How (Not) to Index Order Revealing Encrypted Databases

Order Reveling Encryption (ORE) enables efficient range queries on encrypted databases, but may leak information that could be exploited by inference attacks. State-of-the-art ORE schemes claim different security guarantees depending on the adversary attack surface. Intuitively, online adversaries who access the database server at runtime may access information leakage; offline adversaries who access only a snapshot of the database data should not be able to gain useful information. We focus on offline security of the ORE scheme proposed by Lewi and Wu (LW-ORE, CCS 2016), which guarantees semantic security of ciphertexts stored in the database, but requires that ciphertexts are maintained sorted with regard to the corresponding plaintexts to support sublinear time queries. The design of LW-ORE does not discuss how to build indexing data structures to maintain sorting. The risk is that practitioners consider indexes as a technicality whose design does not affect security. We show that indexes can affect offline security of LW-ORE because they may leak duplicate plaintext values, and statistical information on plaintexts distribution and on transactions history. As a real-world demonstration, we found two open source implementations related to academic research (JISA 2018, VLDB 2019), and both adopt standard search trees which may introduce such vulnerabilities. We discuss necessary conditions for indexing data structures to be secure for ORE databases, and we outline practical solutions. Our analyses could represent an insightful lesson in the context of security failures due to gaps between theoretical modeling and actual implementation, and may also apply to other cryptographic techniques for securing outsourced databases

A Framework for Automating Security Assessments with Deductive Reasoning

Proper testing of hardware and software infrastructure and applications has become mandatory. To this purpose, security researchers and software companies have released a plethora of domain specific tools, libraries and frameworks that assist human operators (penetration testers, red teamers, bug hunters) in finding and exploiting specific vulnerabilities, and orchestrating the activities of a security assessment. Most tools also require minor reconfigurations in order to operate properly with isomorphic systems, characterized by the same exploitation path even in presence of different configurations. In this paper we present a human-assisted framework that tries to overcome the aforementioned limitations. Our proposal is based on a Prolog-based expert system with facts and deductive rules that allow to infer new facts from existing ones. Rules are bound to actions whose results are fed back into the knowledge base as further facts. In this way, a security assessment is treated like a theorem that has to be proven. We have built an initial prototype and evaluated it in different security assessments of increasing complexity (jeopardy and boot-to-root machines). Our preliminary results show that the proposed approach can address the following challenges; (a) reaching non-standard goals (which would be missed by most tools and frameworks); (b) solving isomorphic systems without the need for reconfiguration; (c) identifying vulnerabilities from chained weaknesses and exposures

AppCon: Mitigating evasion attacks to ML cyber detectors

Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature. We address this problem by presenting AppCon, an original approach to harden intrusion detectors against adversarial evasion attacks. Our proposal leverages the integration of ensemble learning to realistic network environments, by combining layers of detectors devoted to monitor the behavior of the applications employed by the organization. Our proposal is validated through extensive experiments performed in heterogeneous network settings simulating botnet detection scenarios, and consider detectors based on distinct machine-and deep-learning algorithms. The results demonstrate the effectiveness of AppCon in mitigating the dangerous threat of adversarial attacks in over 75% of the considered evasion attempts, while not being affected by the limitations of existing countermeasures, such as performance degradation in non-adversarial settings. For these reasons, our proposal represents a valuable contribution to the development of more secure cyber defense platforms

Practical Evaluation of Graph Neural Networks in Network Intrusion Detection

The most recent proposals of Machine and Deep Learning algorithms for Network Intrusion Detection Systems (NIDS) leverage Graph Neural Networks (GNN). These techniques create a graph representation of network traffic and analyze both network topology and netflow features to produce more accurate predictions. Although prior research shows promising results, they are biased by evaluation methodologies that are incompatible with real-world online intrusion detection. We are the first to identify these issues and to evaluate the performance of a state-of-the-art GNN-NIDS under real-world constraints. The experiments demonstrate that the literature overestimates the detection performance of GNN-based NIDS. Our results analyze and discuss the trade-off between detection delay and detection performance for different types of attacks, thus paving the way for the practical deployment of GNN-based NIDS

DOLOS: A Novel Architecture for Moving Target Defense

Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asymmetric disadvantage for the attacker by using deception and randomization techniques to create a dynamic attack surface. Moving Target Defense (MTD) typically relies on system randomization and diversification, while Cyber Deception is based on decoy nodes and fake systems to deceive attackers. However, current Moving Target Defense techniques are complex to manage and can introduce high overheads, while Cyber Deception nodes are easily recognized and avoided by adversaries. This paper presents DOLOS, a novel architecture that unifies Cyber Deception and Moving Target Defense approaches. DOLOS is motivated by the insight that deceptive techniques are much more powerful when integrated into production systems rather than deployed alongside them. DOLOS combines typical Moving Target Defense techniques, such as randomization, diversity, and redundancy, with cyber deception and seamlessly integrates them into production systems through multiple layers of isolation. We extensively evaluate DOLOS against a wide range of attackers, ranging from automated malware to professional penetration testers, and show that DOLOS is effective in slowing down attacks and protecting the integrity of production systems. We also provide valuable insights and considerations for the future development of MTD techniques based on our findings

An Energy Aware Cost Recovery Approach for Virtual Machine Migration

Datacenters provide an IT backbone for today's business and economy, and are the principal electricity consumers for Cloud computing. Various studies suggest that approximately 30% of the running servers in US datacenters are idle and the others are under-utilized, making it possible to save energy and money by using Virtual Machine (VM) consolidation to reduce the number of hosts in use. However, consolidation involves migrations that can be expensive in terms of energy consumption, and sometimes it will be more energy efficient not to consolidate. This paper investigates how migration decisions can be made such that the energy costs involved with the migration are recovered, as only when costs of migration have been recovered will energy start to be saved. We demonstrate through a number of experiments, using the Google workload traces for 12,583 hosts and 1,083,309 tasks, how different VM allocation heuristics, combined with different approaches to migration, will impact on energy effciency. We suggest, using reasonable assumptions for datacenter setup, that a combination of energy-aware ll-up VM allocation and energy-aware migration, and migration only for relatively long running VMs, provides for optimal energy efficiency

Marchi e Brevetti, guida teorico-pratica alla proprietà industriale

Il libro illustra i principi della Proprietà Industriale ed è aggiornato al D.Lgs. 13 agosto 2010, n. 131. Il libro è diviso in quattro parti: I) Principi comuni; II) Marchi; III) Brevetti, modelli di utilità, nuove varietà vegetali, disegni o modelli; IV) Tutela giurisdizionale dei diritti di proprietà industrial