Biblio

Web server is an appealing target for attackers since it may be exploited to gain access to an organization’s internal network. After compromising a web server, the attacker can construct a webshell to maintain a long-term and stealthy access for further attacks. Among all webshell-based attacks, command injection is a powerful attack that can be launched to steal sensitive data from the web server or compromising other computers in the network. To monitor and analyze webshell-based command injection, we develop a hybrid webshell honeypot framework called HoneyBog, which intercepts and redirects malicious injected commands from the front-end honeypot to the high-fidelity back-end honeypot for execution. HoneyBog can achieve two advantages by using the client-server honeypot architecture. First, since the webshell-based injected commands are transferred from the compromised web server to a remote constrained execution environment, we can prevent the attacker from launching further attacks in the protected network. Second, it facilitates the centralized management of high-fidelity honeypots for remote honeypot service providers. Moreover, we increase the system fidelity of HoneyBog by synchronizing the website files between the front-end and back-end honeypots. We implement a prototype of HoneyBog using PHP and the Apache web server. Our experiments on 260 PHP webshells show that HoneyBog can effectively intercept and redirect injected commands with a low performance overhead.

Modern electric power systems are complex cyber-physical systems. The integration of traditional power and digital technologies result in interdependencies that need to be considered in risk analysis. In this paper we argue the need for analysis methods that can combine the competencies of various experts in a common analysis focusing on the overall system perspective. We report on our experiences on using the Vulnerability Analysis Framework (VAF) and bow-tie diagrams in a combined analysis of the power and cyber security aspects in a realistic case. Our experiences show that an extended version of VAF with increased support for interdependencies is promising for this type of analysis.

A new methodology to calculate the hysteresis induced power loss of inductor from the measured waveforms of DC-to-DC converter during the voltage conversion is presented. From this study, we find that the duty cycles (D) of the buck and boost converters used till date for inductance current calculation are not exactly equal to VOUT/VIN and 1-VIN/VOUT as the inductance change induced by the hysteresis effect cannot be neglected. Although the increase in the loading currents of the converter increases the remanence magnetization of inductor at the turn-off time (toff), this remanence magnetization is destroyed by the turbulence induced vortex current at the transistor turn-on transient. So, the core power loss of inductor increases with the loading current of the converter and becomes much larger than other power losses and cannot be neglected for the power efficiency calculation during power stage design.

Network Address Translation (NAT) is an address remapping technique placed at the borders of stub domains. It is present in almost all routers and CPEs. Most NAT devices implement Port Address Translation (PAT), which allows the mapping of multiple private IP addresses to one public IP address. Based on port number information, PAT matches the incoming traffic to the corresponding "hidden" client. In an enterprise context, and with the proliferation of unauthorized wired and wireless NAT routers, NAT can be used for re-distributing an Intranet or Internet connection or for deploying hidden devices that are not visible to the enterprise IT or under its oversight, thus causing a problem known as shadow IT. Thus, it is important to detect NAT devices in an intranet to prevent this particular problem. Previous methods in identifying NAT behavior were based on features extracted from traffic traces per flow. In this paper, we propose a method to identify NAT devices using a machine learning approach from aggregated flow features. The approach uses multiple statistical features in addition to source and destination IPs and port numbers, extracted from passively collected traffic data. We also use aggregated features extracted within multiple window sizes and feed them to a machine learning classifier to study the effect of timing on NAT detection. Our approach works completely passively and achieves an accuracy of 96.9% when all features are utilized.

Routing protocol for low power and lossy networks (RPL) is a fundamental routing protocol of 6LoWPAN, a centre correspondence standard for the Internet of Things. RPL outplay other wireless sensor and ad hoc routing protocols in the aspect of service (QoS), device management, and energy-saving performance. The Rank definition in RPL addresses several issues, such as path optimization, loop avoidance, and power overhead management. RPL rank and version number attacks are two types of the most common forms of RPL attacks, may have crucial ramification for RPL networks. The research directed upon these attacks includes considerable vulnerabilities and efficiency issues. The rank attack on sensor networks is perhaps the utmost common, posing a challenge to network connectivity by falling data or disrupting routing routes. This work presents a rank attack detection system focusing on RPL. Considering many of such issues a method has been proposed using spatial correlation function (SCF) and Dijkstra's algorithm considering parameters like energy and throughput.

Aiming at ensure the security and self-control of heterogeneous alliance network, this paper proposes a novel structure of identity authentication based on domestic commercial cryptography with blockchain in the heterogeneous alliance network. The domestic commercial cryptography, such as SM2, SM3, SM4, SM9 and ZUC, is adopted to solve the encryption, decryption, signature and verification of blockchain, whose key steps of data layer are solved by using domestic commercial cryptographic algorithms. In addition, it is the distributed way to produce the public key and private key for the security of the keys. Therefore, the cross domain identity authentication in the heterogeneous alliance network can be executed safely and effectively.

Intrusion Detection System (IDS) is a system that could detect suspicious activity in a network. Two approaches are known for IDS, namely signature-based and anomaly-based. The anomaly-based detection method was chosen to detect suspicious and abnormal activity for the system that cannot be performed by the signature-based method. In this study, attack testing was carried out using three DoS tools, namely the LOIC, Torshammer, and Xerxes tools, with a test scenario using IDS and without IDS. From the test results that have been carried out, IDS has successfully detected the attacks that were sent, for the delivery of the most consecutive attack packages, namely Torshammer, Xerxes, and LOIC. In the detection of Torshammer attack tools on the target FTP Server, 9421 packages were obtained, for Xerxes tools as many as 10618 packages and LOIC tools as many as 6115 packages. Meanwhile, attacks on the target Web Server for Torshammer tools were 299 packages, for Xerxes tools as many as 530 packages, and for LOIC tools as many as 103 packages. The accuracy of the IDS performance results is 88.66%, the precision is 88.58% and the false positive rate is 63.17%.

Wireless sensor networks (WSNs) have gained increasing popularity in ubiquitous support of sensing system services. Often, WSNs are energy-constrained and they are deployed in harsh and unattended environments. Consequently, WSNs are vulnerable to energy and environmental factors. To ensure secure and reliable operations in safety-critical monitoring WSNs, it is important to guarantee energy-efficient communications, location privacy protection, and reliability. Fake packet-based source location privacy (SLP) protocols are known to be energy-inefficient. Therefore, in this study, we investigate the impact of energy-inefficient communications on the privacy performance of the fake packet-based SLP protocols. Experiment results show that the protocols achieve short-term and less reliable SLP protection.

The Internet of Vehicles (IoV) is a network that considers vehicles as intelligent machines. They interact and communicate with each other to improve the performance and safety of traffic. IoV solves certain problems, but it has some issues such as response time, which prompted researchers to propose the integration of Fog Computing into vehicular networks. In Vehicular Fog Computing (VFC), the services are provided at the edge of the network to increase data rate and reduce response time. However, in order to satisfy network users, the security and privacy of sensitive data should be guaranteed. Using pseudonyms instead of real identities is one of the techniques considered to preserve the privacy of users, however, this can push malicious vehicles to exploit such a process and launch the Sybil attack by creating several pseudonyms in order to perform various malicious activities. In this paper, we describe the Sybil attack effects on VFC networks and compare them to those in conventional networks, as well as identify the various existing methods for detecting this attack and determine if they are applicable to VFC networks.

Modbus is a popular industrial communication protocol supported by most automation devices. Despite its popularity, it is not a secure protocol because when it was developed, security was not a concern due to closed environments of industrial control systems. With the convergence of information technology and operational technology in recent years, the security of industrial control systems has become a serious concern. Due to the high availability requirements, it is not practical or feasible to do security experimentation of production systems. We present an implementation of cyber-physical systems with Modbus/TCP communication for real-time security testing. The proposed architecture consists of a process simulator, an IEC 61131-3 compliant programmable logic controller, and a human-machine interface, all communicating via Modbus/TCP protocol. We use Simulink as the process simulator. It does not have built-in support for the Modbus protocol. A contribution of the proposed work is to extend the functionality of Simulink with a custom block to enable Modbus communication. We use two case studies to demonstrate the utility of the cyber-physical system architecture. We can model complex industrial processes with this architecture, can launch cyber-attacks, and develop protection mechanisms.

The digitalization of safety-critical railroad infrastructure enables new types of attacks. This increases the need to integrate Information Technology (IT) security measures into railroad systems. For that purpose, we rely on a security architecture for a railway object controller which controls field elements that we developed in previous work. Our architecture enables the integration of security mechanisms into a safety-certified railway system. In this paper, we demonstrate the practical feasibility of our architecture by using a Trusted Platform Module (TPM) 2.0 and a Multiple Independent Levels of Safety and Security (MILS) Separation Kernel (SK) for our implementation. Our evaluation includes a test bed and shows how certification and homologation can be achieved.

Relational database management systems not only support larger-than-memory data processing and very advanced query optimization, but also offer the benefits of data security, privacy, and consistency. When machine learning on large data sets is processed directly on an existing SQL database server, the data does not need to be exported and transferred to a separate big data processing platform. To achieve this, we implement a linear regression algorithm using SQL code generation such that the computation can be performed server-side and directly in the RDBMs. Our method and its implementation, programmed in Python, solves linear regression (LR) using the ordinary least squares (OLS) method directly in the RDBMS using SQL code generation, leaving most of the processing in the database. Only the matrix of the system of equations, whose size is equal to the number of variables squared, is transferred from the SQL server to the Python client to be solved for OLS regression. For evaluation purposes, our LR implementation was tested with artificially generated datasets and compared to an existing Python library (Scikit Learn). We found that our implementation consistently solves OLS regression faster than Scikit Learn for datasets with more than 10,000 input rows, and if the number of columns is less than 64. Moreover, under the same test conditions where the computation is larger than memory, our implementation showed a fast result, while Scikit returned an out-of-memory error. We conclude that SQL is a promising tool for in-database processing of large-volume, low-dimensional data sets with a particular class of machine learning algorithms, namely those that can be efficiently solved with map-reduce queries such as OLS regression.

Signcryption is a sophisticated cryptographic tool that combines the benefits of digital signature and data encryption in a single step, resulting in reduced computation and storage cost. However, the existing signcryption techniques do not account for a scenario in which a company must escrow an employee's private encryption key so that the corporation does not lose the capacity to decrypt a ciphertext when the employee or user is no longer available. To circumvent the issue of non-repudiation, the private signing key does not need to be escrowed. As a result, this paper presents an implicit certificate-based signcryption technique with private encryption key escrow, which can assist an organization in preventing the loss of private encryption. A certificate, or more broadly, a digital signature, protects users' public encryption and signature keys from man-in-the-middle attacks under our proposed approach.

Machine learning algorithms are becoming more and more widespread in industrial as well as in societal settings. This diffusion is starting to become a critical aspect of new software-intensive applications due to the need of fast reactions to changes, even if temporary, in data. This paper investigates on the improvement of reliability in the Machine Learning based classification by extending Random Forests with Bayesian Network models. Such models, combined with a mechanism able to adjust the reputation level of single learners, may improve the overall classification trustworthiness. A small example taken from the healthcare domain is presented to demonstrate the proposed approach.

In recent times, the world has seen a tremendous increase in the number of attacks on IoT devices. A majority of these attacks have been botnet attacks, where an army of compromised IoT devices is used to launch DDoS attacks on targeted systems. In this paper, we study how the choice of a dataset and the extracted features determine the performance of a Machine Learning model, given the task of classifying Linux Binaries (ELFs) as being benign or malicious. Our work focuses on Linux systems since embedded Linux is the more popular choice for building today’s IoT devices and systems. We propose using 4 different types of files as the dataset for any ML model. These include system files, IoT application files, IoT botnet files and general malware files. Further, we propose using static, dynamic as well as network features to do the classification task. We show that existing methods leave out one or the other features, or file types and hence, our model outperforms them in terms of accuracy in detecting these files. While enhancing the dataset adds to the robustness of a model, utilizing all 3 types of features decreases the false positive and false negative rates non-trivially. We employ an exhaustive scenario based method for evaluating a ML model and show the importance of including each of the proposed files in a dataset. We also analyze the features and try to explain their importance for a model, using observed trends in different benign and malicious files. We perform feature extraction using the open source Limon sandbox, which prior to this work has been tested only on Ubuntu 14. We installed and configured it for Ubuntu 18, the documentation of which has been shared on Github.

Various algorithms and models have been proposed to address text classification tasks; however, they rarely consider incorporating the additional knowledge hidden in class labels. We argue that hidden information in class labels leads to better classification accuracy. In this study, instead of encoding the labels into numerical values, we incorporated the knowledge in the labels into the original model without changing the model architecture. We combined the output of an original classification model with the relatedness calculated based on the embeddings of a sequence and a keyword set. A keyword set is a word set to represent knowledge in the labels. Usually, it is generated from the classes while it could also be customized by the users. The experimental results show that our proposed method achieved statistically significant improvements in text classification tasks. The source code and experimental details of this study can be found on Github11https://github.com/HeroadZ/KiL.

With China's overall national strength, the education of studying in China has entered a period of rapid development, and China has become one of the important destination countries for international student mobility. With political stability, rapid economic development, and continuous improvement in the quality of higher education, the educational value of studying in China is increasingly recognized by international students. International students study and live in the same way as domestic students. While the development of the Internet has brought convenience to people, it has also created many security risks. How to protect the information security of international students is the focus of this paper. This paper introduces the classification, characteristics and security risks of international students' personal information. In order to protect the private data of international students from being leaked, filtering rules are set in the campus network through WinRoute firewall to effectively prevent information from being leaked, tampered or deleted, which can be used for reference by other universities.

This research covers the problem related to user behavior and its relationship with the protection of computer assets in terms of confidentiality, integrity, and availability. The main objective was to evaluate the relationship between the dimensions of awareness, compliance and appropriation of the information security culture and the asset protection variable, the ISCA diagnostic instrument was applied, and social engineering techniques were incorporated for this process. The results show the levels of awareness, compliance and appropriation of the university that was considered as a case study, these oscillate between the second and third level of four levels. Similarly, the performance regarding asset protection ranges from low to medium. It was concluded that there is a significant relationship between the variables of the investigation, verifying that of the total types of incidents registered in the study case, approximately 69% are associated with human behavior. As a contribution, an information security culture model was formulated whose main characteristic is a complementary diagnostic process between surveys and social engineering techniques, the model also includes the information security management system, risk management and security incident handling as part of the information security culture ecosystem in an enterprise.

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.

This paper presents a design concept of an innovative CAPTCHA that can filter the color-vision–recognition states of different users. It can simultaneously verify the real-human-user identity, differentiate between the color-vision needs, and decide the content to be presented automatically.

Blockchain transactions are signed by private keys. Secure key storage and tamper resistant computing, are critical requirements for deployments of trusted infrastructure. In this paper we identify some threats against blockchain wallets, and we introduce a set of physical and logical countermeasures in order to defeat them. We introduce open software and hardware architectures based on secure elements, which enable detection of cloned device and corrupted software. These technologies are based on resistant computing (javacard), smartcard anti cloning, smartcard self content attestation, applicative firewall, bare metal architecture, remote attestation, dynamic PUF (Physical Unclonable Function), and programming token as root of trust.

In recent years, insider threat incidents and losses of companies or organizations are on the rise, and internal network security is facing great challenges. Traditional intrusion detection methods cannot identify malicious behaviors of insiders. As an effective method, insider threat detection technology has been widely concerned and studied. In this paper, we use the tree structure method to analyze user behavior, form feature sequences, and combine the Copula Based Outlier Detection (COPOD) method to detect the difference between feature sequences and identify abnormal users. We experimented on the insider threat dataset CERT-IT and compared it with common methods such as Isolation Forest.

Internal attacks are one of the biggest cybersecurity issues to companies and businesses. Despite the implemented perimeter security systems, the risk of adversely affecting the security and privacy of the organization’s information remains very high. Actually, the detection of such a threat is known to be a very complicated problem, presenting many challenges to the research community. In this paper, we investigate the effectiveness and usefulness of using Autoencoder and Variational Autoencoder deep learning algorithms to automatically defend against insider threats, without human intervention. The performance evaluation of the proposed models is done on the public CERT dataset (CERT r4.2) that contains both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a higher detection accuracy and a reasonable false positive rate.

A main type of Mobile Ad hoc Networks (MANET) and essential infrastructure to provide a wide range of safety applications to passengers in vehicles (VANET) are established. VANETs are more popular today as they connect to a variety of invisible services. VANET protection is crucial as its potential use must not endanger the safety and privacy of its users. The safety of these VANETs is essential to safe and efficient safety systems and facilities and uncertainty continues and research in this field continues to grow rapidly. We will explain the characteristics and problems of VANETs in this paper. Also, all threats and attacks that affect integrity and authentication in VANETs will be defined. Description of researchers' work was consequently addressed as the table with the problems of the suggested method and objective.

With the development of communication technology, the disadvantages of traditional notification methods such as low efficiency gradually appear. With the introduction of WAP with WTLS security and its development and maintenance, more and more notification systems are using this technology. Through the analysis, design and implementation of notification system for large user groups, this paper studies how to collect and notify data without affecting the business system, and proposes a scheme of real-time data acquisition and filtering based on trigger. The middleware and application server implementation transaction management and database operation to separate CICS middleware technology based on research using UNIXC, Socket programming, SQL statements, SYBASE database technology, from the system requirements, business process, function structure, database and data structure, the input and output of the system, system testing the aspects such as design of practical significance to intelligent notification system for large user groups. Finally, the paper describes the test effect of the system in detail. 10 users send 1, 5, 10 and 20 strokes at the same time, and the completion time is 0.28, 1.09, 1.58 and 2.20 seconds, which proves that the system has practical significance.