Search

Deepfake is an advanced technology that creates extremely realistic facial images and videos. This new technique operates under specific conditions and has a wide range of applications. For example, it can be used in the entertainment industry to create impressive visual effects or to insert actors into scenes convincingly. Similarly, in the film industry, deepfakes can help make movies by faithfully reproducing the appearance of actors who are not physically present. It is also useful for creating realistic digital avatars of people, which can be used in virtual environments, video games, or augmented reality applications. Recently, the emergence of new content generation models capable of creating impressively realistic images has been gaining momentum. Despite their advantages, they also cause significant issues when used maliciously, such as for identity theft, misinformation, and obscene depictions of well-known individuals. Therefore, it is crucial to implement effective methods to expose this generated content and thus reduce crime associated with deepfakes. This article presents a novel method for detecting fake content based on an in-depth analysis of the characteristics of eye irises. By applying a gradient map to the iris, it is possible to visualize the biological characteristics specific to eye irises, such as the round shape, identical reflections in the two irises of the same face, the size of the iris, etc. The gradient map highlights all the contours of the objects present in the iris; thus, the reflected light present in the corneas is represented by brighter pixels comparable to heat. We show that two irises of the same face are almost identical in shape, reflection, and size. Our experimental results on the Flickr-Faces-HQ (FFHQ) dataset and images obtained from StyleGAN2 demonstrate that our algorithm achieves a remarkable detection accuracy of 0.979 and 0.921 sensitivity. Furthermore, the method has a specificity of 0.937 and a precision of 0.960, thereby proving the effectiveness of the gradient map associated with the shape of the pupil in detecting Generative adversarial network (GAN) generated faces.

This paper presents a scalable solution for zero-sum, one-sided, partially observable stochastic games (Z-POSGs) in the realm of cybersecurity. Existing heuristic search value iteration (HSVI) methods often face significant challenges with scalability in large and complex networks. To overcome this limitation, we introduce an innovative approach that leverages core attack graphs summarized abstractions that streamline incremental strategy generation. This technique reduces the belief and action spaces, making it possible to manage large-scale networks more efficiently. By focusing the analysis on the core attack graph, our approach minimizes the necessity to process the entire network, leading to substantial reductions in time and memory requirements while maintaining solution accuracy. Our method achieves an average solution accuracy within an epsilon value of 0.68%. Additionally, experimental results show that the execution time on the core attack graph is reduced by around 45% compared to that on the original graph, showcasing its significant performance advantage. Furthermore, we successfully solved the largest instances, comprising up to 300 nodes, underscoring the scalability and efficiency of our approach in handling complex, large-scale networks. Comparative evaluations demonstrate that integrating core attack graphs with existing HSVI techniques not only enhances scalability but also preserves solution quality. These findings highlight the proposed method’s potential for effective and efficient application in large-scale cybersecurity networks, outperforming state-of-the-art solutions.

Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node triggers subsequent failures across the network. To address this issue, we model our networks as unweighted, undirected graphs consisting of non-mobile nodes and use the host survival rate—the percentage of functional hosts after a failure—as the network’s critical function. Assuming the network includes a host-based restoration process as an inherent recovery mechanism that initiates after an incident, we compare the resilience of different network topologies (scale-free, regular lattice, and Erdőos-Rényi), by conducting computational experiments to assess the effect of various traffic generation patterns by placing hosts on high-degree, low-degree, or randomly selected nodes. This is done by developing a discrete mathematical framework for measuring cyber resilience as an integral of the critical function in discrete time steps. Our findings indicate that when the hosts have multiple connections to other nodes, it significantly enhances network resilience. Notably, scale-free networks demonstrate more than twice the resilience compared to other topologies, showing a host survival rate improvement from 0.2093 to 0.4498 when hosts are positioned on high-degree nodes as opposed to being placed randomly.

Advanced persistent threats (APTs) have emerged as multi-stage attacks that have targeted nation-states and their associated entities, including private and corporate sectors. Cyber deception has emerged as a defense approach to secure our cyber infrastructure from APTs. Practical deployment of cyber deception relies on defenders’ ability to place decoy nodes along the APT path optimally. This paper presents a cyber deception approach focused on predicting the most likely sequence of attack paths and deploying decoy nodes along the predicted path. Our proposed approach combines reactive (graph analysis) and proactive (cyber deception technology) defense to thwart the adversaries’ lateral movement. The proposed approach is realized through two phases. The first phase predicts the most likely attack path based on Intrusion Detection System (IDS) alerts and network trace, and the second phase is determining optimal deployment of decoy nodes along the predicted path. We employ transition probabilities in a Hidden Markov Model to predict the path. In the second phase, we utilize the predicted attack path to deploy decoy nodes. However, it is likely that the attacker will not follow that predicted path to move laterally. To address this challenge, we employ a Partially Observable Monte-Carlo Planning (POMCP) framework. POMCP helps the defender assess several defense actions to block the attacker when it deviates from the predicted path. The evaluation results show that our approach can predict the most likely attack paths and thwarts the adversarial lateral movement.