ANALYSIS OF USERS’ PROTECTION FROM SOCIO-ENGINEERING ATTACKS: SOCIAL GRAPH CREATION BASED ON INFORMATION FROM SOCIAL NETWORK WEBSITES

Subject of Research. The paper deals with accounts in social network websites as a source of information about the intensity of communication between employees in the team. On their basis we form success probability estimates for the spread of malefactorsocio-engineering attack on the user. Scope of Research. The research goal is to build a success assessment for malefactormulti-pass socio-engineering attack on the user based on information obtained from the accounts of company employees in social network websites which characterizes communication intensity between them. The research is aimed at development of models and algorithms for socio-engineering attack spreading on the collapsed social graph of the company and description of methods for calculation of security estimates for the information system users from multi-pass socio-engineering attacks, such attacks, where the target and the entry point do not match. Method. The methods are used of information searching, comparing and analyzing, which characterizes communication intensity between company employees, and data extracted from their accounts in social network websites. Success probability estimate of multi-pass socio-engineering attack reduces to probability estimate creation of a complex event. Main Results. A formula is presented for calculating of probability estimates of socio-engineering attack propagation between users. The estimates obtained in this way are compared to the arcs in the company's social graph, which is used in turn to assess the success probability of a multi-pass socio-engineering attack, the attack, passing through a chain of users. In the earlier studies, estimates of probabilities were defined expertly. The advantages of calculation automating of probability estimates based on data received from social network websites are described. Research Novelty.The paper considers approaches to probabilistic estimates of multi-pass socio-engineering attack success where attacks are intermediate, non-direct, and non-reducible to a single malefactoract. These estimates take into account user’s links in his or her social graph; the parameters of those links are based on the data obtained from social media/networks. Practical Relevance.The approach proposed in this paper provides the basis for further analysis of possible propagation trajectories of multi-pass social engineering attacks, as well as calculation of the probability of each such trajectory that in turn helps to expand the number of factors affecting the security evaluation of the information system users, and gives the possibility to set the backtracking task for attacks in one of the successful forms for finding solutions

The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches

Influence maximization is among the most fundamental algorithmic problems in social influence analysis. Over the last decade, a great effort has been devoted to developing efficient algorithms for influence maximization, so that identifying the ``best'' algorithm has become a demanding task. In SIGMOD'17, Arora, Galhotra, and Ranu reported benchmark results on eleven existing algorithms and demonstrated that there is no single state-of-the-art offering the best trade-off between computational efficiency and solution quality. In this paper, we report a high-level experimental study on three well-established algorithmic approaches for influence maximization, referred to as Oneshot, Snapshot, and Reverse Influence Sampling (RIS). Different from Arora et al., our experimental methodology is so designed that we examine the distribution of random solutions, characterize the relation between the sample number and the actual solution quality, and avoid implementation dependencies. Our main findings are as follows: 1. For a sufficiently large sample number, we obtain a unique solution regardless of algorithms. 2. The average solution quality of Oneshot, Snapshot, and RIS improves at the same rate up to scaling of sample number. 3. Oneshot requires more samples than Snapshot, and Snapshot requires fewer but larger samples than RIS. We discuss the time efficiency when conditioning Oneshot, Snapshot, and RIS to be of identical accuracy. Our conclusion is that Oneshot is suitable only if the size of available memory is limited, and RIS is more efficient than Snapshot for large networks; Snapshot is preferable for small, low-probability networks.Comment: To appear in SIGMOD 202

Maximum Recommendation in Geo-social Network for Business

Most of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease the recommendation effect but also increase the recommendation cost in the business. To remove the shortcomings, we propose a Maximum Recommendation scheme in Geo-social network for business (called as MRG). On the one hand, we identify k nodes with maximum recommendation according to the expected paid node number k. On the other hand, we exclude the negative node from the geo-social network. Based on the above innovation, we effectively increase the recommendation effect and decrease the company\u27s recommendation cost. Meanwhile, MRG considers the negative influence to enhance the recommendation efficiency. Experimental results show that our scheme has better performance than most of the existing methods in the maximum recommendation field

Influential Slot and Tag Selection in Billboard Advertisement

The selection of influential billboard slots remains an important problem in billboard advertisements. Existing studies on this problem have not considered the case of context-specific influence probability. To bridge this gap, in this paper, we introduce the Context Dependent Influential Billboard Slot Selection Problem. First, we show that the problem is NP-hard. We also show that the influence function holds the bi-monotonicity, bi-submodularity, and non-negativity properties. We propose an orthant-wise Stochastic Greedy approach to solve this problem. We show that this method leads to a constant factor approximation guarantee. Subsequently, we propose an orthant-wise Incremental and Lazy Greedy approach. In a generic sense, this is a method for maximizing a bi-submodular function under the cardinality constraint, which may also be of independent interest. We analyze the performance guarantee of this algorithm as well as time and space complexity. The proposed solution approaches have been implemented with real-world billboard and trajectory datasets. We compare the performance of our method with many baseline methods, and the results are reported. Our proposed orthant-wise stochastic greedy approach leads to significant results when the parameters are set properly with reasonable computational overhead.Comment: 15 page

Influence Maximization in Social Networks: A Survey

Online social networks have become an important platform for people to communicate, share knowledge and disseminate information. Given the widespread usage of social media, individuals' ideas, preferences and behavior are often influenced by their peers or friends in the social networks that they participate in. Since the last decade, influence maximization (IM) problem has been extensively adopted to model the diffusion of innovations and ideas. The purpose of IM is to select a set of k seed nodes who can influence the most individuals in the network. In this survey, we present a systematical study over the researches and future directions with respect to IM problem. We review the information diffusion models and analyze a variety of algorithms for the classic IM algorithms. We propose a taxonomy for potential readers to understand the key techniques and challenges. We also organize the milestone works in time order such that the readers of this survey can experience the research roadmap in this field. Moreover, we also categorize other application-oriented IM studies and correspondingly study each of them. What's more, we list a series of open questions as the future directions for IM-related researches, where a potential reader of this survey can easily observe what should be done next in this field

Spreading information in social networks containing adversarial users

In the modern day, social networks have become an integral part of how people communicate information and ideas. Consequently, leveraging the network to maximize information spread is a science that is applied in viral marketing, political propaganda. In social networks, an idea/information starts from a small group of users (known as seed users) and is propagated through the network via connections of the seed users. There are limitations on the number of seed users that can be convinced to adopt a certain idea. Therefore, the problem exists in finding a small set of users who can maximally spread an idea/information. This is known as the influence maximization problem. While this problem has been studied extensively, the presence of potential adversarial users and their impact on the information spread, has not been considered in existing solutions.In this thesis, we study the problem of spreading information to Target users while limiting the spread from reaching adversarial(Non Target) users. To this end, we consider a hard constraint - the objective is to maximize the information spread among the Target users while the number of Non-Target users to whom the information reaches is limited by a hard constraint. We design two algorithms - Natural Greedy and Multi Greedy with efficient RIS based implementations. We run our solutions on real world social networks to study the information spread. Finally, we evaluate the quality of our solutions on different models of diffusion and network settings

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page