network security engineers

Abstract: Distributed Denial-of-Service (DDoS) attacks remain a serious threat to transport networks, with recent attack volumes exceeding 30 Tbps, and the telecommunications industry being the main target. Recent work has yet to study the impact of the hosting software architecture on network monitoring solutions, or to assess recent algorithms for improving attack detection. This paper presents a Network Intrusion Detection System (NIDS) for DDoS detection in transport networks, developed in collaboration with Ericsson. Building on a statistical baseline, we improve detection effectiveness with an Isolation Forest trained on a wider set of flow features, extracted by GoFlowMeter, our open-source Go implementation of CICFlowMeter, and we integrate eBPF/XDP so that the NIDS filters real traffic at the kernel level. We further compare three deployments, monolithic, Kafka-based, and gRPC-based microservices, on a Raspberry Pi 5 testbed replaying the CIC-DDoS2019 dataset as real network traffic. Detection quality is governed mainly by the choice of detector rather than by the transport: the Isolation Forest raises recall and F1 score (0.965 live in the monolithic variant) over the baseline by flagging low-volume attack windows that the baseline misses. The transport is not neutral, however: gRPC reaches almost the same accuracy as the monolithic variant while adding less than 2 milliseconds of transport time per window, whereas the asynchronous Kafka pipeline trails by roughly nine percentage points and adds about 27 milliseconds. These findings clarify the trade-off between detection quality and architectural overhead when deploying a NIDS on resource-constrained hardware.

Abstract: Commercial security gateways repeatedly ship implementation bugs in the code path between the network and the policy decision: hand-written enforcement logic that diverges from the policy author's intent, and ad-hoc request parsers at the network boundary that introduce memory-safety flaws of their own. In both cases the bug is in the deployed enforcement code, not in the policy. Existing approaches either leave the enforcement runtime unverified or connect a formal model to a hand-written engine only by differential testing. Our contribution is a class result: a forward-only, backtrack-free EverParse validator is a verified recognizer for a bounded, finite-state class, and access-control decision functions with fixed-offset fields and bounded disjunction belong to it, so one machine-checked proof transfers to every policy in the class rather than being re-established per policy. Concretely, we encode a bounded policy language's decision function into a fixed-size byte buffer and verify the enforcement code once---covering all byte values---with an SMT solver, proving the validator accepts if and only if the decision function accepts, for every policy, request, and session. Editing rule content over a fixed endpoint set then needs no new proof; adding endpoints reruns the toolchain; extending the language needs new proofs. We establish faithful enforcement of a policy, not that a policy is itself secure. The verified gate is platform-independent, requiring only EverParse/Z3 and a C compiler, whose correctness we assume. We demonstrate a deployment on the seL4 microkernel, which ensures every request passes through the gate and that unverified components cannot corrupt the verified enforcement chain.

Abstract: Graph-based network intrusion detection systems (GIDS) report strong benchmark detection metrics, but those metrics establish little about deployability. We approach the problem from first principles: rather than inheriting the preprocessing, windowing, and thresholding conventions of each published system, we ask what a controlled comparison requires and impose it uniformly. The result is GIDS-Eval, an evaluation framework that decomposes a GIDS into six interchangeable stages and turns those conventions into explicit experimental variables, so reported performance can be attributed to individual stages instead of whole pipelines. We survey nine representative GIDS, reimplement five of them within GIDS-Eval, and evaluate them on four datasets under one matched protocol. We identify nine recurring evaluation gaps and quantify the impact of each: two crafted edges achieve full evasion against three of the eight detector-dataset pairs with anything to hide; the snapshot window alone accounts for a mean 38.3% relative swing in average precision (AP); aligning preprocessing across systems moves AP by up to 61.8 percentage points for a single detector; and none of the 18 detector-dataset pairs we replay can alert as events arrive. We introduce GIDS-Lite, an encoder-free control built in the same framework, which ranks first by AP on two of the four datasets at up to 575$\times$ lower runtime. Architectural complexity is therefore not a consistent driver of detection quality under our matched protocol on current benchmarks, but it does enlarge the runtime, calibration, and attack surfaces operators must defend.

Abstract: Cyberattacks against Operational Technology (OT) systems, which monitor and control industrial processes, pose an increasing threat to essential societal services. For this reason, developing automated intrusion response strategies is highly important. In this paper, we present a formal model of an OT intrusion response use case using the POMDP framework. It includes a realistic model of partial observability that is based on traffic measurements. This approach allows us to develop tractable, learning-based solution methods for automated intrusion response, which are based on PPO. We evaluate the obtained response strategies on an emulated OT system and find that they are effective against several types of MITRE attacks for the studied use case.

Abstract: Off-path network attacks against TCP and DNS (over UDP) client-server connections are generally considered impractical nowadays, due to built-in security features in these protocols, e.g. randomized TCP (initial) sequence numbers and randomized UDP source ports, respectively. In this work, we refute this presumption by demonstrating that an unprivileged malicious application running on the client (but practically off-path), when combined with a remote off-path adversary, can enable powerful network attacks against such connections. We show how such a local--remote collaboration between the malicious application and a remote adversary allows inference of sensitive connection state, including TCP sequence numbers and DNS stub-resolver UDP source ports. Our attacks exploit standard socket API calls such as bind(), protocol mechanisms such as IP options, and operating system features such as cBPF and procfs to infer the TCP initial sequence number (ISN) and the UDP source port in use by the connection of interest. Specifically, we take advantage of certain properties of the ISN generation algorithm as implemented in major operating systems. We demonstrate TCP connection hijacking in Linux, Android, Windows, macOS and iOS, and DNS cache poisoning against Windows, Android and the popular systemd-resolved DNS stub resolver in Linux. We evaluate our techniques across multiple operating systems and realistic deployment settings, including environments behind port-preserving NAT-integrated routers. We disclosed our techniques to Microsoft, Apple, Linux and Google, which led to the release of several patches.

Abstract: SSH is the standard protocol for secure remote administration of servers. At the transport layer, SSH uses the Binary Packet Protocol (BPP) for encrypted and authenticated communication. Above this, the SSH Connection Protocol multiplexes one or more logical channels over a single connection, supporting interactive shells, port forwarding, and related functionality. We show that SSH channel multiplexing creates a previously unrecognized compression side channel: all channels on a connection share the same compression context. When compression is enabled, an attacker can inject partially chosen plaintext into a channel and observe the length of the resulting ciphertext on the network. This enables an adaptive chosen-plaintext attack that recovers secrets from one channel by interacting with another. While related attacks such as CRIME and BREACH have been studied extensively for HTTP over TLS, this is, to our knowledge, the first compression side-channel attack on SSH and the first SSH analysis to consider a combined passive eavesdropper and web attacker threat model. We further demonstrate the attack in three different application scenarios and evaluate its effectiveness under varying levels of protocol noise. We find that, in the lowest-noise scenario, an 8-character secret over a 26-letter alphabet can be recovered using at most 276 guesses. Finally, we analyze the SSH ecosystem for compression support and other implementation characteristics that influence the practical efficacy of the attack.