Biblio

With the rapid development of power Internet of Things (IoT), the ubiquitous edge agents are frequently exposed in a risky environment, where the white-box attacker could steal all the internal information by full observation of dynamic execution of the cryptographic software. In this situation, a new table-based white-box cryptography implementation of SM4 algorithm is proposed to prevent the attacker from extracting the secret key, which hides the encryption and decryption process in obfuscated lookup tables. Aiming to improve the diversity and ambiguity of the lookup tables as well as resist different types of white-box attacks, the random bijective nonlinear mappings are applied as scrambling encodings of the lookup tables. Moreover, in order to make our implementation more practical in the resource-constrained edge IoT agent, elaborate design is proposed to make some tables reusability, leading to less memory occupation while guaranteeing the security. The validity and security of the proposed implementation will be illustrated through several evaluation indicators.

In the context of the Industry 4.0 initiative, Cyber-Physical Production Systems (CPPS) or Cyber Manufacturing Systems (CMS) can be characterized as advanced networked mechatronic production systems gaining their added value by interaction with different systems using advanced communication technologies. Appropriate wired and wireless communication technologies and standards need to add timing in combination with security concepts to realize the potential improvements in the production process. One of these standards is IO-Link Wireless, which is used for sensor/actuator network operation. In this paper cryptographic performance and energy efficiency of an IO-Link Wireless Device are analyzed. The power consumption and the influence of the cryptographic operations on the trans-mission timing of the IO-Link Wireless protocol are exemplary measured employing a Phytec module based on a CC2650 system-on-chip (SoC) radio transceiver [2]. Confidentiality is considered in combination with the cryptographic performance as well as the energy efficiency. Different cryptographic algorithms are evaluated using the on chip hardware accelerator compared to a cryptographic software implementation.

A code smells detection rule is a combination of metrics with their corresponding crisp thresholds and labels. The goal of this paper is to deal with metrics' thresholds uncertainty; as usually such thresholds could not be exactly determined to judge the smelliness of a particular software class. To deal with this issue, we first propose to encode each metric value into a binary possibility distribution with respect to a threshold computed from a discretization technique; using the Possibilistic C-means classifier. Then, we propose ADIPOK-UMT as an evolutionary algorithm that evolves a population of PK-NN classifiers for the detection of smells under thresholds' uncertainty. The experimental results reveal that the possibility distribution-based encoding allows the implicit weighting of software metrics (features) with respect to their computed discretization thresholds. Moreover, ADIPOK-UMT is shown to outperform four relevant state-of-art approaches on a set of commonly adopted benchmark software systems.

Mobile embedded terminals widely applied in individual lives, but its security threats become more and more serious. Malicious attacker can steal sensitive information such as user’s phonebook, credit card information by instrumenting malicious programs, or compromising vulnerable software. Against these problems, this paper proposes a scheme for trusted protection system on the embedded platform. The system uses SM algorithms and hardware security chip as the root of trust to establish security mechanisms, including trusted boot of system image, trusted monitoring of the system running environment, disk partition encryption and verification, etc. These security mechanisms provide comprehensive protection to embedded system boot, runtime and long-term storage devices. This paper introduces the architecture and principles of the system software, design system security functions and implement prototype system for protection of embedded OS. The experiments results indicates the promotion of embedded system security and the performance test shows that encryption performance can meet the practical application.

Realizing flow security in a concurrent environment is extremely challenging, primarily due to non-deterministic nature of execution. The difficulty is further exacerbated from a security angle if sequential threads disclose control locations through publicly observable statements like print, sleep, delay, etc. Such observations lead to internal and external timing attacks. Inspired by previous works that use classical Hoare style proof systems for establishing correctness of distributed (real-time) programs, in this paper, we describe a method for finding information leaks in concurrent programs through the introduction of leaky assertions at observable program points. Specifying leaky assertions akin to classic assertions, we demonstrate how information leaks can be detected in a concurrent context. To our knowledge, this is the first such work that enables integration of different notions of non-interference used in functional and security context. While the approach is sound and relatively complete in the classic sense, it enables the use of algorithmic techniques that enable programmers to come up with leaky assertions that enable checking for information leaks in sensitive applications.

Due to the shortage of resources and services, agents are often in competition with each other. Excessive competition will lead to a social dilemma. Under the viewpoint of breaking social dilemma, we present a novel trust-based logic framework called Trust Computation Logic (TCL) for measure method to find the best partners to collaborate and automatically verifying trust in Multi-Agent Systems (MASs). TCL starts from defining trust state in Multi-Agent Systems, which is based on contradistinction between behavior in trust behavior library and in observation. In particular, a set of reasoning postulates along with formal proofs were put forward to support our measure process. Moreover, we introduce symbolic model checking algorithms to formally and automatically verify the system. Finally, the trust measure method and reported experimental results were evaluated by using DeepMind’s Sequential Social Dilemma (SSD) multi-agent game-theoretic environments.

The paper describes a methodology for assessing non-functional requirements, such as trust characteristics for applications running on computationally constrained devices in the Internet of Things. The methodology is demonstrated through an example of a microcontroller-based temperature monitoring system. The concepts of trust and trustworthiness for software and devices of the Internet of Things are complex characteristics for describing the correct and secure operation of such systems and include aspects of operational and information security, reliability, resilience and privacy. Machine learning models, which are increasingly often used for such tasks in recent years, are resource-consuming software implementations. The paper proposes to use a logic circuit model to implement the above algorithms as an additional module for computationally constrained devices for checking the trustworthiness of applications running on them. Such a module could be implemented as a hardware, for example, as an FPGA in order to achieve more effectiveness.

Safety- and security-critical developers have long recognized the importance of applying a high degree of scrutiny to a system’s (or subsystem’s) I/O messages. However, lack of care in the development of message-handling components can lead to an increase, rather than a decrease, in the attack surface. On the DARPA Cyber-Assured Systems Engineering (CASE) program, we have focused our research effort on identifying cyber vulnerabilities early in system development, in particular at the Architecture development phase, and then automatically synthesizing components that mitigate against the identified vulnerabilities from high-level specifications. This approach is highly compatible with the goals of the LangSec community. Advances in formal methods have allowed us to produce hardware/software implementations that are both performant and guaranteed correct. With these tools, we can synthesize high-assurance “building blocks” that can be composed automatically with high confidence to create trustworthy systems, using a method we call Security-Enhancing Architectural Transformations. Our synthesis-focused approach provides a higherleverage insertion point for formal methods than is possible with post facto analytic methods, as the formal methods tools directly contribute to the implementation of the system, without requiring developers to become formal methods experts. Our techniques encompass Systems, Hardware, and Software Development, as well as Hardware/Software Co-Design/CoAssurance. We illustrate our method and tools with an example that implements security-improving transformations on system architectures expressed using the Architecture Analysis and Design Language (AADL). We show how message-handling components can be synthesized from high-level regular or context-free language specifications, as well as a novel specification language for self-describing messages called Contiguity Types, and verified to meet arithmetic constraints extracted from the AADL model. Finally, we guarantee that the intent of the message processing logic is accurately reflected in the application binary code through the use of the verified CakeML compiler, in the case of software, or the Restricted Algorithmic C toolchain with ACL2-based formal verification, in the case of hardware/software co-design.

Object-oriented program (OOP) is very popular in these years for its advantages, but the testing method for OOP is still not mature enough. To deal with the problem that it is impossible to generate the probability density function by simply numeralizing a point in the test case caused by the complex structure of the object-oriented test case, we propose the Adaptive Random Testing through Test Profile for Object-Oriented software (ARTTP-OO). It generates a test case at the edge of the input field and calculates the distance between object-oriented test cases using Object and Method Invocation Sequence Similarity (OMISS) metric formula. And the probability density function is generated by the distance to select the test cases, thereby realizing the application of ARTTP algorithm in OOP. The experimental results indicate the proposed ARTTP-OO consumes less time cost without reducing the detection effectiveness.

This article describes the process of synchronizing the generation of random sequences by a quantum random number generator (QRNG) that can be used as secret keys for known cryptographic transformations. The subject of the research is a method for synchronizing the generation of random QRNG sequences based on L1 (C/A) signals of the global positioning system (GPS) using control correcting information received from control correcting stations.

Quick response (QR) codes are currently used ubiq-uitously. Their interaction protocol design is initially unsecured. It forces users to scan QR codes, which makes it harder to differentiate a genuine code from a malicious one. Intruders can change the original QR code and make it fake, which can lead to phishing websites that collect sensitive data. The interaction model can be improved and made more secure by adding some modifications to the backend side of the application. This paper addresses the vulnerabilities of QR codes and recommends improvements in security design. Furthermore, two state-of-the-art algorithms, Digital Signature (DS) and Elliptic Curve Digital Signature (ECDS), are analytically compared to determine their strengths in QR code security.

Naval vessels infrastructures are evolving towards increasingly connected and automatic systems. Such accelerated complexity boost to search for more adapted and useful navigation devices may be at odds with cybersecurity, making necessary to develop adapted analysis solutions for experts. This paper introduces a novel process to visualize and analyze naval Cyber-Physical Systems (CPS) using oriented graphs, considering operational constraints, to represent physical and functional connections between multiple components of CPS. Rapid prototyping of interconnected components is implemented in a semi-automatic manner by defining the CPS’s digital and physical systems as nodes, along with system variables as edges, to form three layers of an oriented graph, using the open-source Neo4j software suit. The generated multi-layer graph can be used to support cybersecurity analysis, like attacks simulation, anomaly detection and propagation estimation, applying existing or new algorithms.

The theoretical basis made it possible to implement software for automated secure biometric verification and personal identification, which can be used by information security systems (including access control and management systems). The work is devoted to solving an urgent problem - the development of methods and algorithms for generating a key for a storage device based on biometric parameters. Biometric cryptosystems take advantage of biometrics to improve the security of encryption keys. The ability not to store a key that is derived from biometric data is a direct advantage of the method of generating cryptographic keys from biometric data of users over other existing encryption methods.

In order to improve the security and anti-interference ability of industrial control system, this paper proposes an improved industrial neural network defense method based on the PCA dimension reduction and the improved deep neural network. Firstly, the proposed method reduces the dimensionality of the industrial data using the dimension reduction theory of principal component analysis (PCA). Then the deep neural network extracts the features of the network. Finally, the softmax classifier classifies industrial data. Experiment results show that compared with unintegrated algorithm, this method achieves higher recognition accuracy and has great application potential.

Traditional software intellectual property protection technology improves the complexity and anti-attack ability of the program, while it also increases the extra execution cost of the program. Therefore, this paper starts with the obfuscation of program control flow in reverse engineering to provide defense strategies for the protection of software intellectual property rights. Focusing on the parsing and obfuscation of Java byte code, we implement a prototype of code obfuscation system. The scheme improves the class aggregation and class splitting algorithms, discusses the fusion methods of various independent code obfuscation technologies, and provides the description and implementation of other key module algorithms. The experimental analysis shows that the obfuscation transformation scheme in this paper not only gets higher security, but also improves the program performance to a certain extent, which can effectively protect the intellectual property rights of Java software.

On the basis of analyzing the development and application of virtual reality (VR) technology at home and abroad, and combining with the specific situation of the exhibition hall, this paper establishes an immersive scene roaming system of the exhibition hall. The system is completed by virtual scene modeling technology and virtual roaming interactive technology. The former uses modeling software to establish the basic model in the virtual scene, while the latter uses VR software to enable users to control their own roles to run smoothly in the roaming scene. In interactive roaming, this paper optimizes the A* pathfinding algorithm, uses binary heap to process data, and on this basis, further optimizes the pathfinding algorithm, so that when the pathfinding target is an obstacle, the pathfinder can reach the nearest place to the obstacle. Texture mapping technology, LOD technology and other related technologies are adopted in the modeling, thus finally realizing the immersive scene roaming system of the exhibition hall.

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.

Software Defined Networking (SDN) is a networking paradigm that has been very popular due to its advantages over traditional networks with regard to scalability, flexibility, and its ability to solve many security issues. Nevertheless, SDN networks are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML). Different than other approaches found in literature which use the native flow features only for attack detection, our model extends the native features. The extended flow features are the average flow packet size, the number of flows to the same host as the current flow in the last 5 seconds, and the number of flows to the same host and port as the current flow in the last 5 seconds. Six ML algorithms were evaluated, namely Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experiments showed that RF is the best performing ML algorithm. Also, results showed that our model is able to detect attacks accurately and quickly, with a low probability of dropping normal traffic.

Software developers can use diverse techniques and tools to reduce the number of vulnerabilities, but the effectiveness of existing solutions in real projects is questionable. For example, Static Analysis Tools (SATs) report potential vulnerabilities by analyzing code patterns, and Software Metrics (SMs) can be used to predict vulnerabilities based on high-level characteristics of the code. In theory, both approaches can be applied from the early stages of the development process, but it is well known that they fail to detect critical vulnerabilities and raise a large number of false alarms. This paper studies the hypothesis of using Machine Learning (ML) to combine alerts from SATs with SMs to predict vulnerabilities in a large software project (under development for many years). In practice, we use four ML algorithms, alerts from two SATs, and a large number of SMs to predict whether a source code file is vulnerable or not (binary classification) and to predict the vulnerability category (multiclass classification). Results show that one can achieve either high precision or high recall, but not both at the same time. To understand the reason, we analyze and compare snippets of source code, demonstrating that vulnerable and non-vulnerable files share similar characteristics, making it hard to distinguish vulnerable from non-vulnerable code based on SAT alerts and SMs.

Nowadays cloud computing has become the crucial part of IT and most important thing is information security in cloud environment. Range of users can access the facilities and use cloud according to their feasibility. Cloud computing is utilized as safe storage of information but still data security is the biggest concern, for example, secrecy, data accessibility, data integrity is considerable factor for cloud storage. Cloud service providers provide the facility to clients that they can store the data on cloud remotely and access whenever required. Due to this facility, it gets necessary to shield or cover information from unapproved access, hackers or any sort of alteration and malevolent conduct. It is inexpensive approach to store the valuable information and doesn't require any hardware and software to hold the data. it gives excellent work experience but main measure is just security. In this work security strategies have been proposed for cloud data protection, capable to overpower the shortcomings of conventional data protection algorithms and enhancing security using steganography algorithm, encryption decryption techniques, compression and file splitting technique. These techniques are utilized for effective results in data protection, Client can easily access our developed desktop application and share the information in an effective and secured way.

Monitoring and managing electric power generation, distribution and transmission requires supervisory control and data acquisition (SCADA) systems. As technology has developed, these systems have become huge, complicated, and distributed, which makes them susceptible to new risks. In particular, the lack of security in SCADA systems make them a target for network attacks such as denial of service (DoS) and developing solutions for this issue is the main objective of this thesis. By reviewing various existing system solutions for securing SCADA systems, a new security approach is recommended that employs Artificial Intelligence(AI). AI is an innovative approach that imparts learning ability to software. Here deep learning algorithms and machine learning algorithms are used to develop an intrusion detection system (IDS) to combat cyber-attacks. Various methods and algorithms are evaluated to obtain the best results in intrusion detection. The results reveal the Bi-LSTM IDS technique provides the highest intrusion detection (ID) performance compared with previous techniques to secure SCADA systems

The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities.We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.

For the network intrusion detection system (NIDS), improving the performance of the analysis process has always been one of the primary goals that NIDS needs to solve. An important method to improve performance is to use parallel processing technology to maximize the usage of multi-core CPU resources. In this paper, by splitting Pcap data packets, the NIDS software Snort3 can process Pcap packets in parallel mode. On this basis, this paper compares the performance between Snort2, Suricata, and Snort3 with different CPU cores in processing different sizes of Pcap data packets. At the same time, a parallel unpacking algorithm is proposed to further improve the parallel processing performance of Snort3.

Software-defined network (SDN) is a network architecture that used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it's not possible to change dynamically, because it's a fixed connection. SDN is a good approach but still is vulnerable to DDoS attacks. The DDoS attack is menacing to the internet. To prevent the DDoS attack, the machine learning algorithm can be used. The DDoS attack is the multiple collaborated systems that are used to target the particular server at the same time. In SDN control layer is in the center that link with the application and infrastructure layer, where the devices in the infrastructure layer controlled by the software. In this paper, we propose a machine learning technique namely Decision Tree and Support Vector Machine (SVM) to detect malicious traffic. Our test outcome shows that the Decision Tree and Support Vector Machine (SVM) algorithm provides better accuracy and detection rate.

To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.