Microarchitectural side channel attacks have been very prominent in security research over the last few years. Caches proved to be an outstanding side channel, as they provide high resolution and generic cross-core leakage. All major cryptographic libraries provide countermeasures to hinder key extraction via cross-core cache attacks by now. In this paper, we analyze implementations protected by prefetch-based countermeasures aimed at preventing well-known cache attacks, and highlight the circumstances causing them to remain vulnerable. Further, we craft a novel attack technique that precisely synchronizes the attacking and the victim processes, enabling the attacking process to evict the target data from the cache at the desired instants. One key improvement of our approach is that it provides unprivileged attackers with a method to remove specific data from the cache with a single memory access and in absence of shared memory by leveraging the transient capabilities of TSX and relying on the L3 replacement policy. We show the feasibility of our approach by extracting an RSA key from the latest wolfSSL library and an AES key from the T-Table and S-Box implementations included in OpenSSL with CacheSniper. Both libraries implement prefetch-based methods as a protection against cache attacks.

In this paper, we present a new fast Fourier transform (FFT) hardware architecture called constant multiplier (CM) FFT. Whereas rotators in previous architectures must rotate among several different angles, the CM FFT exploits the use of constant multipliers to calculate the rotations. The paper explores the 4-parallel and 8-parallel radix-2 CM FFT, which calculates the FFT entirely by using constant multipliers. Later, radices 2kare presented, with emphasis in radix-24and radix-25as the best alternatives. Experimental results for a 1024-point radix-25CM FFT show that the proposed approach reduces the number of block random-access memories (BRAM) and digital signal processing (DSP) slices with respect to previous approaches, while achieving the highest clock frequency for a 4-parallel FFT architecture on field-programmable gate arrays (FPGAs) reported so far.

Network Slicing is one of the fundamental capabilities of the new Fifth-generation (5G)networks. It is defined as several logical networks that are created to fulfil specific Quality of Service (QoS)and Quality of Experience (QoE)requirements and are available over the same physical infrastructure. This paper proposes a novel extension to P4-NetFPGA framework to achieve network slicing between different 5G users in the edge-to-core network segment. This solution provides hardware-isolation of the performance in terms of bandwidth, latency and packet loss of 5G network traffic. The work proposed has been validated in a real 5G infrastructure.

Microarchitectural attacks pose a great threat to any code running in parallel to other untrusted processes. Especially in public clouds, where system resources such as caches are shared across several tenants, microarchitectural attacks remain an unsolved problem. Cache attacks rely on evictions by the spy process, which alter the execution behavior of the victim process. Similarly, all attacks exploiting shared resource access will influence these resources, thereby influencing the process they are targeting. We show that hardware performance events reveal the presence of such attacks. Based on this observation, we propose CacheShield, a tool to protect legacy code by self-monitoring its execution and detecting the presence of microarchitectural attacks. CacheShield can be run by users and does not require alteration of the OS or hypervisor, while previously proposed software-based countermeasures require cooperation from the hypervisor. Unlike methods that try to detect malicious processes, our approach is lean, as only a fraction of the system needs to be monitored. It also integrates well into today's cloud infrastructure, as concerned users can opt to use CacheShield without support from the cloud service provider. Our results show that CacheShield detects attacks fast, with high reliability, and with few false positives, even in the presence of strong noise.

The evolution from the current Fourth-Generation (4G) networks to the emerging Fifth-Generation (5G) technologies implies significant changes in the architecture and poses demanding requirements on network infrastructures. One of the Key Performance Indicators (KPIs) in 5G is to ensure a secure network with zero downtime. In this paper, we focus on the provisioning of protection capabilities for 5G infrastructures. Our objective is to implement a new 5G firewall that allows the detection, differentiation and selective blocking of 5G network traffic in the edge-to-core network segment of a 5G infrastructure, using a hardware-accelerated framework based on Field Programmable Gate Arrays (FPGA), developed using the P4 language. The proposed 5G firewall has been prototyped with the new capabilities proposed empirically validated.

The most serious obstacle in further proliferation of wireless sensor networks is their low level of security, where the insider attacks are the most challenging issue. In this work we propose a holistic solution for detecting and confining insider attacks that couples reputation systems with clustering techniques, namely unsupervised genetic lgorithm and self-organizing maps, trained for detecting outliers in data. The novelty of this work is the redundancy in detecting agents, their evaluation based on the majority voting and the calculation of the reputation as the average value, which makes it more robust to different attack scenarios and their parameter variations. The algorithms use the feature space based on sequences of sensor outputs (both temporal and spatial), as well as the routing paths used to forward the data to the base station, and designed with the idea of introducing the ability to detect a wide range of attacks. The solution performs both attack detection and recovery from attacks, and it offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and high ability in detecting and confining attacks.