System auditing is a crucial technique for detecting APT attacks. However, attackers may try to compromise the system auditing frameworks to conceal their malicious activities. In this paper, we present a comprehensive and systematic study of the super producer threat in auditing frameworks, which enables attackers to either corrupt the auditing framework or paralyze the entire system. We analyze that the main cause of the super producer threat is the lack of data isolation in the centralized architecture of existing solutions. To address this threat, we propose a novel auditing framework, NODROP, which isolates provenance data generated by different processes with a threadlet-based architecture design. Our evaluation demonstrates that NODROP can ensure the integrity of the auditing frameworks while achieving an average 6.58% higher application overhead compared to vanilla Linux and 6.30% lower application overhead compared to a state-of-the-art commercial auditing framework, Sysdig across eight different hardware configurations.
相關內容
In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a promising avenue. However, current methods often assume full observability of the scene and struggle with scale invariance. In many tasks and settings, this assumption does not hold as objects in the scene are often occluded or lie outside the field of view of the camera, rendering the camera observation ambiguous with regard to their location. To tackle this problem, we present BASK, a Bayesian approach to tracking scale-invariant keypoints over time. Our approach successfully resolves inherent ambiguities in images, enabling keypoint tracking on symmetrical objects and occluded and out-of-view objects. We employ our method to learn challenging multi-object robot manipulation tasks from wrist camera observations and demonstrate superior utility for policy learning compared to other representation learning techniques. Furthermore, we show outstanding robustness towards disturbances such as clutter, occlusions, and noisy depth measurements, as well as generalization to unseen objects both in simulation and real-world robotic experiments.
Thematic analysis is a cornerstone of qualitative research, yet it is often marked by labor-intensive procedures. Recent advances in artificial intelligence (AI), especially with large-scale language models (LLMs) such as ChatGPT, present potential avenues to enhance qualitative data analysis. This research delves into the effectiveness of ChatGPT in refining the thematic analysis process. We conducted semi-structured interviews with 17 participants, inclusive of a 4-participant pilot study, to identify the challenges and reservations concerning the incorporation of ChatGPT in qualitative analysis. In partnership with 13 qualitative analysts, we crafted cueing frameworks to bolster ChatGPT's contribution to thematic analysis. The results indicate that these frameworks not only amplify the quality of thematic analysis but also bridge a significant connection between AI and qualitative research. These insights carry pivotal implications for academics and professionals keen on harnessing AI for qualitative data exploration.
We investigate in this work a recently emerging type of scam token called Trapdoor, which has caused the investors hundreds of millions of dollars in the period of 2020-2023. In a nutshell, by embedding logical bugs and/or owner-only features to the smart contract codes, a Trapdoor token allows users to buy but prevent them from selling. We develop the first systematic classification of Trapdoor tokens and a comprehensive list of their programming techniques, accompanied by a detailed analysis on representative scam contracts. We also construct the very first dataset of 1859 manually verified Trapdoor tokens on Uniswap and build effective opcode-based detection tools using popular machine learning classifiers such as Random Forest, XGBoost, and LightGBM, which achieve at least 0.98% accuracies, precisions, recalls, and F1-scores
Knowledge is considered an essential resource for organizations. For organizations to benefit from their possessed knowledge, knowledge needs to be managed effectively. Despite knowledge sharing and management being viewed as important by practitioners, organizations fail to benefit from their knowledge, leading to issues in cooperation and the loss of valuable knowledge with departing employees. This study aims to identify hindering factors that prevent individuals from effectively sharing and managing knowledge and understand how to eliminate these factors. Empirical data were collected through semi-structured group interviews from 50 individuals working in an international large IT organization. This study confirms the existence of a gap between the perceived importance of knowledge management and how little this importance is reflected in practice. Several hindering factors were identified, grouped into personal social topics, organizational social topics, technical topics, environmental topics, and interrelated social and technical topics. The presented recommendations for mitigating these hindering factors are focused on improving employees' actions, such as offering training and guidelines to follow. The findings of this study have implications for organizations in knowledge-intensive fields, as they can use this knowledge to create knowledge sharing and management strategies to improve their overall performance.
Honeypots play a crucial role in implementing various cyber deception techniques as they possess the capability to divert attackers away from valuable assets. Careful strategic placement of honeypots in networks should consider not only network aspects but also attackers' preferences. The allocation of honeypots in tactical networks under network mobility is of great interest. To achieve this objective, we present a game-theoretic approach that generates optimal honeypot allocation strategies within an attack/defense scenario. Our proposed approach takes into consideration the changes in network connectivity. In particular, we introduce a two-player dynamic game model that explicitly incorporates the future state evolution resulting from changes in network connectivity. The defender's objective is twofold: to maximize the likelihood of the attacker hitting a honeypot and to minimize the cost associated with deception and reconfiguration due to changes in network topology. We present an iterative algorithm to find Nash equilibrium strategies and analyze the scalability of the algorithm. Finally, we validate our approach and present numerical results based on simulations, demonstrating that our game model successfully enhances network security. Additionally, we have proposed additional enhancements to improve the scalability of the proposed approach.
Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate Activation Maps (AMs) and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing AMs could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies homomorphic encryption on the AMs before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.
Recommender systems are used to provide relevant suggestions on various matters. Although these systems are a classical research topic, knowledge is still limited regarding the public opinion about these systems. Public opinion is also important because the systems are known to cause various problems. To this end, this paper presents a qualitative analysis of the perceptions of ordinary citizens, civil society groups, businesses, and others on recommender systems in Europe. The dataset examined is based on the answers submitted to a consultation about the Digital Services Act (DSA) recently enacted in the European Union (EU). Therefore, not only does the paper contribute to the pressing question about regulating new technologies and online platforms, but it also reveals insights about the policy-making of the DSA. According to the qualitative results, Europeans have generally negative opinions about recommender systems and the quality of their recommendations. The systems are widely seen to violate privacy and other fundamental rights. According to many Europeans, these also cause various societal problems, including even threats to democracy. Furthermore, existing regulations in the EU are commonly seen to have failed due to a lack of proper enforcement. Numerous suggestions were made by the respondents to the consultation for improving the situation, but only a few of these ended up to the DSA.
Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate more reliably without human interventions. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how these decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds a comprehensive light on developing explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the present gaps with respect to explanations in the state-of-the-art autonomous vehicle industry. We then show the taxonomy of explanations and explanation receivers in this field. Thirdly, we propose a framework for an architecture of end-to-end autonomous driving systems and justify the role of XAI in both debugging and regulating such systems. Finally, as future research directions, we provide a field guide on XAI approaches for autonomous driving that can improve operational safety and transparency towards achieving public approval by regulators, manufacturers, and all engaged stakeholders.
Current deep learning research is dominated by benchmark evaluation. A method is regarded as favorable if it empirically performs well on the dedicated test set. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving sets of benchmark data are investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten due to the iterative parameter updates. However, comparison of individual methods is nevertheless treated in isolation from real world application and typically judged by monitoring accumulated test set performance. The closed world assumption remains predominant. It is assumed that during deployment a model is guaranteed to encounter data that stems from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown instances and break down in the face of corrupted data. In this work we argue that notable lessons from open set recognition, the identification of statistically deviating data outside of the observed dataset, and the adjacent field of active learning, where data is incrementally queried such that the expected performance gain is maximized, are frequently overlooked in the deep learning era. Based on these forgotten lessons, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Our results show that this not only benefits each individual paradigm, but highlights the natural synergies in a common framework. We empirically demonstrate improvements when alleviating catastrophic forgetting, querying data in active learning, selecting task orders, while exhibiting robust open world application where previously proposed methods fail.
Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursue good learning performance, human experts are heavily involved in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.
小貼士
登錄享
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191