Biblio

We showcase the capabilities of V2Verifier, a new open-source software-defined radio (SDR) testbed for vehicle-to-vehicle (V2V) communications security, to expose the strengths and vulnerabilities of current V2V security systems based on the IEEE 1609.2 standard. V2Verifier supports both major V2V technologies and facilitates a broad range of experimentation with upper- and lower-layer attacks using a combination of SDRs and commercial V2V on-board units (OBUs). We demonstrate two separate attacks (jamming and replay) against Dedicated Short Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X) technologies, experimentally quantifying the threat posed by these types of attacks. We also use V2Verifier's open-source implementation to show how the 1609.2 standard can effectively mitigate certain types of attacks (e.g., message replay), facilitating further research into the security of V2V.

Satisfying security requirements for Mobile Ad-hoc Networks (MANETs) is a key challenge due to the limited power budget for the nodes composing those networks. Therefore, it is essential to exploit lightweight cryptographic algorithms to preserve the confidentiality of the messages being transmitted between different nodes in MANETs. At the heart of such algorithms lies the Elliptic Curve Cryptography (ECC). The importance of ECC lies in offering equivalent security with smaller key sizes, which results in faster computations, lower power consumption, as well as memory and bandwidth savings. However, when exploiting ECC in MANETs, it is essential to properly choose the parameters of ECC such that an acceptable level of confidentiality is achieved without entirely consuming the power budget of nodes. In addition, the delay of the communication should not abruptly increase. In this paper, we study the effect of changing the prime number use in ECC on power consumption, delay, and the security of the nodes in MANETs. Once a suitable prime number is chosen, a comparative analysis is conducted between two reactive routing protocols, namely, Ad-hoc on Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) in terms of power consummation and delay. Experimental results show that a prime number value of 197 for ECC alongside with DSR for routing preserve an acceptable level of security for MANETs with low average power consumption and low average delay in the communication.

In this research we have explained to set up an Infrared Array Sensor system that is IOT based in order to provide security at remote location. We have tried to Establishment of cloud environment to host IOT application & Development of IOT Application using Asp.net with C\# programming platform. We have Integrated IOT with Infrared Array sensors in order to implement proposed work. In this research camera captures the external event and sent signal to Infrared grid array sensor. Internet of Things (IoT) would enable applications of utmost societal value including smart cities, smart grids & smart healthcare. For majority of such applications, strict dependability requirements are placed on IOT performance, & sensor data as well as actuator commands must be delivered reliably & timely.

Application programming interfaces (APIs) are a vital part of every online business. APIs are responsible for transferring data across systems within a company or to the users through the web or mobile applications. Security is a concern for any public-facing application. The objective of this study is to analyze incoming requests to a target API and flag any malicious activity. This paper proposes a solution using sequence models to identify whether or not an API request has SQL, XML, JSON, and other types of malicious injections. We also propose a novel heuristic procedure that minimizes the number of false positives. False positives are the valid API requests that are misclassified as malicious by the model.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

For the task of natural language processing (NLP), Word embedding technology has a certain impact on the accuracy of deep neural network algorithms. Considering that the current word embedding method cannot realize the coexistence of words and phrases in the same vector space. Therefore, we propose an enhanced word embedding (EWE) method. Before completing the word embedding, this method introduces a unique sentence reorganization technology to rewrite all the sentences in the original training corpus. Then, all the original corpus and the reorganized corpus are merged together as the training corpus of the distributed word embedding model, so as to realize the coexistence problem of words and phrases in the same vector space. We carried out experiment to demonstrate the effectiveness of the EWE algorithm on three classic benchmark datasets. The results show that the EWE method can significantly improve the classification performance of the CNN model.

Quantum cryptography doesn't depend on computational capabilities of intruders; it uses inviolability of quantum physics postulates (postulate of measurement, no-cloning theorem, uncertainty principle). Some quantum key distribution protocols have absolute (theoretical and informational) stability, but quantum secure direct communication (deterministic) protocols have only asymptotic stability. For a whole class of methods to ensure Q-trit deterministic quantum cryptography protocols stability, reliable trit generation method is required. In this paper, authors have developed a high-speed and secure pseudorandom number (PRN) generation method. This method includes the following steps: initialization of the internal state vector and direct PRN generation. Based on this method TriGen v.2.0 pseudo-random number generator (PRNG) was developed and studied in practice. Therefore, analysing the results of study it can be concluded following: 1) Proposed Q-trit PRNG is better then standard C ++ PRNG and can be used on practice for critical applications; 2) NIST STS technique cannot be used to evaluate the quality (statistical stability) of the Q-trit PRNG and formed trit sequences; 3) TritSTS 2020 technique is suitable for evaluating Q-trit PRNG and trit sequences quality. A future research study can be related to developing a fully-functional version of TritSTS technique and software tool.

Wireless Sensor Network (WSN) is considered as the backbone of Internet of Things (IoT) networks. Authentication is the most important phase that guarantees secure access to such networks but it is more critical than that in traditional Internet because the communications are established between constrained devices that could not compute heavy cryptographic primitives. In this paper, we are studying with real experimentation the efficiency of HIP Diet EXchange header (HIP DEX) protocol over IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) in IoT. The adopted application layer protocol is Constrained Application Protocol (CoAP) and as a routing protocol, the Routing Protocol for Low power and lossy networks (RPL). The evaluation concerns the total End-to-End transmission delays during the authentication process between the communicating peers regarding the processing, propagation, and queuing times' overheads results. Most importantly, we propose an efficient handshake packets' compression header, and we detailed a comparison of the above evaluation's criteria before and after the proposed compression. Obtained results are very encouraging and reinforce the efficiency of HIP DEX in IoT networks during the handshake process of constrained nodes.

In many applications, software protection can not be sufficient to provide high security needed by some critical applications. A noteworthy example are the bitcoin wallets. Designed the most secure piece of software, their security can be compromised by a simple piece of malware infecting the device storing keys used for signing transactions. Secure hardware devices such as Trusted Platform Module (TPM) offers the ability to create a piece of code that can run unmolested by the rest of software applications hosted in the same machine. This has turned out to be a valuable approach for preventing several malware threats. Unfortunately, their restricted functionalities make them inconsistent with the use of multi and threshold signature mechanisms which are in the heart of real world cryptocurrency wallets implementation. This paper proposes an efficient multi-signature scheme that fits the requirement of the TPM. Based on discrete logarithm and pairings, our scheme does not require any interaction between signers and provide the same benefits as the well established BLS signature scheme. Furthermore, we proposed a formal model of our design and proved it security in a semi-honest model. Finally, we implemented a prototype of our design and studied its performance. From our experimental analysis, the proposed design is highly efficient and can serve as a groundwork for using TPM in future cryptocurrency wallets.

Reverse engineers are in high demand in digital forensics for their ability to investigate malicious cyberspace threats. This group faces unique challenges due to the security-intensive environment, such as working in isolated networks, a limited ability to share files with others, immense time pressure, and a lack of cognitive support tools supporting the iterative exploration of binary executables. This paper presents an exploratory study that interviewed experienced reverse engineers' work processes, tools, challenges, and visualization needs. The findings demonstrate that engineers have difficulties managing hypotheses, organizing results, and reporting findings during their analysis. By considering the provenance support techniques of existing research in other domains, this study contributes new insights about the needs and opportunities for reverse engineering provenance tools.

The extensive use of the internet and web-based applications spot the multiserver authentication as a significant component. The users can get their services after authenticating with the service provider by using similar registration records. Various protocol schemes are developed for multiserver authentication, but the existing schemes are not secure and often lead towards various vulnerabilities and different security issues. Recently, Zhao et al. put forward a proposal for smart card and user's password-based authentication protocol for the multiserver environment and showed that their proposed protocol is efficient and secure against various security attacks. This paper points out that Zhao et al.'s authentication scheme is susceptive to traceability as well as anonymity attacks. Thus, it is not feasible for the multiserver environment. Furthermore, in their scheme, it is observed that a user while authenticating does not send any information with any mention of specific server identity. Therefore, this paper proposes an enhanced, efficient and secure user authentication scheme for use in any multiserver environment. The formal security analysis and verification of the protocol is performed using state-of-the-art tool “ProVerif” yielding that the proposed scheme provides higher levels of security.

Owing to the advancements in communication media and devices all over the globe, there has arisen a dire need for to limit the alarming number of attacks targeting these and to enhance their security. Multiple techniques have been incorporated in different researches and various protocols and schemes have been put forward to cater security issues of session initiation protocol (SIP). In 2008, Qiu et al. presented a proposal for SIP authentication which while effective than many existing schemes, was still found vulnerable to many security attacks. To overcome those issues, Zhang et al. proposed an authentication protocol. This paper presents the analysis of Zhang et al. authentication scheme and concludes that their proposed scheme is susceptible to user traceablity. It also presents an improved SIP authentication scheme that eliminates the possibility of traceability of user's activities. The proposed scheme is also verified by contemporary verification tool, ProVerif and it is found to be more secure, efficient and practical than many similar SIP authetication scheme.

Due to the extraordinary volume of business information, classy cyber-attacks pointing the networks of all enterprise have become more casual, with intruders trying to pierce vast into and grasp broader from the compromised network machines. The vital security essential is that field experts and the network administrators have a common terminology to share the attempt of intruders to invoke the system and to rapidly assist each other retort to all kind of threats. Given the enormous huge system traffic, traditional Machine Learning (ML) algorithms will provide ineffective predictions of the network anomaly. Thereby, a hybridized multi-model system can improve the accuracy of detecting the intrusion in the networks. In this manner, this article presents a novel approach Clustered Generalization oriented Ensemble Learning Model (CGELM) for predicting the network anomaly. The performance metrics of the anticipated approach are Detection Rate (DR) and False Predictive Rate (FPR) for the two heterogeneous data sets namely NSL-KDD and UGR'16. The proposed method provides 98.93% accuracy for DR and 0.14% of FPR against Decision Stump AdaBoost and Stacking Ensemble methods.

The discovery of adversarial examples poses a serious risk to the deep neural networks (DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well-behaved DNN model can be easily fooled and completely change the prediction categories of the input samples. However, research on adversarial attacks in the field of modulation recognition mainly focuses on increasing the prediction error of the classifier, while ignores the importance of decreasing the perceptual invisibility of attack. Aiming at the task of DNNbased modulation recognition, this study designs the Fitting Difference as a metric to measure the perturbed waveforms and proposes a new method: the Nesterov Adam Iterative Method to generate adversarial examples. We show that the proposed algorithm not only exerts excellent white-box attacks but also can initiate attacks on a black-box model. Moreover, our method decreases the waveform perceptual invisibility of attacks to a certain degree, thereby reducing the risk of an attack being detected.

Context: Security is vital to software developed for commercial or personal use. Although more organizations are realizing the importance of applying secure coding practices, in many of them, security concerns are not known or addressed until a security failure occurs. The root cause of security failures is vulnerable code. While metrics have been used to predict software vulnerabilities, we explore the relationship between code and architectural smells with security weaknesses. As smells are surface indicators of a deeper problem in software, determining the relationship between smells and software vulnerabilities can play a significant role in vulnerability prediction models. Objective: This study explores the relationship between smells and software vulnerabilities to identify the smells. Method: We extracted the class, method, file, and package level smells for three systems: Apache Tomcat, Apache CXF, and Android. We then compared their occurrences in the vulnerable classes which were reported to contain vulnerable code and in the neutral classes (non-vulnerable classes where no vulnerability had yet been reported). Results: We found that a vulnerable class is more likely to have certain smells compared to a non-vulnerable class. God Class, Complex Class, Large Class, Data Class, Feature Envy, Brain Class have a statistically significant relationship with software vulnerabilities. We found no significant relationship between architectural smells and software vulnerabilities. Conclusion: We can conclude that for all the systems examined, there is a statistically significant correlation between software vulnerabilities and some smells.

The generation, transmission, distribution, and storage of electric power is becoming increasingly decentralized. Advances in Distributed Energy Resources (DERs) are rapidly changing the nature of the power grid. Moreover, the accommodation of these new technologies by the legacy grid requires that an increasing number of devices be Internet connected so as to allow for sensor and actuator information to be collected, transmitted, and processed. With the wide adoption of the Internet of Things (IoT), the cybersecurity vulnerabilities of smart grid devices that can potentially affect the stability, reliability, and resilience of the power grid need to be carefully examined and addressed. This is especially true in situations in which smart grid devices are deployed with default configurations or without reasonable protections against malicious activities. While much work has been done to characterize the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices, this paper demonstrates that similar vulnerabilities associated with the newer class of IoT smart grid devices are becoming a concern. Specifically, this paper first performs an evaluation of such devices using the Shodan platform and text processing techniques to analyze a potential vulnerability involving the lack of password protection. This work further explores several Shodan search terms that can be used to identify additional smart grid components that can be evaluated in terms of cybersecurity vulnerabilities. Finally, this paper presents recommendations for the more secure deployment of such smart grid devices.

Ransomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.

To better protect users' privacy, various authentication mechanisms have been applied on smartphones. Android pattern lock has been widely used because it is easy to memorize, however, simple ones are more vulnerable to attack such as shoulder surfing attack. In this paper, we propose a security rating evaluation scheme based on pattern lock. In particular, an entropy function of a pattern lock can be calculated, which is decided by five kinds of attributes: size, length, angle, overlap and intersection for quantitative evaluation of pattern lock. And thus, the security rating thresholds will be determined by the distribution of entropy values. Finally, we design and develop an APP based on Android Studio, which is used to verify the effectiveness of our proposed security rating evaluation scheme.

Radio frequency identification (RFID) is widespread and still necessary in many important applications. However, and in various significant cases, the use of this technology faces multiple security issues that must be addressed. This is mainly related to the use of RFID tags (transponders) which are electronic components communicating wirelessly, and hence they are vulnerable to multiple attacks through several means. In this work, an extensive fault analysis is performed on a tag architecture in order to evaluate its hardness. Tens of millions of single-bit upset (SBU) and multiple-bit upset (MBU) faults are emulated randomly on this tag architecture using an FPGA-based emulation platform. The emulated faults are classified under five groups according to faults effect on the tag behaviour. The obtained results show the faults effect variation in function of the number of MBU affected bits. The interpretation of this variation allows evaluating the tag robustness. The proposed approach represents an efficient mean that permits to study tag architectures at the design level and evaluating their robustness and vulnerability to fault attacks.

This paper investigates the event-triggered control (ETC) problem for stochastic networked control systems (NCSs) with exogenous disturbances and Denial-of-Service (DoS) attacks. The ETC strategy is proposed to reduce the utilization of network resource while defending the DoS attacks. Based on the introduced ETC strategy, sufficient conditions, which rely on the frequency and duration properties of DoS attacks, are obtained to achieve the stochastic input-to-state stability and Zeno-freeness of the ETC stochastic NCSs. An example of air vehicle system is given to explain the effectiveness of proposed ETC strategy.

With the application of virtualization and multi-core processor in embedded system, the computing capacity of embedded system has been improved comprehensively, but it is also faced with malicious attacks against virtualization technology. First, it was analyzed the security requirements of each layer of embedded virtualization computing platform. Aiming at the security requirements, it was proposed the security architecture of embedded virtualization computing platform based on trusted computing module. It was designed the hardware trusted root on the hardware layer, the virtualization trusted root on the virtual machine manager layer, trusted computing component and security function component on guest operation system layer. Based on the trusted roots, it was built the static extension of the trusted chain on the platform. This security architecture can improve the active security protection capability of embedded virtualization computing platform.

Malicious software (malware) is designed to cause unwanted or destructive effects on computers. Since modern society is dependent on computers to function, malware has the potential to do untold damage. Therefore, developing techniques to effectively combat malware is critical. With the rise in popularity of polymorphic malware, conventional anti-malware techniques fail to keep up with the rate of emergence of new malware. This poses a major challenge towards developing an efficient and robust malware detection technique. One approach to overcoming this challenge is to classify new malware among families of known malware. Several machine learning methods have been proposed for solving the malware classification problem. However, these techniques rely on hand-engineered features extracted from malware data which may not be effective for classifying new malware. Deep learning models have shown paramount success for solving various classification tasks such as image and text classification. Recent deep learning techniques are capable of extracting features directly from the input data. Consequently, this paper proposes an end-to-end deep learning framework for multimodels (henceforth, multimodel learning) to solve the challenging malware classification problem. The proposed model utilizes three different deep neural network architectures to jointly learn meaningful features from different attributes of the malware data. End-to-end learning optimizes all processing steps simultaneously, which improves model accuracy and generalizability. The performance of the model is tested with the widely used and publicly available Microsoft Malware Challenge Dataset and is compared with the state-of-the-art deep learning-based malware classification pipeline. Our results suggest that the proposed model achieves comparable performance to the state-of-the-art methods while offering faster training using end-to-end multimodel learning.

System security is becoming an indispensable part of our daily life due to the rapid proliferation of unknown malware attacks. Recent malware found to have a very complicated structure that is hard to detect by the traditional malware detection techniques such as antivirus, intrusion detection systems, and network scanners. In this paper, we propose a complex network-based malware detection technique, Malware Detection using Complex Network (MDCN), that considers Application Program Interface Call Transition Matrix (API-CTM) to generate complex network topology and then extracts various feature set by analyzing different metrics of the complex network to distinguish malware and benign applications. The generated feature set is then sent to several machine learning classifiers, which include naive-Bayes, support vector machine, random forest, and multilayer perceptron, to comparatively analyze the performance of MDCN-based technique. The analysis reveals that MDCN shows higher accuracy, with lower false-positive cases, when the multilayer perceptron-based classifier is used for the detection of malware. MDCN technique can efficiently be deployed in the design of an integrated enterprise network security system.

Nanosize multiferroic La-doped YFeO3 powders are harvested via a low-temperature solid-state reaction method. X-ray diffraction (XRD), scanning electron microscopy (SEM) and Raman spectra analysis reveal that with La addition, YFeO3 powders are successfully fabricated at a lower temperature with the size below 60 nm, and a refined structure is obtained. Magnetic hysteresis loop illustrates ferromagnetic behavior of YFeO3 nano particles can be enhanced with La addition. The maximum and remnant magnetization of the powders are about 4.03 and 1.22 emu/g, respectively. It is shown that the optical band gap is around 2.25 eV, proving that La doped YFeO3 nano particles can strongly absorb visible light. Both magnetic and optical properties are greatly enhanced with La addition, proving its potential application in magnetic and optical field.

Practical activities usually require the ability to simultaneously work with internal, distributed information resources and access to the Internet. The need to solve this problem necessitates the use of appropriate administrative and technical methods to protect information. Such methods relate to the idea of domain isolation. This paper considers the principles of implementation and properties of an "Endpoint Cloud Terminal" that is general-purpose software tool with built-in security instruments. This apparatus solves the problem by combining an arbitrary number of isolated and independent workplaces on one hardware unit, a personal computer.