Over the last few years, the adoption of encryption in network traffic has been constantly increasing. The percentage of encrypted communications worldwide is estimated to exceed 90%. Although network encryption protocols mainly aim to secure and protect users' online activities and communications, they have been exploited by malicious entities that hide their presence in the network. It was estimated that in 2022, more than 85% of the malware used encrypted communication channels.

The growing adoption of network encryption protocols, like TLS, has altered the scene of monitoring network traffic. With the advent increase in network encryption, typical DPI systems that monitor network packet payload contents are becoming obsolete, while in the meantime, adversaries abuse the utilization of the TLS protocol to bypass them. In this paper, aiming to understand the botnet ecosystem in the wild, we contact IP addresses known to participate in malicious activities using the JARM tool for active probing. Based on packets acquired from TLS handshakes, server fingerprints are constructed during a time period of 7 months. We investigate if it is feasible to detect suspicious servers and re-identify other similar within blocklists with no prior knowledge of their activities. We show that it is important to update fingerprints often or follow a more effective fingerprinting approach, since the overlapping ratio with legitimate servers rises over time.

While the number of connected devices is constantly growing, we observe an increased incident rate of cyber attacks that target user data. Typically, personal devices contain the most sensitive information regarding their users, so there is no doubt that they can be a very valuable target for adversaries. Typical defense solution to safeguard user devices and data, are based in malware analysis mechanisms. To amortize the processing and maintenance overheads, the outsourcing of network inspection mechanisms to the cloud has become very popular recently. However, the majority of such cloud-based applications usually offers limited privacy preserving guarantees for data processing in third-party environments. In this work, we propose TrustAV, a practical cloud-based malware detection solution destined for a plethora of device types. TrustAV is able to offload the processing of malware analysis to a remote server, where it is executed entirely inside, hardware supported, secure enclaves. By doing so, TrustAV is capable to shield the transfer and processing of user data even in untrusted environments with tolerable performance overheads, ensuring that private user data are never exposed to malicious entities or honest-but-curious providers. TrustAV also utilizes various techniques in order to overcome performance overheads, introduced by the Intel SGX technology, and reduce the required enclave memory --a limiting factor for malware analysis executed in secure enclave environments-- offering up to 3x better performance.

Modern commodity computing systems are composed of a number of heterogeneous processing units, each one with its own unique performance and energy characteristics. However, the majority of current network packet processing frameworks targets only one device (either the CPU or an accelerator), leaving the remaining computational resources underutilized or even idle. In this paper, we propose an adaptive scheduling approach for network packet processing applications that exploits any heterogeneous architecture that can be found in a commodity high-end hardware setup. Our scheduler not only distributes the workloads to the appropriate devices in the system to achieve the desired performance results, but also enables the multiplexing of diverse, concurrently executed network packet processing applications, eliminating the interference effects introduced at run-time. The evaluation results show that our scheduler is able to tackle any interference in the shared hardware resources as well to respond quickly to dynamic fluctuations (e.g., application overloads, traffic bursts, infrastructural changes, etc.) that may occur at real time.

Heterogeneous and asymmetric computing systems are composed by a set of different processing units, each with its own unique performance and energy characteristics. Still, the majority of current network packet processing frameworks targets only a single device (the CPU or some accelerator), leaving the rest processing resources unused and idle. In this paper, we propose an adaptive scheduling approach that supports the heterogeneous and asymmetric hardware, tailored for network packet processing applications. Our scheduler is able to respond quickly to dynamic performance fluctuations that occur at real time, such as traffic bursts, application overloads, and system changes. The experimental results show that our system is able to match the peak throughput of a diverse set of packet processing applications, while consuming up to $3.5\times$ less energy.