Biblio

Return-Oriented Programming (ROP) is a sophisticated exploitation technique that is able to drive target applications to perform arbitrary unintended operations by constructing a gadget chain reusing existing small code sequences (gadgets) collected across the entire code space. In this paper, we propose to address ROP attacks from a different angle-shrinking available code space at runtime. We present ROPStarvation , a generic and transparent ROP countermeasure that defend against all types of ROP attacks with almost zero run-time overhead. ROPStarvation does not aim to completely stop ROP attacks, instead it attempts to significantly increase the bar by decreasing the possibility of launching a successful ROP exploit in reality. Moreover, shrinking available code space at runtime is lightweight that makes ROPStarvation practical for being deployed with high performance requirement. Results show that ROPStarvation successfully reduces the code space of target applications by 85%. With the reduced code segments, ROPStarvation decreases the probability of building a valid ROP gadget chain by 100% and 83% respectively, with the assumptions that whether the adversary knows the vulnerable applications are protected by ROPStarvation . Evaluations on the SPEC CPU2006 benchmark show that ROPStarvation introduces nearly zero (0.2% on average) run-time performance overhead.

Order-revealing encryption (ORE) is a basic cryptographic primitive for ciphertext comparisons based on the order relationship of plaintexts while maintaining the privacy of them. In the data era we are experiencing, cross-dataset transactions become ubiquitous in practice. However, almost all the previous ORE schemes can only support comparisons on ciphertexts from the same user, which does not meet the requirement for the multi-user environment. In this work, we introduce and design ORE schemes with delegation functionality, which is referred to as delegatable ORE (DORE). The "delegation" here is an authorization that allows for efficient ciphertext comparisons among different users. To the best of our knowledge, it is the first ORE that allows an user to delegate the comparison privilege for his ciphertexts, which also opens the door for future explorations. At the heart of the construction and analysis of DORE is a new building tool proposed in this work, named delegatable equality-revealing encoding (DERE), which might be of independent interest.

This paper investigates the temperature demagnetization modeling method for permanent magnets (PM) in permanent magnet synchronous linear motor (PMSLM). First, the PM characteristics are presented, and finite element analysis (FEA) is conducted to show the magnetic distribution under different temperatures. Second, demagnetization degrees and remanence of the five PMs' experiment sample are actually measured in stove at temperatures varying from room temperature to 300 °C, and to obtain the real data for next-step modeling. Third, machine learning algorithm called extreme learning machine (ELM) is introduced to map the nonlinear relationships between temperature and demagnetization characteristics of PM and build the demagnetization models. Finally, comparison experiments between linear modeling method, polynomial modeling method, and ELM can certify the effectiveness and advancement of this proposed method.

The construction and improvement of the underwater acoustic network is the premise and guarantee for the development of the marine industry. Because the underwater nodes need to work for a long time, it is especially important to ensure that the nodes have a long standby capacity. In general, the node is in a low-power standby state waiting for a wake-up signal. When the node detects the wakeup signal, it will resume normal operation. In this paper, we propose a signal design based on the m-sequence. which can detect the hidden awakening signal in the complex environment with low SNR and small Doppler shift. Simulation and experimental data indicate that when the input SNR is as low as -11 dB and the signal has a small Doppler shift, the system can still achieve a detection probability of 100% and ensure that the false alarm probability is lower than 10-6.

Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations. Despite the long-term vision, however, existing studies on black-box adversarial attacks are still restricted to very specific settings of threat models (e.g., single distortion metric and restrictive assumption on target model's feedback to queries) and/or suffer from prohibitively high query complexity. To push for further advances in this field, we introduce a general framework based on an operator splitting method, the alternating direction method of multipliers (ADMM) to devise efficient, robust black-box attacks that work with various distortion metrics and feedback settings without incurring high query complexity. Due to the black-box nature of the threat model, the proposed ADMM solution framework is integrated with zeroth-order (ZO) optimization and Bayesian optimization (BO), and thus is applicable to the gradient-free regime. This results in two new black-box adversarial attack generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image classification datasets show that our proposed approaches have much lower function query complexities compared to state-of-the-art attack methods, but achieve very competitive attack success rates.

SQL injection attacks are a kind of the greatest security risks on Web applications. Much research has been done to detect SQL injection attacks by rule matching and syntax tree. However, due to the complexity and variety of SQL injection vulnerabilities, these approaches fail to detect unknown and variable SQL injection attacks. In this paper, we propose a model, ATTAR, to detect SQL injection attacks using grammar pattern recognition and access behavior mining. The most important idea of our model is to extract and analyze features of SQL injection attacks in Web access logs. To achieve this goal, we first extract and customize Web access log fields from Web applications. Then we design a grammar pattern recognizer and an access behavior miner to obtain the grammatical and behavioral features of SQL injection attacks, respectively. Finally, based on two feature sets, machine learning algorithms, e.g., Naive Bayesian, SVM, ID3, Random Forest, and K-means, are used to train and detect our model. We evaluated our model on these two feature sets, and the results show that the proposed model can effectively detect SQL injection attacks with lower false negative rate and false positive rate. In addition, comparing the accuracy of our model based on different algorithms, ID3 and Random Forest have a better ability to detect various kinds of SQL injection attacks.

Proposed an approach for information security breaches detection in distributed control systems based on prediction of multidimensional time series formed of sensor and actuator data.

Digital ant technology is a new distributed and self-organization cyberspace defense paradigm. This paper describes digital ants system's developing process, characteristics, system architecture and mechanisms to illustrate its superiority, searches the possible applications of digital ants system. The summary of the paper and the trends of digital ants system are pointed out.

The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.

When building the architecture of cyber defense systems, one of the important tasks is to create a methodology for current diagnostics of cybersecurity status of information systems and objects of information activity. The complexity of this procedure is that having a strong security level of the object at the software level does not mean that such power is available at the hardware level or at the cryptographic level. There are always weaknesses in all levels of information security that criminals are constantly looking for. Therefore, the task of promptly calculating the likelihood of possible negative consequences from the successful implementation of cyberattacks is an urgent task today. This paper proposes an approach of obtaining an instantaneous calculation of the probabilities of negative consequences from the successful implementation of cyberattacks on objects of information activity on the basis of delayed differential equation theory and the mechanism of constructing a logical Fuzzy function. This makes it possible to diagnose the security status of the information system.

From the point of view of statistical signal processing, the dynamic range for one-bit quantisers with time-varying thresholds is studied. Maximum tolerable amplitudes, minimum detectable amplitudes and dynamic ranges of this one-bit sampling approach and uniform quantisers, such as N-bits analogue-to-digital converters (ADCs), are derived and simulated. The results reveal that like conventional ADCs, the dynamic ranges of one-bit sampling approach are linearly proportional to the Gaussian noise standard deviations, while one-bit sampling's dynamic ranges are lower than N-bits ADC under the same noise levels.

Most of the security algorithms proposed for the sensor networks such as secure routing, data encryption and authentication, and intrusion detection target protecting the content of the collected data from being exposed to different types of attacks. However, the context of the collected data, such as event occurrence, event time, and event location, is not addressed by these security mechanisms and can still be leaked to the adversaries. Therefore, we propose in this paper a novel and efficient unobservability scheme for source/sink location privacy for wireless multimedia sensor networks. The proposed privacy scheme is based on a cross-layer design between the application and routing layers in order to exploit the multimedia processing technique with multipath routing to hide the event occurrences and locations of important nodes without degrading the network performance. Simulation analysis shows that our proposed scheme satisfies the privacy requirements and has better performance compared to other existing techniques.

Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. The intensity and frequency of these attacks are increasing at an alarming rate. Numerous schemes have been proposed to mitigate the impact of DDoS attacks. This paper presents a comprehensive empirical evaluation of Machine Learning (ML)based DDoS detection techniques, to gain better understanding of their performance in different types of environments. To this end, a framework is developed, focusing on different attack scenarios, to investigate the performance of a class of ML-based techniques. The evaluation uses different performance metrics, including the impact of the “Class Imbalance Problem” on ML-based DDoS detection. The results of the comparative analysis show that no one technique outperforms all others in all test cases. Furthermore, the results underscore the need for a method oriented feature selection model to enhance the capabilities of ML-based detection techniques. Finally, the results show that the class imbalance problem significantly impacts performance, underscoring the need to address this problem in order to enhance ML-based DDoS detection capabilities.

Today, large and complex software is developed with integrated components using application programming interfaces (APIs). Correct usage of APIs in practice presents a challenge due to implicit constraints, such as call conditions or call orders. API misuse, i.e., violation of these constraints, is a well-known source of bugs, some of which can cause serious security vulnerabilities. Although researchers have developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent. In this paper, we provide a comprehensive empirical study on API-misuse bugs in open-source C programs. To understand the nature of API misuses in practice, we analyze 830 API-misuse bugs from six popular programs across different domains. For all the studied bugs, we summarize their root causes, fix patterns and usage statistics. Furthermore, to understand the capabilities and limitations of state-of-the-art static analysis detectors for API-misuse detection, we develop APIMU4C, a dataset of API-misuse bugs in C code based on our empirical study results, and evaluate three widely-used detectors on it qualitatively and quantitatively. We share all the findings and present possible directions towards more powerful API-misuse detectors.

Network threats often come from multiple sources and affect a variety of domains. Collaborative sharing and analysis of Cyber Threat Information (CTI) can greatly improve the prediction and prevention of cyber-attacks. However, CTI data containing sensitive and confidential information can cause privacy exposure and disclose security risks, which will deter organisations from sharing their CTI data. To address these concerns, the consortium of the EU H2020 project entitled Collaborative and Confidential Information Sharing and Analysis for Cyber Protection (C3ISP) has designed and implemented a framework (i.e. C3ISP Framework) as a service for cyber threat management. This paper focuses on the design and development of an API Gateway, which provides a bridge between end-users and their data sources, and the C3ISP Framework. It facilitates end-users to retrieve their CTI data, regulate data sharing agreements in order to sanitise the data, share the data with privacy-preserving means, and invoke collaborative analysis for attack prediction and prevention. In this paper, we report on the implementation of the API Gateway and experiments performed. The results of these experiments show the efficiency of our gateway design, and the benefits for the end-users who use it to access the C3ISP Framework.

With the increase in the incidences of data leakage, enterprises have started to realize that the endpoints (especially mobile devices) used by their employees are the primary cause of data breach in most of the cases. Data shows that employee training, which aims to promote the awareness of protecting the sensitive data of the organization is not very useful. Besides, popular third-party cloud services make it even more difficult for employees to keep the secrets of their workplace safer. This pressing issue has caused the emergence of a significant market for various software products that provide endpoint data protection for these organizations. Our study will discuss some methods and technologies that deal with traditional, negative endpoint protection: Endpoint protection platform (EPP), and another new, positive endpoint protection: Endpoint detection and response (EDR). The comparison and evaluation between EPP and EDR in mechanism and effectiveness will also be shown. The study also aims to analyze the merits, faults, and key features that an excellent protection software should have. The objective of this paper is to assist small-scale and big-scale companies to improve their understanding of insider threats in such rapidly developing cyberspace, which is full of potential risks and attacks. This will also help the companies to have better control over their employee's endpoint to be able to avoid any future data leaks. It will also help negligent users to comprehend how serious is the problem that they are faced with, and how they should be careful in handling their privacy when they are surfing the Internet while being connected to the company's network. This paper aims to contribute to further research on endpoint detection and protection or some similar topics by trying to predict the future of protection products.

This paper introduces an approach based on control theory to model, analyze and select optimal security policies for Moving Target Defense (MTD) deployment strategies. A Markov Decision Process (MDP) scheme is presented to model states of the system from attacking point of view. The employed value iteration method is based on the Bellman optimality equation for optimal policy selection for each state defined in the system. The model is then utilized to analyze the impact of various costs on the optimal policy. The MDP model is then applied to two case studies to evaluate the performance of the model.

Web services from large-scale systems are prevalent all over the world. However, these systems are naturally vulnerable and incline to be intruded by adversaries for illegal benefits. To detect anomalous events, previous works focus on inspecting raw system logs by identifying the outliers in workflows or relying on machine learning methods. Though those works successfully identify the anomalies, their models use large training set and process whole system logs. To reduce the quantity of logs that need to be processed, high recall suspicious network alert systems can be applied to preprocess system logs. Only the logs that trigger alerts are retrieved for further usage. Due to the universally usage of network traffic alerts among Security Operations Center, anomalies detection problems could be transformed to classify truly suspicious network traffic alerts from false alerts.In this work, we propose an ensemble model to distinguish truly suspicious alerts from false alerts. Our model consists of two sub-models with different feature extraction strategies to ensure the diversity and generalization. We use decision tree based boosters and deep neural networks to build ensemble models for classification. Finally, we evaluate our approach on suspicious network alerts dataset provided by 2019 IEEE BigData Cup: Suspicious Network Event Recognition. Under the metric of AUC scores, our model achieves 0.9068 on the whole testing set.

In this paper, we leverage previous work in the Identity Ecosystem, a Bayesian network mathematical representation of a person's identity, to create a framework to evaluate identity protection systems. Information dynamic is considered and a protection game is formed given that the owner and the attacker both gain some level of control over the status of other PII within the dynamic Identity Ecosystem. We present a policy iteration algorithm to solve the optimal policy for the game and discuss its convergence. Finally, an evaluation and comparison of identity protection strategies is provided given that an optimal policy is used against different protection policies. This study is aimed to understand the evolutionary process of identity theft and provide a framework for evaluating different identity protection strategies and future privacy protection system.

Industrial Control Security (ICS) plays an important role in protecting Industrial assets and processed from being tampered by attackers. Recent years witness the fast development of ICS technology. However there are still shortage of techniques and measures to verify the effectiveness of ICS approaches. In this paper, we propose a verification framework named vICS, for security measures in industrial environments. vICS does not requires installing any agent in industrial environments, and could be viewed as a non-intrusive way. We use vICS to evaluate the effectiveness of classic ICS techniques and measures through several experiments. The results shown that vICS provide an feasible solution for verifying the effectiveness of classic ICS techniques and measures for industrial environments.

We propose a new architecture introducing AI to span the control layer and the data layer in SDON. This demonstration shows the cooperation of the AI engines in two layers in dealing with failure disposal.

Public sector and private organizations began using cybersecurity control in order to defend their assets against cybercriminals attack. Cybersecurity audits assist organizations to deal with cyber threats, cybercriminals, and cyber-attacks thatare growing in an aggressive cyber landscape. However, cyber-attacks and threats become more increase and complex in complicated cyber landscapes challenge auditors to perform an effective cybersecurity audit. This current situation puts in evidens ce the critical need for a new approach in the cybersecurity audit execution. This study reviews an alternative method in the execution of cybersecurity security checks. The analysis is on the character and behavioral of cyber-attacks and threats using feature extraction and selection method to get crucial elements from the common group of cyber-attacks and threats. Cyber-attacks and threats profile are systematic approaches driven by a clear understanding of the form of cyber-attacks and threats character and behavior patterns in cybersecurity requirements. As a result, this study proposes cyber-attacks and threats profiling for cybersecurity audit as a set of control elements that are harmonized with audit components that drive audits based on cyber threats.

Data provenance tools capture the steps used to produce analyses. However, scientists must choose among workflow provenance systems, which allow arbitrary code but only track provenance at the granularity of files; provenance APIs, which provide tuple-level provenance, but incur overhead in all computations; and database provenance tools, which track tuple-level provenance through relational operators and support optimization, but support a limited subset of data science tasks. None of these solutions are well suited for tracing errors introduced during common ETL, record alignment, and matching tasks - for data types such as strings, images, etc. Scientists need new capabilities to identify the sources of errors, find why different code versions produce different results, and identify which parameter values affect output. We propose PROVision, a provenance-driven troubleshooting tool that supports ETL and matching computations and traces extraction of content within data objects. PROVision extends database-style provenance techniques to capture equivalences, support optimizations, and enable selective evaluation. We formalize our extensions, implement them in the PROVision system, and validate their effectiveness and scalability for common ETL and matching tasks.

Many organizations around the world recognize the vitality of cloud computing. However, some concerns make organizations reluctant to adopting cloud computing. These include data security, privacy, and trust issues. It is very important that these issues are addressed to meet client concerns and to encourage the wider adoption of cloud computing. This paper develops a user privacy framework based upon on emerging security model that includes access control, encryption and protection monitor schemas in the cloud environment.

Many enterprises and individuals prefer to outsource data to public cloud via various pricing approaches. One of the most widely-used approaches is the pay-as-you-go model, where the data owner hires public cloud to share data with data consumers, and only pays for the actually consumed services. To realize controllable and secure data sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. But there are some serious challenges when applying CP-ABE in pay-as-you-go. Firstly, the decryption cost in ABE is too heavy for data consumers. Secondly, ABE ciphertexts probably suffer distributed denial of services (DDoS) attacks, but there is no solution that can eliminate the security risk. At last, the data owner should audit resource consumption to guarantee the transparency of charge, while the existing method is inefficient. In this work, we propose a general construction named fully accountable ABE (FA-ABE), which simultaneously solves all the challenges by supporting all-sided accountability in the pay-as-you-go model. We formally define the security model and prove the security in the standard model. Also, we implement an instantiate construction with the self-developed library libabe. The experiment results indicate the efficiency and practicality of our construction.