, while ensuring that the adversary’s guess error rate is about 0.95, and the training speed is greatly increased to keep it below 5000 steps, ensuring safety and efficiency Communication.

Web caching is one strategy that can be used to speed up response times by storing frequently accessed data in the cache server. Given the cache server limited capacity, it is necessary to determine the priority of cached data that can enter the cache server. This study simulated cached data prioritization based on an objective function as a characteristic of problem-solving using an optimization approach. The objective function of web caching is formulated based on the variable data size, count access, and frequency-time access. Then we use the knapsack problem method to find the optimal solution. The Simulations run three swarm intelligence algorithms Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO), divided into several scenarios. The simulation results show that the GA algorithm relatively stable and fast to convergence. The ACO algorithm has the advantage of a non-random initial solution but has followed the pheromone trail. The BPSO algorithm is the fastest, but the resulting solution quality is not as good as ACO and GA.

Due to broadcast nature of acoustic signal, underwater acoustic sensor networks face security challenge. In the paper, we propose a physical layer security transmission scheme with cooperative jamming. The proposed scheme takes advantage of the long propagation delay of the underwater acoustic channel to interfere with eavesdropper without affecting the reception of intended users. The results of both simulation and field experiment show that the proposed mechanism can improve the secrecy capacity of the network and effectively jam eavesdropper.

Security databases such as Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) maintain diverse high-quality security concepts, which are treated as security entities. Meanwhile, security entities are documented with many potential relation types that profit for security analysis and comprehension across these three popular databases. To support reasoning security entity relationships, translation-based knowledge graph representation learning treats each triple independently for the entity prediction. However, it neglects the important semantic information about the neighbor entities around the triples. To address it, we propose a text-enhanced graph attention network model (text-enhanced GAT). This model highlights the importance of the knowledge in the 2-hop neighbors surrounding a triple, under the observation of the diversity of each entity. Thus, we can capture more structural and textual information from the knowledge graph about the security databases. Extensive experiments are designed to evaluate the effectiveness of our proposed model on the prediction of security entity relationships. Moreover, the experimental results outperform the state-of-the-art by Mean Reciprocal Rank (MRR) 0.132 for detecting the missing relationships.

This paper considers a multivariate quickest detection problem with false data injection (FDI) attacks in internet of things (IoT) systems. We derive a sequential generalized likelihood ratio test (GLRT) for zero-mean Gaussian FDI attacks. Exploiting the fact that covariance matrices are positive, we propose strategies to detect positive semi-definite matrix additions rather than arbitrary changes in the covariance matrix. The distribution of the GLRT is only known asymptotically whereas quickest detectors deal with short sequences, thereby leading to loss of performance. Therefore, we use a finite-sample correction to reduce the false alarm rate. Further, we provide a numerical approach to estimate the threshold sequences, which are analytically intractable to compute. We also compare the average detection delay of the proposed detector for constant and varying threshold sequences. Simulations showed that the proposed detector outperforms the standard sequential GLRT detector.

Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%.

Anomalous users detection in social network is an imperative task for security problems. Motivated by the great power of Graph Neural Networks(GNNs), many current researches adopt GNN-based detectors to reveal the anomalous users. However, the increasing scale of social activities, explosive growth of users and manifold technical disguise render the user detection a difficult task. In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model (RAU-GNN) to obtain a fine-grained detection result. RAU-GNN first extracts multiple relations of all types of users in social network, including both benign and anomalous users, and accordingly constructs the multiple user relation graph. Secondly, we employ relevance-aware GNN framework to learn the hidden features of users, and discriminate the anomalous users after discriminating. Concretely, by integrating Graph Convolution Network(GCN) and Graph Attention Network(GAT), we design a GCN-based relation fusion layer to aggregate initial information from different relations, and a GAT-based embedding layer to obtain the high-level embeddings. Lastly, we feed the learned representations to the following GNN layer in order to consolidate the node embedding by aggregating the final users' embeddings. We conduct extensive experiment on real-world datasets. The experimental results show that our approach can achieve high accuracy for anomalous users detection.

In order to increase the availability and reliability of photovoltaic (PV) systems, fault diagnosis and condition monitoring of inverters are of crucial means to meet the goals. Numerous methods are implemented for fault diagnosis of PV inverters, providing robust features and handling massive amount of data. However, existing methods rely on simplistic frameworks that are incapable of inspecting a wide range of intrinsic and explicit features, as well as being time-consuming. In this paper, a novel method based on a multilayer deep belief network (DBN) is suggested for fault diagnosis, which allows the framework to discover the probabilistic reconstruction across its inputs. This approach equips a robust hierarchical generative model for exploiting features associated with faults, interprets functions that are highly variable, and needs lesser prior information. Moreover, the method instantaneously categorizes the fault conditions, which eventually strengthens the adaptability of applying it on a variety of diagnostic problems in an inverter domain. The proposed method is evaluated using multiple input signals at different sampling frequencies. To evaluate the efficacy of DBN, a test model based on a three-phase 2-level grid-tied PV inverter was used. The results show that the method is capable of achieving precise diagnosis operations.

Affected by the inherent ideas like "Focus on Function Realization, Despise Security Protection", there are lots of hidden threats in the industrial control system of wind farms (ICS-WF), such as unreasonable IP configuration, failure in virus detection and killing, which are prone to illegal invasion and attack from the cyberspace. Those unexpected unauthorized accesses are quite harmful for the stable operation of the wind farms and regional power grid. Therefore, by investigating the current security situation and needs of ICS-WF, analyzing the characteristics of ICS-WF’s architecture and internal communication, and integrating the ideas of the classified protection of cybersecurity, this paper proposes a new customized cybersecurity strategy for ICS-WF based on the barrel theory. We also introduce an new anomalous intrusion detection technology for ICS-WF, which is developed based on statistical models of wind farm network characteristics. Finally, combined all these work with the network security offense and defense drill in the industrial control safety simulation laboratory of wind farms, this research formulates a three-dimensional comprehensive protection solution for ICS-WF, which significantly improves the cybersecurity level of ICS-WF.

With the spread of the Internet, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. The first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device at the end. To address these issues, fog computing, which is located in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. The main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk measures by evaluating the risk values. The proposed risk countermeasures for fog computing should help us to utilize IoT devices in a safe and secure manner.

In recent years, new cyber attacks such as targeted attacks have caused extensive damage. With the continuing development of the IoT society, various devices are now connected to the network and are being used for various purposes. The Internet of Things has the potential to link cyber risks to actual property damage, as cyberspace risks are connected to physical space. With this increase in unknown cyber risks, the demand for cyber insurance is increasing. One of the most serious emerging risks is the silent cyber risk, and it is likely to increase in the future. However, at present, security measures against silent cyber risks are insufficient. In this study, we conducted a risk management of silent cyber risk for organizations with the objective of contributing to the development of risk management methods for new cyber risks that are expected to increase in the future. Specifically, we modeled silent cyber risk by focusing on state transitions to different risks. We newly defined two types of silent cyber risk, namely, Alteration risk and Combination risk, and conducted risk assessment. Our assessment identified 23 risk factors, and after analyzing them, we found that all of them were classified as Risk Transference. We clarified that the most effective risk countermeasure for Alteration risk was insurance and for Combination risk was measures to reduce the impact of the risk factors themselves. Our evaluation showed that the silent cyber risk could be reduced by about 50%, thus demonstrating the effectiveness of the proposed countermeasures.