Security Through Amnesia: A Software-Based Solution to the Cold Boot Attack on Disk Encryption

Disk encryption has become an important security measure for a multitude of clients, including governments, corporations, activists, security-conscious professionals, and privacy-conscious individuals. Unfortunately, recent research has discovered an effective side channel attack against any disk mounted by a running machine\cite{princetonattack}. This attack, known as the cold boot attack, is effective against any mounted volume using state-of-the-art disk encryption, is relatively simple to perform for an attacker with even rudimentary technical knowledge and training, and is applicable to exactly the scenario against which disk encryption is primarily supposed to defend: an adversary with physical access. To our knowledge, no effective software-based countermeasure to this attack supporting multiple encryption keys has yet been articulated in the literature. Moreover, since no proposed solution has been implemented in publicly available software, all general-purpose machines using disk encryption remain vulnerable. We present Loop-Amnesia, a kernel-based disk encryption mechanism implementing a novel technique to eliminate vulnerability to the cold boot attack. We offer theoretical justification of Loop-Amnesia's invulnerability to the attack, verify that our implementation is not vulnerable in practice, and present measurements showing our impact on I/O accesses to the encrypted disk is limited to a slowdown of approximately 2x. Loop-Amnesia is written for x86-64, but our technique is applicable to other register-based architectures. We base our work on loop-AES, a state-of-the-art open source disk encryption package for Linux.Comment: 13 pages, 4 figure

A Taxonomy for Attack Patterns on Information Flows in Component-Based Operating Systems

We present a taxonomy and an algebra for attack patterns on component-based operating systems. In a multilevel security scenario, where isolation of partitions containing data at different security classifications is the primary security goal and security breaches are mainly defined as undesired disclosure or modification of classified data, strict control of information flows is the ultimate goal. In order to prevent undesired information flows, we provide a classification of information flow types in a component-based operating system and, by this, possible patterns to attack the system. The systematic consideration of informations flows reveals a specific type of operating system covert channel, the covert physical channel, which connects two former isolated partitions by emitting physical signals into the computer's environment and receiving them at another interface.Comment: 9 page

CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.Comment: 15 pages, 16 figure

ELECTRON: An Architectural Framework for Securing the Smart Electrical Grid with Federated Detection, Dynamic Risk Assessment and Self-Healing

The electrical grid has significantly evolved over the years, thus creating a smart paradigm, which is well known as the smart electrical grid. However, this evolution creates critical cybersecurity risks due to the vulnerable nature of the industrial systems and the involvement of new technologies. Therefore, in this paper, the ELECTRON architecture is presented as an integrated platform to detect, mitigate and prevent potential cyberthreats timely. ELECTRON combines both cybersecurity and energy defence mechanisms in a collaborative way. The key aspects of ELECTRON are (a) dynamic risk assessment, (b) asset certification, (c) federated intrusion detection and correlation, (d) Software Defined Networking (SDN) mitigation, (e) proactive islanding and (f) cybersecurity training and certification