Biblio
The traditional logistics transaction lacks a perfect traceability mechanism, and the data information's integrity and safety are not guaranteed in the existing traceability system. In order to solve the problem of main body responsibility caused by the participation of many stakeholders and the uncompleted supervision system in the process of logistics service transaction, This paper proposes a traceability algorithm for logistics service transactions based on blockchain. Based on the logistics service supply chain and alliance chain, the paper firstly investigates the traditional logistics service supply chain, analyzes the existing problems, and combines the structural characteristics of the blockchain to propose a decentralized new logistics service supply chain concept model based on blockchain. Then, using Globe sandara 1 to standardize the physical products and data circulating in the new logistics service supply chain, form unified and standard traceable data, and propose a multi-dimensional traceable data model based on logistics service supply chain. Based on the proposed model, combined with the business process of the logistics service supply chain and asymmetric encryption, a blockchain-based logistics service transaction traceability algorithm is designed. Finally, the simulation results show that the algorithm realizes the end-to-end traceability of the logistics service supply chain, and the service transaction is transparent while ensuring the integrity and security of the data.
Supply chain management (SCM) is fundamental for gaining financial, environmental and social benefits in the supply chain industry. However, traditional SCM mechanisms usually suffer from a wide scope of issues such as lack of information sharing, long delays for data retrieval, and unreliability in product tracing. Recent advances in blockchain technology show great potential to tackle these issues due to its salient features including immutability, transparency, and decentralization. Although there are some proof-of-concept studies and surveys on blockchain-based SCM from the perspective of logistics, the underlying technical challenges are not clearly identified. In this paper, we provide a comprehensive analysis of potential opportunities, new requirements, and principles of designing blockchain-based SCM systems. We summarize and discuss four crucial technical challenges in terms of scalability, throughput, access control, data retrieval and review the promising solutions. Finally, a case study of designing blockchain-based food traceability system is reported to provide more insights on how to tackle these technical challenges in practice.
The United States and European Union have an increasing number of projects that are engaging end-use devices for improved grid capabilities. Areas such as building-to-grid and vehicle-to-grid are simple examples of these advanced capabilities. In this paper, we present an innovative concept study for a ship-to-grid integration. The goal of this study is to simulate a two-way power flow between ship(s) and the grid with GridLAB-D for the port of Kyllini in Greece, where a ship-to-shore interconnection was recently implemented. Extending this further, we explore: (a) the ability of ships to meet their load demand needs, while at berth, by being supplied with energy from the electric grid and thus powering off their diesel engines; and (b) the ability of ships to provide power to critical loads onshore. As a result, the ship-to-grid integration helps (a) mitigate environmental pollutants from the ships' diesel engines and (b) provide resilience to nearby communities during a power disruption due to natural disasters or man-made threats.
Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.
A 2D-Compressive Sensing and hyper-chaos based image compression-encryption algorithm is proposed. The 2D image is compressively sampled and encrypted using two measurement matrices. A chaos based measurement matrix construction is employed. The construction of the measurement matrix is controlled by the initial and control parameters of the chaotic system, which are used as the secret key for encryption. The linear measurements of the sparse coefficients of the image are then subjected to a hyper-chaos based diffusion which results in the cipher image. Numerical simulation and security analysis are performed to verify the validity and reliability of the proposed algorithm.
In the last few decades, the relative simplicity of the logistic map made it a widely accepted point in the consideration of chaos, which is having the good properties of unpredictability, sensitiveness in the key values and ergodicity. Further, the system parameters fit the requirements of a cipher widely used in the field of cryptography, asymmetric and symmetric key chaos based cryptography, and for pseudorandom sequence generation. Also, the hardware-based embedded system is configured on FPGA devices for high performance. In this paper, a novel stream cipher using chaotic logistic map is proposed. The two chaotic logistic maps are coded using Verilog HDL and implemented on commercially available FPGA hardware using Xilinx device: XC3S250E for the part: FT256 and operated at frequency of 62.20 MHz to generate the non-recursive key which is used in key scheduling of pseudorandom number generation (PRNG) to produce the key stream. The realization of proposed cryptosystem in this FPGA device accomplishes the improved efficiency equal to 0.1186 Mbps/slice. Further, the generated binary sequence from the experiment is analyzed for X-power, thermal analysis, and randomness tests are performed using NIST statistical.
Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.
With the exponential hike in cyber threats, organizations are now striving for better data mining techniques in order to analyze security logs received from their IT infrastructures to ensure effective and automated cyber threat detection. Machine Learning (ML) based analytics for security machine data is the next emerging trend in cyber security, aimed at mining security data to uncover advanced targeted cyber threats actors and minimizing the operational overheads of maintaining static correlation rules. However, selection of optimal machine learning algorithm for security log analytics still remains an impeding factor against the success of data science in cyber security due to the risk of large number of false-positive detections, especially in the case of large-scale or global Security Operations Center (SOC) environments. This fact brings a dire need for an efficient machine learning based cyber threat detection model, capable of minimizing the false detection rates. In this paper, we are proposing optimal machine learning algorithms with their implementation framework based on analytical and empirical evaluations of gathered results, while using various prediction, classification and forecasting algorithms.
The main security problems, typical for the Internet of Things (IoT), as well as the purpose of gaining unauthorized access to the IoT, are considered in this paper. Common characteristics of the most widespread botnets are provided. A method to detect compromised IoT devices included into a botnet is proposed. The method is based on a model of logistic regression. The article describes a developed model of logistic regression which allows to estimate the probability that a device initiating a connection is running a bot. A list of network protocols, used to gain unauthorized access to a device and to receive instructions from common and control (C&C) server, is provided too.
An important source of cyber-attacks is malware, which proliferates in different forms such as botnets. The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies or industries. It attempts to infect as many connected devices as possible, using their resources for automated tasks that may cause significant economic and social harm while being hidden to the user and device. Thus, it becomes very difficult to detect such activity. A considerable amount of research has been conducted to detect and prevent botnet infestation. In this paper, we attempt to create a foundation for an anomaly-based intrusion detection system using a statistical learning method to improve network security and reduce human involvement in botnet detection. We focus on identifying the best features to detect botnet activity within network traffic using a lightweight logistic regression model. The network traffic is processed by Bro, a popular network monitoring framework which provides aggregate statistics about the packets exchanged between a source and destination over a certain time interval. These statistics serve as features to a logistic regression model responsible for classifying malicious and benign traffic. Our model is easy to implement and simple to interpret. We characterized and modeled 8 different botnet families separately and as a mixed dataset. Finally, we measured the performance of our model on multiple parameters using F1 score, accuracy and Area Under Curve (AUC).
Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.
At a time when all it takes to open a Twitter account is a mobile phone, the act of authenticating information encountered on social media becomes very complex, especially when we lack measures to verify digital identities in the first place. Because the platform supports anonymity, fake news generated by dubious sources have been observed to travel much faster and farther than real news. Hence, we need valid measures to identify authors of misinformation to avert these consequences. Researchers propose different authorship attribution techniques to approach this kind of problem. However, because tweets are made up of only 280 characters, finding a suitable authorship attribution technique is a challenge. This research aims to classify authors of tweets by comparing machine learning methods like logistic regression and naive Bayes. The processes of this application are fetching of tweets, pre-processing, feature extraction, and developing a machine learning model for classification. This paper illustrates the text classification for authorship process using machine learning techniques. In total, there were 46,895 tweets used as both training and testing data, and unique features specific to Twitter were extracted. Several steps were done in the pre-processing phase, including removal of short texts, removal of stop-words and punctuations, tokenizing and stemming of texts as well. This approach transforms the pre-processed data into a set of feature vector in Python. Logistic regression and naive Bayes algorithms were applied to the set of feature vectors for the training and testing of the classifier. The logistic regression based classifier gave the highest accuracy of 91.1% compared to the naive Bayes classifier with 89.8%.
This paper describes a novel aerospace electronic component risk assessment methodology and supporting virtual laboratory structure designed to augment existing supply chain management practices and aid in Microelectronics Trust Assurance. This toolkit and methodology applies structure to the unclear and evolving risk assessment problem, allowing quantification of key risks affecting both advanced and obsolete systems that rely on semiconductor technologies. The impacts of logistics & supply chain risk, technology & counterfeit risk, and faulty component risk on trusted and non-trusted procurement options are quantified. The benefits of component testing on part reliability are assessed and incorporated into counterfeit mitigation calculations. This toolkit and methodology seek to assist acquisition staff by providing actionable decision data regarding the increasing threat of counterfeit components by assessing the risks faced by systems, identifying mitigation strategies to reduce this risk, and resolving these risks through the optimal test and procurement path based on the component criticality risk tolerance of the program.
In several critical military missions, more than one decision level are involved. These decision levels are often independent and distributed, and sensitive pieces of information making up the military mission must be kept hidden from one level to another even if all of the decision levels cooperate to accomplish the same task. Usually, a mission is negotiated through insecure networks such as the Internet using cryptographic protocols. In such protocols, few security properties have to be ensured. However, designing a secure cryptographic protocol that ensures several properties at once is a very challenging task. In this paper, we propose a new secure protocol for multipart military missions that involve two independent and distributed decision levels having different security levels. We show that it ensures the secrecy, authentication, and non-repudiation properties. In addition, we show that it resists against man-in-the-middle attacks.
There are several security requirements identification methods proposed by researchers in up-front requirements engineering (RE). However, in open source software (OSS) projects, developers use lightweight representation and refine requirements frequently by writing comments. They also tend to discuss security aspect in comments by providing code snippets, attachments, and external resource links. Since most security requirements identification methods in up-front RE are based on textual information retrieval techniques, these methods are not suitable for OSS projects or just-in-time RE. In our study, we propose a new model based on logistic regression to identify security requirements in OSS projects. We used five metrics to build security requirements identification models and tested the performance of these metrics by applying those models to three OSS projects. Our results show that four out of five metrics achieved high performance in intra-project testing.
This paper introduces an ensemble model that solves the binary classification problem by incorporating the basic Logistic Regression with the two recent advanced paradigms: extreme gradient boosted decision trees (xgboost) and deep learning. To obtain the best result when integrating sub-models, we introduce a solution to split and select sets of features for the sub-model training. In addition to the ensemble model, we propose a flexible robust and highly scalable new scheme for building a composite classifier that tries to simultaneously implement multiple layers of model decomposition and outputs aggregation to maximally reduce both bias and variance (spread) components of classification errors. We demonstrate the power of our ensemble model to solve the problem of predicting the outcome of Hearthstone, a turn-based computer game, based on game state information. Excellent predictive performance of our model has been acknowledged by the second place scored in the final ranking among 188 competing teams.
Emerging communication technologies in distributed network systems require transfer of biometric digital images with high security. Network security is identified by the changes in system behavior which is either Dynamic or Deterministic. Performance computation is complex in dynamic system where cryptographic techniques are not highly suitable. Chaotic theory solves complex problems of nonlinear deterministic system. Several chaotic methods are combined to get hyper chaotic system for more security. Chaotic theory along with DNA sequence enhances security of biometric image encryption. Implementation proves the encrypted image is highly chaotic and resistant to various attacks.
In recent years, the chaos based cryptographic algorithms have enabled some new and efficient ways to develop secure image encryption techniques. In this paper, we propose a new approach for image encryption based on chaotic maps in order to meet the requirements of secure image encryption. The chaos based image encryption technique uses simple chaotic maps which are very sensitive to original conditions. Using mixed chaotic maps which works based on simple substitution and transposition techniques to encrypt the original image yields better performance with less computation complexity which in turn gives high crypto-secrecy. The initial conditions for the chaotic maps are assigned and using that seed only the receiver can decrypt the message. The results of the experimental, statistical analysis and key sensitivity tests show that the proposed image encryption scheme provides an efficient and secure way for image encryption.
Image encryption takes been used by armies and governments to help top-secret communication. Nowadays, this one is frequently used for guarding info among various civilian systems. To perform secure image encryption by means of various chaotic maps, in such system a legal party may perhaps decrypt the image with the support of encryption key. This reversible chaotic encryption technique makes use of Arnold's cat map, in which pixel shuffling offers mystifying the image pixels based on the number of iterations decided by the authorized image owner. This is followed by other chaotic encryption techniques such as Logistic map and Tent map, which ensures secure image encryption. The simulation result shows the planned system achieves better NPCR, UACI, MSE and PSNR respectively.