Cryptosystems I.- On Ideal Lattices and Learning with Errors over Rings.- Fully Homomorphic Encryption over the Integers.- Converting Pairing-Based Cryptosystems from Composite-Order Groups to Prime-Order Groups.- Fully Secure Functional Encryption: Attribute-Based Encryption and (Hierarchical) Inner Product Encryption.- Obfuscation and Side Channel Security.- Secure Obfuscation for Encrypted Signatures.- Public-Key Encryption in the Bounded-Retrieval Model.- Protecting Circuits from Leakage: the Computationally-Bounded and Noisy Cases.- 2-Party Protocols.- Partial Fairness in Secure Two-Party Computation.- Secure Message Transmission with Small Public Discussion.- On the Impossibility of Three-Move Blind Signature Schemes.- Efficient Device-Independent Quantum Key Distribution.- Cryptanalysis.- New Generic Algorithms for Hard Knapsacks.- Lattice Enumeration Using Extreme Pruning.- Algebraic Cryptanalysis of McEliece Variants with Compact Keys.- Key Recovery Attacks of Practical Complexity on AES-256 Variants with up to 10 Rounds.- IACR Distinguished Lecture.- Cryptography between Wonderland and Underland.- Automated Tools and Formal Methods.- Automatic Search for Related-Key Differential Characteristics in Byte-Oriented Block Ciphers: Application to AES, Camellia, Khazad and Others.- Plaintext-Dependent Decryption: A Formal Security Treatment of SSH-CTR.- Computational Soundness, Co-induction, and Encryption Cycles.- Models and Proofs.- Encryption Schemes Secure against Chosen-Ciphertext Selective Opening Attacks.- Cryptographic Agility and Its Relation to Circular Encryption.- Bounded Key-Dependent Message Security.- Multiparty Protocols.- Perfectly Secure Multiparty Computation and the Computational Overhead of Cryptography.- Adaptively Secure Broadcast.- Universally Composable Quantum Multi-party Computation.- Cryptosystems II.- A Simple BGN-Type Cryptosystem from LWE.- Bonsai Trees, or How to Delegate a Lattice Basis.- Efficient Lattice (H)IBE in the Standard Model.- Hash and MAC.- Multi-property-preserving Domain Extension Using Polynomial-Based Modes of Operation.- Stam's Collision Resistance Conjecture.- Universal One-Way Hash Functions via Inaccessible Entropy.- Foundational Primitives.- Constant-Round Non-malleable Commitments from Sub-exponential One-Way Functions.- Constructing Verifiable Random Functions with Large Input Spaces.- Adaptive Trapdoor Functions and Chosen-Ciphertext Security.

Advances in cryptology -- EUROCRYPT 2010 : 29th Annual International Conference on the Theory and Applications of Cryptographic Techniques, French Riviera, May 30-June 3, 2010 : proceedings

Reactive defense mechanisms, such as intrusion detection systems, have made significant efforts to secure a system or network for the last several decades. However, the nature of reactive security mechanisms has limitations because potential attackers cannot be prevented in advance. We are facing a reality with the proliferation of persistent, advanced, intelligent attacks while defenders are often way behind attackers in taking appropriate actions to thwart potential attackers. The concept of moving target defense (MTD) has emerged as a proactive defense mechanism aiming to prevent attacks. In this work, we conducted a comprehensive, in-depth survey to discuss the following aspects of MTD: key roles, design principles, classifications, common attacks, key methodologies, important algorithms, metrics, evaluation methods, and application domains. We discuss the pros and cons of all aspects of MTD surveyed in this work. Lastly, we highlight insights and lessons learned from this study and suggest future work directions. The aim of this paper is to provide the overall trends of MTD research in terms of critical aspects of defense systems for researchers who seek to develop proactive, adaptive MTD mechanisms.

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Due to the nature of this material, this document refers to numerous hardware and software products by their trade names. References to other companies and their products are for informational purposes only, and all trademarks are the properties of their respective companies. It is not the intent of ProTech Professional Technical Services, Inc. to use any of these names generically C o u rs e O u tl in e Writing Secure Code

Writing Secure Code

Network defenses based on traditional tools, techniques, and procedures (TTP) fail to account for the attacker’s inherent advantage present due to the static nature of network services and configurations. To take away this asymmetric advantage, Moving Target Defense (MTD) continuously shifts the configuration of the underlying system, in turn reducing the success rate of cyberattacks. In this survey, we analyze the recent advancements made in the development of MTDs and highlight (1) how these defenses can be defined using common terminology, (2) can be made more effective with the use of artificial intelligence techniques for decision making, (3) be implemented in practice and (4) evaluated. We first define an MTD using a simple and yet general notation that captures the key aspects of such defenses. We then categorize these defenses into different sub-classes depending on what they move, when they move and how they move. In trying to answer the latter question, we showcase the use of domain knowledge and game-theoretic modeling can help the defender come up with effective and efficient movement strategies. Second, to understand the practicality of these defense methods, we discuss how various MTDs have been implemented and find that networking technologies such as Software Defined Networking and Network Function Virtualization act as key enablers for implementing these dynamic defenses. We then briefly highlight MTD test-beds and case-studies to aid readers who want to examine or deploy existing MTD techniques. Third, our survey categorizes proposed MTDs based on the qualitative and quantitative metrics they utilize to evaluate their effectiveness in terms of security and performance. We use well-defined metrics such as risk analysis and performance costs for qualitative evaluation and metrics based on Confidentiality, Integrity, Availability (CIA), attack representation, QoS impact, and targeted threat models for quantitative evaluation. Finally, we show that our categorization of MTDs is effective in identifying novel research areas and highlight directions for future research.

A Survey of Moving Target Defenses for Network Security

There are a growing number of people who hold accounts on social media platforms (SMPs) but hide their identity for malicious purposes Unfortunately, very little research has been done to date to detect fake identities created by humans, especially so on SMPs In contrast, many examples exist of cases where fake accounts created by bots or computers have been detected successfully using machine learning models In the case of bots these machine learning models were dependent on employing engineered features, such as the “friend-to-followers ratio” These features were engineered from attributes, such as “friend-count” and “follower-count,” which are directly available in the account profiles on SMPs The research discussed in this paper applies these same engineered features to a set of fake human accounts in the hope of advancing the successful detection of fake identities created by humans on SMPs

Using Machine Learning to Detect Fake Identities: Bots vs Humans

Distributed Denial of Service attacks against high-profile targets have become more frequent in recent years. In response to such massive attacks, several architectures have adopted proxies to introduce layers of indirection between end users and target services and reduce the impact of a DDoS attack by migrating users to new proxies and shuffling clients across proxies so as to isolate malicious clients. However, the reactive nature of these solutions presents weaknesses that we leveraged to develop a new attack - the proxy harvesting attack - which enables malicious clients to collect information about a large number of proxies before launching a DDoS attack. We show that current solutions are vulnerable to this attack, and propose a moving target defense technique consisting in periodically and proactively replacing one or more proxies and remapping clients to proxies. Our primary goal is to disrupt the attacker's reconnaissance effort. Additionally, to mitigate ongoing attacks, we propose a new client-to-proxy assignment strategy to isolate compromised clients, thereby reducing the impact of attacks. We validate our approach both theoretically and through simulation, and show that the proposed solution can effectively limit the number of proxies an attacker can discover and isolate malicious clients.

A moving target defense approach to mitigate DDoS attacks against proxy-based architectures

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.

https://dl.acm.org/doi/pdf/10.1145/2995272.2995280

A Moving Target Defense Approach to Disrupting Stealthy Botnets

Distributed denial-of-service attacks are an increasing problem facing web applications, for which many defense techniques have been proposed, including several moving-target strategies. These strategies typically work by relocating targeted services over time, increasing uncertainty for the attacker, while trying not to disrupt legitimate users or incur excessive costs. Prior work has not shown, however, whether and how a rational defender would choose a moving-target method against an adaptive attacker, and under what conditions. We formulate a denial-of-service scenario as a two-player game, and solve a restricted-strategy version of the game using the methods of empirical game-theoretic analysis. Using agent-based simulation, we evaluate the performance of strategies from prior literature under a variety of attacks and environmental conditions. We find evidence for the strategic stability of various proposed strategies, such as proactive server movement, delayed attack timing, and suspected insider blocking, along with guidelines for when each is likely to be most effective.

https://dl.acm.org/doi/pdf/10.1145/2995272.2995279

Moving Target Defense against DDoS Attacks: An Empirical Game-Theoretic Analysis

Modern botnets can persist in networked systems for extended periods of time by operating in a stealthy manner. Despite the progress made in the area of botnet prevention, detection, and mitigation, stealthy botnets continue to pose a significant risk to enterprises. Furthermore, existing enterprise-scale solutions require significant resources to operate effectively, thus they are not practical. In order to address this important problem in a resource-constrained environment, we propose a reinforcement learning based approach to optimally and dynamically deploy a limited number of defensive mechanisms, namely honeypots and network-based detectors, within the target network. The ultimate goal of the proposed approach is to reduce the lifetime of stealthy botnets by maximizing the number of bots identified and taken down through a sequential decision-making process. We provide a proof-of-concept of the proposed approach, and study its performance in a simulated environment. The results show that the proposed approach is promising in protecting against stealthy botnets.

https://dl.acm.org/doi/pdf/10.1145/3140549.3140552

Detecting Stealthy Botnets in a Resource-Constrained Environment using Reinforcement Learning

In today’s IT landscape, organizations are increasingly exposed to an array of novel and sophisticated threats—including advanced persistent threats (APTs) and distributed denial-of-service (DDoS) attacks—which can bypass traditional defenses and persist in target systems indefinitely Threat actors often rely on networks of compromised and remotely controlled hosts, known as botnets, to execute a number of different cyberattacks and engage in criminal or unauthorized activities Protecting sensitive and mission-critical data from competitors, state actors, and organized crime has become increasingly critical for the well-being of many organizations A promising approach to botnet detection and mitigation relies on moving target defense (MTD), a novel and game-changing approach to cyber defense MTD creates asymmetric uncertainty, providing the defender with a tactical advantage over the attacker MTD techniques are designed to continuously change or shift a system’s attack surface, thus increasing cost and complexity for the threat actors We show how the botnet detection and mitigation problem can be decomposed in three related and relatively simpler challenges, and how these challenges can be effectively tackled adopting an MTD approach, ultimately limiting the ability of a botnet to persist within a target system

Sridhar Venkatesan

Papers

A moving target defense approach to mitigate DDoS attacks against proxy-based architectures

A Moving Target Defense Approach to Disrupting Stealthy Botnets

Moving Target Defense against DDoS Attacks: An Empirical Game-Theoretic Analysis

Detecting Stealthy Botnets in a Resource-Constrained Environment using Reinforcement Learning

Defending from Stealthy Botnets Using Moving Target Defenses