Biblio

It is proposed to address existing methodological issues in the educational process with the development of intellectual technologies and knowledge representation systems to improve the efficiency of higher education institutions. For this purpose, the structure of relational database is proposed, it will store the information about defended dissertations in the form of a set of attributes (heuristics), representing the mandatory qualification attributes of theses. An inference algorithm is proposed to process the information. This algorithm represents an artificial intelligence, its work is aimed at generating queries based on the applicant preferences. The result of the algorithm's work will be a set of choices, presented in ranked order. Given technologies will allow applicants to quickly become familiar with known scientific results and serve as a starting point for new research. The demand for co-researcher practice in solving the problem of updating the projective thinking methodology and managing the scientific research process has been justified. This article pays attention to the existing parallels between the concepts of technical and human sciences in the framework of their convergence. The concepts of being (economic good and economic utility) and the concepts of consciousness (humanitarian economic good and humanitarian economic utility) are used to form projective thinking. They form direct and inverse correspondences of technology and humanitarian practice in the techno-humanitarian mathematical space. It is proposed to place processed information from the language of context-free formal grammar dissertation abstracts in this space. The principle of data manipulation based on formal languages with context-free grammar allows to create new structures of subject areas in terms of applicants' preferences.It is believed that the success of applicants’ work depends directly on the cognitive training of applicants, which needs to be practiced psychologically. This practice is based on deepening the objectivity and adequacy qualities of obtaining information on the basis of heuristic methods. It requires increased attention and development of intelligence. The paper studies the use of heuristic methods by applicants to find new research directions leads to several promising results. These results can be perceived as potential options in future research. This contributes to an increase in the level of retention of higher education professionals.

Graph-based Semi-Supervised Learning (GSSL) is a practical solution to learn from a limited amount of labelled data together with a vast amount of unlabelled data. However, due to their reliance on the known labels to infer the unknown labels, these algorithms are sensitive to data quality. It is therefore essential to study the potential threats related to the labelled data, more specifically, label poisoning. In this paper, we propose a novel data poisoning method which efficiently approximates the result of label inference to identify the inputs which, if poisoned, would produce the highest number of incorrectly inferred labels. We extensively evaluate our approach on three classification problems under 24 different experimental settings each. Compared to the state of the art, our influence-driven attack produces an average increase of error rate 50% higher, while being faster by multiple orders of magnitude. Moreover, our method can inform engineers of inputs that deserve investigation (relabelling them) before training the learning model. We show that relabelling one-third of the poisoned inputs (selected based on their influence) reduces the poisoning effect by 50%. ACM Reference Format: Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, and Yves Le Traon. 2022. Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN’22), May 16–24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3522664.3528606

The main aim of the Internet of things is to improve the safety of the device through inter-Device communication (IDC). Various applications are emerging in Internet of things. Various aspects of Internet of things differ from Internet of things, especially the nodes have more velocity which causes the topology to change rapidly. The requirement of researches in the concept of Internet of things increases rapidly because Internet of things face many challenges on the security, protocols and technology. Despite the fact that the problem of organizing the interaction of IIoT devices has already attracted a lot of attention from many researchers, current research on routing in IIoT cannot effectively solve the problem of data exchange in a self-adaptive and self-organized way, because the number of connected devices is quite large. In this article, an adaptive neuro-fuzzy clustering algorithm is presented for the uniform distribution of load between interacting nodes. We synthesized fuzzy logic and neural network to balance the choice of the optimal number of cluster heads and uniform load distribution between sensors. Comparison is made with other load balancing methods in such wireless sensor networks.

This article discusses the problem of information security management in computer systems and describes the process of developing an algorithm that allows to determine measures to protect personal data. The organizational and technical measures formulated by the FSTEC are used as measures.

Quasi-identifier is an attribute combined with other attributes to identify specific tuples or partial tuples. Improper selection of quasi-identifiers will lead to the failure of current privacy protection anonymization technology. Therefore, in this paper, we propose a method to solve single structured relational data quasi-identifiers based on functional dependency and determines the attribute classification standard. Firstly, the solution scope of quasi-identifier is determined to be all attributes except identity attributes and critical attributes. Secondly, the real data set is used to evaluate the dependency relationship between the indefinite attribute subset and the identity attribute to solve the quasi-identifiers set. Finally, we propose an algorithm to find all quasi-identifiers and experiment on real data sets of different sizes. The results show that our method can achieve better performance on the same dataset.

The Internet of vehicles is emerging as an exciting application to improve safety and providing better services in the form of active road signs, pay-as-you-go insurance, electronic toll, and fleet management. Internet connected vehicles are exposed to new attack vectors in the form of cyber threats and with the increasing trend of cyber attacks, the success of autonomous vehicles depends on their security. Existing techniques for IoV security are based on the un-realistic assumption that all the vehicles are equipped with the same hardware (at least in terms of computational capabilities). However, the hardware platforms used by various vehicle manufacturers are highly heterogeneous. Therefore, a security protocol designed for IoVs should be able to detect the computational capabilities of the underlying platform and adjust the security primitives accordingly. To solve this issue, this paper presents a technique for algorithm-hardware separation for IoV security. The proposed technique uses an iterative routine and the corresponding execution time to detect the computational capabilities of a hardware platform using an artificial intelligence based inference engine. The results on three different commonly used micro-controllers show that the proposed technique can effectively detect the type of hardware platform with up to 100% accuracy.

The risk posed by high-profile data breaches has raised the stakes for adhering to data access policies for many organizations, but the complexity of both the policies themselves and the applications that must obey them raises significant challenges. To mitigate this risk, fine-grained audit of access to private data has become common practice, but this is a costly, time-consuming, and error-prone process.We propose an approach for automating much of the work required for fine-grained audit of private data access. Starting from the assumption that the auditor does not have an explicit, formal description of the correct policy, but is able to decide whether a given policy fragment is partially correct, our approach gradually infers a policy from audit log entries. When the auditor determines that a proposed policy fragment is appropriate, it is added to the system's mechanized policy, and future log entries to which the fragment applies can be dealt with automatically. We prove that for a general class of attribute-based data policies, this inference process satisfies a monotonicity property which implies that eventually, the mechanized policy will comprise the full set of access rules, and no further manual audit is necessary. Finally, we evaluate this approach using a case study involving synthetic electronic medical records and the HIPAA rule, and show that the inferred mechanized policy quickly converges to the full, stable rule, significantly reducing the amount of effort needed to ensure compliance in a practical setting.

When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.

Face capturing is a task to capture and store the "best" face of each person passing by the monitor. To some extent, it is similar to face tracking, but uses a different criterion and requires a valuable (i.e., high-quality and recognizable) face selection procedure. Face capturing systems play a critical role in public security. When deployed on edge devices, it is capable of reducing redundant storage in data center and speeding up retrieval of a certain person. However, high computation complexity and high repetition rate caused by ID switch errors are major challenges. In this paper, we propose a novel solution to constructing a real-time low-repetition face capturing system on chips. First, we propose a two-stage association algorithm for memory-efficient and accurate face tracking. Second, we propose a fast and reliable face quality estimation algorithm for valuable face selection. Our pipeline runs at over 20fps on Hisiv 3559A SoC with a single NNIE device for neural network inference, while achieving over 95% recall and less than 0.4 repetition rate in real world surveillance videos.

Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference, or can be identified during the validation phase. There-fore, data poisoning attacks during inference (e.g., adversarial attacks) are becoming more popular. However, many of them do not consider the imperceptibility factor in their optimization algorithms, and can be detected by correlation and structural similarity analysis, or noticeable (e.g., by humans) in multi-level security system. Moreover, majority of the inference attack rely on some knowledge about the training dataset. In this paper, we propose a novel methodology which automatically generates imperceptible attack images by using the back-propagation algorithm on pre-trained DNNs, without requiring any information about the training dataset (i.e., completely training data-unaware). We present a case study on traffic sign detection using the VGGNet trained on the German Traffic Sign Recognition Benchmarks dataset in an autonomous driving use case. Our results demonstrate that the generated attack images successfully perform misclassification while remaining imperceptible in both “subjective” and “objective” quality tests.

Because of the high-value data of an enterprise, sophisticated cyber-attacks targeted at enterprise networks have become prominent. Attack graphs are useful tools that facilitate a scalable security analysis of enterprise networks. However, the administrators face difficulties in effectively modelling security problems and making right decisions when constructing attack graphs as their risk assessment experience is often limited. In this paper, we propose an innovative method of security assessment through an ontology- and graph-based approach. An ontology is designed to represent security knowledge such as assets, vulnerabilities, attacks, countermeasures, and relationships between them in a common vocabulary. An efficient algorithm is proposed to generate an attack graph based on the inference ability of the security ontology. The proposed algorithm is evaluated with different sizes and topologies of test networks; the results show that our proposed algorithm facilitates a scalable security analysis of enterprise networks.

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.

Inference based techniques are one of the major approaches to analyze DNS data and detect malicious domains. The key idea of inference techniques is to first define associations between domains based on features extracted from DNS data. Then, an inference algorithm is deployed to infer potential malicious domains based on their direct/indirect associations with known malicious ones. The way associations are defined is key to the effectiveness of an inference technique. It is desirable to be both accurate (i.e., avoid falsely associating domains with no meaningful connections) and with good coverage (i.e., identify all associations between domains with meaningful connections). Due to the limited scope of information provided by DNS data, it becomes a challenge to design an association scheme that achieves both high accuracy and good coverage. In this paper, we propose a new approach to identify domains controlled by the same entity. Our key idea is an in-depth analysis of active DNS data to accurately separate public IPs from dedicated ones, which enables us to build high-quality associations between domains. Our scheme avoids the pitfall of naive approaches that rely on weak "co-IP" relationship of domains (i.e., two domains are resolved to the same IP) that results in low detection accuracy, and, meanwhile, identifies many meaningful connections between domains that are discarded by existing state-of-the-art approaches. Our experimental results show that the proposed approach not only significantly improves the domain coverage compared to existing approaches but also achieves better detection accuracy. Existing path-based inference algorithms are specifically designed for DNS data analysis. They are effective but computationally expensive. To further demonstrate the strength of our domain association scheme as well as improve the inference efficiency, we construct a new domain-IP graph that can work well with the generic belief propagation algorithm. Through comprehensive experiments, we show that this approach offers significant efficiency and scalability improvement with only a minor impact to detection accuracy, which suggests that such a combination could offer a good tradeoff for malicious domain detection in practice.

Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine learning of mobile Android applications in combination with algorithms that discover attack paths in the learned state machine. The presence of an attack path evidences the existence of a vulnerability in the mobile application. We apply our methods to real-life apps and show that the novel methodology is capable of identifying vulnerabilities.

As a vital component of variety cyber attacks, malicious domain detection becomes a hot topic for cyber security. Several recent techniques are proposed to identify malicious domains through analysis of DNS data because much of global information in DNS data which cannot be affected by the attackers. The attackers always recycle resources, so they frequently change the domain - IP resolutions and create new domains to avoid detection. Therefore, multiple malicious domains are hosted by the same IPs and multiple IPs also host same malicious domains in simultaneously, which create intrinsic association among them. Hence, using the labeled domains which can be traced back from queries history of all domains to verify and figure out the association of them all. Graphs seem the best candidate to represent for this relationship and there are many algorithms developed on graph with high performance. A graph-based interface can be developed and transformed to the graph mining task of inferring graph node's reputation scores using improvements of the belief propagation algorithm. Then higher reputation scores the nodes reveal, the more malicious probabilities they infer. For demonstration, this paper proposes a malicious domain detection technique and evaluates on a real-world dataset. The dataset is collected from DNS data servers which will be used for building a DNS graph. The proposed technique achieves high performance in accuracy rates over 98.3%, precision and recall rates as: 99.1%, 98.6%. Especially, with a small set of labeled domains (legitimate and malicious domains), the technique can discover a large set of potential malicious domains. The results indicate that the method is strongly effective in detecting malicious domains.

The modern day approach in boulevard network centers on efficient factor in safe routing. The safe routing must follow up the low risk cities. The troubles in routing are a perennial one confronting people day in and day out. The common goal of everyone using a boulevard seems to be reaching the desired point through the fastest manner which involves the balancing conundrum of multiple expected and unexpected influencing factors such as time, distance, security and cost. It is universal knowledge that travelling is an almost inherent aspect in everyone's daily routine. With the gigantic and complex road network of a modern city or country, finding a low risk community for traversing the distance is not easy to achieve. This paper follows the code based community for detecting the boulevard network and fuzzy technique for identifying low risk community.

Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of >45% and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.