Biblio

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.

Per the National Energy Policy, Demand Side Management (DSM) is one of the energy conservations that performs a function to manage electric power of demand-side resources. One of the DSM solutions is a demand response program, which is a part of Thailand Smart Grid Action Plan 2017 - 2021. Demand response program such as peak demand reduction plays a role in both the management of the electricity crisis and enhance energy security. This paper presents a pilot project for a fully automated demand response program at MEA Rat Burana District Office. The system is composed of a Building Energy Management System (BEMS) with Demand Response Client gateway and 5 energy controllers at the air conditioner by using the OpenADR2.0b protocol. Also, this concept leads to automatic or semi-automatic demand response program in the future. The result shows the total energy consumption reduction for air conditioners by 53.5%. The future works to be carried out are to implement into other MEA District Office such as Khlong Toei, Yan Nawa and Bang Khun Thian and to test with a Load Aggregator Management System (LAMS).

Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.

Blockchain is becoming more popular because of its decentralized, secured, and transparent nature. Supply chain and its management is indispensable to improve customer services, reduce operating costs and improve financial position of a firm. Integration of blockchain and supply chain is substantial, but it alone is not enough for the sustainability of supply chain systems. The proposed mechanism speaks about the method of rewarding the supply chain parties with incentives so as to improve the security and make the integration of supply chain with blockchain sustainable. The proposed incentive mechanism employs the co-operative approach of game theory where all the supply chain parties show a cooperative behavior of following the blockchain-based supply chain protocols and also this mechanism makes a fair attempt in rewarding the supply chain parties with incentives.

Smart grid is the key infrastructure of the country, and its network security is an important link to ensure the national important infrastructure security. SOC as a secure operation mechanism for adaptive and continuous improvement of information security, it is practically significant to address the challenge to the network security of the smart grid. Based on the analysis of the technical characteristics and security of smart grid, and taking a grid enterprise smart grid as an example, we propose the design scheme and implementation plan of smart grid SOC platform. Experimental results show that the platform we designed can meet the performance requirements, it also meets the requirements of real-time storage of behavioral data and provides support for interactive analysis and batch analysis.

Stochastic computing is based on probability concepts which are different from conventional mathematical operations. Advantages of stochastic computing in the fields of neural networks and digital image processing have been reported in literature recently. Arithmetic operations especially multiplications can be performed either by logical AND gates in unipolar format or by EXNOR gates in bipolar format in stochastic computation. Stochastic computing is inherently fault-tolerant and requires fewer logic gates to implement arithmetic operations. Long computing time and low accuracy are the main drawbacks of this system. In this presentation, to reduce hardware requirement and delay, modified stochastic multiplication using AND gate array and multiplexer are used for the design of Finite Impulse Response Filter cores. Performance parameters such as area, power and delay for FIR filter using modified stochastic computing methods are compared with conventional floating point computation.

Low-power wireless networks provide IPv6 connectivity through 6LoWPAN, a set of standards to aggressively compress IPv6 packets over small maximum transfer unit (MTU) links such as 802.15.4.The entire purpose of IP was to interconnect different networks, but we find that different 6LoWPAN implementations fail to reliably communicate with one another. These failures are due to stacks implementing different subsets of the standard out of concern for code size. We argue that this failure stems from 6LoWPAN's design, not implementation, and is due to applying traditional Internet protocol design principles to low- power networks.We propose three design principles for Internet protocols on low-power networks, designed to prevent similar failures in the future. These principles are based around the importance of providing flexible tradeoffs between code size and energy efficiency. We apply these principles to 6LoWPAN and show that the modified protocol provides a wide range of implementation strategies while allowing implementations with different strategies to reliably communicate.

The current state of the integrated circuit (IC) ecosystem is that only a handful of foundries are at the forefront, continuously pushing the state of the art in transistor miniaturization. Establishing and maintaining a FinFET-capable foundry is a billion dollar endeavor. This scenario dictates that many companies and governments have to develop their systems and products by relying on 3rd party IC fabrication. The major caveat within this practice is that the procured silicon cannot be blindly trusted: a malicious foundry can effectively modify the layout of the IC, reverse engineer its IPs, and overproduce the entire chip. The Hardware Security community has proposed many countermeasures to these threats. Notably, obfuscation has gained a lot of traction - here, the intent is to hide the functionality from the untrusted foundry such that the aforementioned threats are hindered or mitigated. In this paper, we summarize the research efforts of three independent research groups towards achieving trustworthy ICs, even when fabricated in untrusted offshore foundries. We extensively address the use of logic locking and its many variants, as well as the use of high-level synthesis (HLS) as an obfuscation approach of its own.

To solve the problem of poor security and performance caused by traditional encryption algorithm in the cloud data storage of power bidding system, we proposes a hybrid encryption method based on symmetric encryption and asymmetric encryption. In this method, firstly, the plaintext upload file is divided into several blocks according to the proportion, then the large file block is encrypted by symmetrical encryption algorithm AES to ensure the encryption performance, and then the small file block and AES key are encrypted by asymmetric encryption algorithm ECC to ensure the file encryption strength and the security of key transmission. Finally, the ciphertext file is generated and stored in the cloud storage environment to prevent sensitive files Pieces from being stolen and destroyed. The experimental results show that the hybrid encryption method can improve the anti-attack ability of cloud storage files, ensure the security of file storage, and have high efficiency of file upload and download.

Home automation is an intelligent, functional as a unit system that facilitates home processes without unnecessarily complicating the user's life. Devices can be connected, which in turn connect and talk through a centralized control unit, which are accessible via mobile phones. These devices include lights, appliances, security systems, alarms and many other sensors and devices. This paper presents the design and implementation of a Bluetooth based smart home automation system which uses a Peripheral interface controller (PIC) microcontroller (16F1937) as the main processer and the appliances are connected to the peripheral ports of the microcontroller via relays. The circuit in the project was designed in Diptrace software. The PCB layout design was completed. The fully functional smart home prototype was built and demonstrated to functional.

The current network security situation of the electric power dispatching and control system is becoming more and more severe. On the basis of the original network security management platform, to increase the collection of network security data information and improve the network security analysis ability, this article proposes the electric power dispatching and control system network security situation awareness analysis module. The perception layer accesses multi-source heterogeneous data sources. Upwards through the top layer, data standardization will be introduced, who realizes data support for security situation analysis, and forms an association mapping with situation awareness elements such as health situation, attack situation, behavior situation, and operation situation. The overall effect is achieving the construction goals of "full control of equipment status, source of security attacks can be traced, operational risks are identifiable, and abnormal behaviors can be found.".

The strength of the substitution box (S-box) determines the security of the cryptographic algorithm because it's the only nonlinear component in the block cipher. Because of the disadvantages of non-uniformity sequence and limited range in the one-dimension (1D) chaotic map, this paper constructs the logistic map and the sine map into a combined chaotic map, and a new S-box construction method based on this combined chaotic map is presented. Performance tests were performed on the S-box, including nonlinearity, linear probability, differential probability, strict avalanche criterion, bits independence criterion. Compared with others S-box, this result indicates that the S-box has more excellent cryptographic performance and can be used as a nonlinear component in the lightweight block cipher algorithm.

In the Ubiquitous Electric Internet of Things (UEIoT), the terminals are very easy to be accessed and attacked by attackers due to the lack of effective monitoring and safe isolation methods. Therefore, in the implementation of UEIoT, the security protection of terminals is particularly important. Therefore, this paper proposes a dual-system design scheme for terminal active immunity based on trusted computing. In this scheme, the terminal node in UEIoT is composed of two parts: computing part and trusted protection part. The computing component and the trusted protection component are logically independent of each other, forming a trusted computing active immune dual-system structure with both computing and protection functions. The Trusted Network Connection extends the trusted state of the terminal to the network, thus providing a solution for terminal secure access in the UEIoT.

In order to significantly improve the reliable interaction and fast processing when TT&C(Tracking, Telemetry and Command) Resource Scheduling and Management System (TRSMS) communicate with external systems which are diverse, multiple directional and high concurrent, this paper designs and implements a highly concurrent and secure middleware for TT&C Resource Automatic Scheduling Interface (TRASI). The middleware designs memory pool, data pool, thread pool and task pool to improve the efficiency of concurrent processing, uses the rule dictionary, communication handshake and wait retransmission mechanism to ensure the data interaction security and reliability. This middleware can effectively meet the requirements of TRASI for data exchange with external users and system, significantly improve the data processing speed and efficiency, and promote the information technology and automation level of Aerospace TT&C Network Management Center (TNMC).

Software applications continue to challenge user privacy when users interact with them. Privacy practices (e.g. Data Minimisation (DM), Privacy by Design (PbD) or General Data Protection Regulation (GDPR)) and related “privacy engineering” methodologies exist and provide clear instructions for developers to implement privacy into software systems they develop that preserve user privacy. However, those practices and methodologies are not yet a common practice in the software development community. There has been no previous research focused on developing “educational” interventions such as serious games to enhance software developers' coding behaviour. Therefore, this research proposes a game design framework as an educational tool for software developers to improve (secure) coding behaviour, so they can develop privacy-preserving software applications that people can use. The elements of the proposed framework were incorporated into a gaming application scenario that enhances the software developers' coding behaviour through their motivation. The proposed work not only enables the development of privacy-preserving software systems but also helping the software development community to put privacy guidelines and engineering methodologies into practice.

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers. Compared to the previous work, CNN can learn appropriate decision functions from the original state information without preprocessing through artificially designed rules, thereby alleviating the dependence on complex pre-designed models. Simulation results demonstrate that the proposed CNN-based approach can effectively improve the performance of detecting and localizing malicious agents, as compared with the conventional pre-designed score-based model.

In many organizations, permissioned blockchain networks are currently transitioning from a proof-of-concept stage to production use. A crucial part of this transition is ensuring awareness of potential threats to network operations. Due to the plethora of software components involved in distributed ledgers, threats may be difficult or impossible to detect without a structured monitoring approach. To this end, we conduct a survey of attacks on permissioned blockchains and develop a set of threat indicators. To gather these indicators, a data processing pipeline is proposed to aggregate log information from relevant blockchain components, enriched with data from external sources. To evaluate the feasibility of monitoring current blockchain frameworks, we determine relevant data sources in Hyperledger Fabric. Our results show that the required data is mostly available, but also highlight significant improvement potential with regard to threat intelligence, chaincode scanners and built-in metrics.

This article discusses the problem of detecting cross-site scripting (XSS) using machine learning methods. XSS is an attack in which malicious code is embedded on a page to interact with an attacker’s web server. The XSS attack ranks third in the ranking of key web application risks according to Open Source Foundation for Application Security (OWASP). This attack has not been studied for a long time. It was considered harmless. However, this is fallacious: the page or HTTP Cookie may contain very vulnerable data, such as payment document numbers or the administrator session token. Machine learning is a tool that can be used to detect XSS attacks. This article describes an experiment. As a result the model for detecting XSS attacks was created. Following machine learning algorithms are considered: the support vector method, the decision tree, the Naive Bayes classifier, and Logistic Regression. The accuracy of the presented methods is made a comparison.

On the current website, there are many undeniable conditions and there is the existence of new plot holes. If data link is normally extracted on each of the websites, it becomes difficult to evaluate each vulnerability, with tolls such as XS S, SQLI, and other such existing tools for vulnerability assessment. Integrated testing criteria for vulnerabilities are met. In addition, the response should be automated and systematic. The primary value of vulnerability Buffer will be made of predefined and self-formatted code written in python, and the software is automated to send reports to their respective users. The vulnerabilities are tried to be classified as accessible. OWASP is the main resource for developing and validating web security processes.

Machine learning and Artificial Intelligent techniques are the most used techniques. It gives opportunity to online sharing market where sharing and adopting model is being popular. It gives attackers many new opportunities. Deep neural network is the most used approached for artificial techniques. In this paper we are presenting a Proof of Concept method to detect Trojan attacks on the Deep Neural Network. Deploying trojan models can be dangerous in normal human lives (Application like Automated vehicle). First inverse the neuron network to create general trojan triggers, and then retrain the model with external datasets to inject Trojan trigger to the model. The malicious behaviors are only activated with the trojan trigger Input. In attack, original datasets are not required to train the model. In practice, usually datasets are not shared due to privacy or copyright concerns. We use five different applications to demonstrate the attack, and perform an analysis on the factors that affect the attack. The behavior of a trojan modification can be triggered without affecting the test accuracy for normal input datasets. After generating the trojan trigger and performing an attack. It's applying SHAP as defense against such attacks. SHAP is known for its unique explanation for model predictions.

As the configuration used for the Mobile Ad hoc Networks (MANET) does not have a fixed infrastructure as well, the mechanism varies for each MANET. The finding of the route in this mechanism also varies because it does not have any fixed path route for routing as well every node in this structure behaves like a base station. MANET has such freedom for its creation, so it also faces various types of attacks on it. Some of the attacks are a black hole, warm hole etc. The researchers have provided various methods to prevent warm hole attacks, as the warm hole attack is seen as difficult to prevent. So here a mechanism is proposed to detect and prevent the warm hole attack using the AODV protocol which is based on trust calculation. In our method, the multiple path selection is used for finding the best path for routing. The path is tested for the warm hole attack, as the node is detected the data packet sent in between the source and destination selects the path from the multi-paths available and the packet delivery is improved. The packet delivery ratio (PDR) is calculated for the proposed mechanism, and the results have improved the PDR by 71.25%, throughput by 74.09 kbps, and the E to E delay is decreased by 57.92ms for the network of 125 nodes.

Mobile antivirus technologies incorporate static analysis which involves the analysis of programs without its execution. This process relies on pattern matching against a signature repository to identify malware, which can be easily tricked by transformation techniques such as obfuscation. Obfuscation as an evasion technique renders character strings disguised and incomprehensive, to prevent tampering and reengineering, which poses to be a valuable technique malware developers adopt to evade detection. This paper attempts to study the detection efficiency of static analyzers against obfuscated Android malware. This study is the first step in a larger project attempting to improve the efficiency of malware detectors.

Digital signatures are suitable technology for public key encryption. Acceptance (non-repudiation) of digital messages and data origin authentication are one of the main usage of digital signature. Digital signature's security mainly depends on the keys (public and private). These keys are used to generate and validate digital signatures. In digital signature signing process is performed using signer's secret key. However, any attacker can present a blinded version of message encrypted with signer's public key and can get the original message. Therefore, this paper proposed a novel method to identify blinded version of digital signature. The proposed method has been tested mathematically and found to be more efficient to detect blind signatures.

In the area of cloud security, detection of DDoS attack is a challenging task such that legitimate users use the cloud resources properly. So in this paper, detection and classification of the attacking packets and normal packets are done by using various machine learning classifiers. We have selected the most relevant features from NSL KDD dataset using five (Information gain, gain ratio, chi-squared, ReliefF, and symmetrical uncertainty) commonly used feature selection methods. Now from the entire selected feature set, the most important features are selected by applying our hybrid feature selection method. Since all the anomalous instances of the dataset do not belong to DDoS category so we have separated only the DDoS packets from the dataset using the selected features. Finally, the dataset has been prepared and named as KDD DDoS dataset by considering the selected DDoS packets and normal packets. This KDD DDoS dataset has been discretized using discretize tool in weka for getting better performance. Finally, this discretize dataset has been applied on some commonly used (Naive Bayes, Bayes Net, Decision Table, J48 and Random Forest) classifiers for determining the detection rate of the classifiers. 10 fold cross validation has been used here for measuring the robustness of the system. To measure the efficiency of our hybrid feature selection method, we have also applied the same set of classifiers on the NSL KDD dataset, where it gives the best anomaly detection rate of 99.72% and average detection rate 98.47% similarly, we have applied the same set of classifiers on NSL DDoS dataset and obtain the average DDoS detection of 99.01% and the best DDoS detection rate of 99.86%. In order to compare the performance of our proposed hybrid method, we have also applied the existing feature selection methods and measured the detection rate using the same set of classifiers. Finally, we have seen that our hybrid approach for detecting the DDoS attack gives the best detection rate compared to some existing methods.

Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation.