With rapid evolution of mobile core network (MCN) architectures, large-scale control-plane traffic (CPT) traces are critical to studying MCN design and performance optimization by the R&D community. The prior-art control-plane traffic generator SMM heavily relies on domain knowledge which requires re-design as the domain evolves. In this work, we study the feasibility of developing a high-fidelity MCN control plane traffic generator by leveraging generative ML models. We identify key challenges in synthesizing high-fidelity CPT including generic (to data-plane) requirements such as multimodality feature relationships and unique requirements such as stateful semantics and long-term (time-of-day) data variations. We show state-of-the-art, generative adversarial network (GAN)-based approaches shown to work well for data-plane traffic cannot meet these fidelity requirements of CPT, and develop a transformer-based model, CPT-GPT, that accurately captures complex dependencies among the samples in each traffic stream (control events by the same UE) without the need for GAN. Our evaluation of CPT-GPT on a large-scale control-plane traffic trace shows that (1) it does not rely on domain knowledge yet synthesizes control-plane traffic with comparable fidelity as SMM; (2) compared to the prior-art GAN-based approach, it reduces the fraction of streams that violate stateful semantics by two orders of magnitude, the max y-distance of sojourn time distributions of streams by 16.0%, and the transfer learning time in deriving new hourly models by 3.36×.

The cybersecurity of connected cars, integral to the broader Internet of Things (IoT) landscape, has become of paramount concern. Cyber-attacks, including hijacking and spoofing, pose significant threats to these technological advance-ments, potentially leading to unauthorized control over vehicular networks or creating deceptive identities. Given the difficulty of deploying comprehensive defensive logic across all vehicles, this paper presents a novel approach for identifying potential attacks through Radio Access Network (RAN) event monitoring. The major contribution of this paper is a location anomaly detection module that identifies aberrant devices that appear in multiple locations simultaneously - a potential indicator of a hijacking attack. We demonstrate how RAN-event based location anomaly detection is effective in combating malicious activity targeting connected cars. Using RAN data generated by tens of millions of connected cars, we developed a fast and efficient method for identifying potential malicious or rogue devices. The implications of this research are far-reaching. By increasing the security of connected cars, we can enhance the safety of users, provide robust defenses for the automotive industry, and improve overall cybersecurity practices for IoT devices.

With 5G deployment gaining momentum, the control-plane traffic volume of cellular networks is escalating. Such rapid traffic growth motivates the need to study the mobile core network (MCN) control-plane design and performance optimization. Doing so requires realistic, large control-plane traffic traces in order to profile and debug the mobile network performance under real workload. However, large-scale control-plane traffic traces are not made available to the public by mobile operators due to business and privacy concerns. As such, it is critically important to develop accurate, scalable, versatile, and open-to-innovation control traffic generators, which in turn critically rely on an accurate traffic model for the control plane. Developing an accurate model of control-plane traffic faces several challenges: (1) how to capture the dependence among the control events generated by each User Equipment (UE), (2) how to model the inter-arrival time and sojourn time of control events of individual UEs, and (3) how to capture the diversity of control-plane traffic across UEs. We present a novel two-level hierarchical state-machine-based control-plane traffic model. We further show how our model can be easily adjusted from LTE to NextG networks (e.g., 5G) to support modeling future control-plane traffic. We experimentally validate that the proposed model can generate large realistic control-plane traffic traces. We have open-sourced our traffic generator to the public to foster MCN research.

Short-lived surges in traffic can cause periods of high queue utilization, leading to packet loss and delay. To diagnose and alleviate performance problems, networks need support for real-time, fine-grained queue measurement. By identifying the flows that contribute significantly to queue build-up directly in the data plane, switches can make targeted decisions to mark, drop, or reroute these flows in real time. However, collecting fine-grained queue statistics is challenging even with modern programmable switch hardware, due to limited memory and processing resources in the data plane. We present ConQuest, a compact data structure that identifies the flows making a significant contribution to the queue. ConQuest operates entirely in the data plane, while working within the hardware constraints of programmable switches. Additionally, we show how to measure queues in legacy devices through link tapping and an off-path switch running ConQuest. Simulations show that ConQuest can identify contributing flows with 90% precision on a 1 ms timescale, using less than 65 KB of memory. Experiments with our Barefoot Tofino prototype show that ConQuest-enabled active queue management reduces flow-completion time.

Early programming languages for software-defined networking (SDN) were built on top of the simple match-action paradigm offered by OpenFlow 1.0. However, emerging hardware and software switches offer much more sophisticated support for persistent state in the data plane, without involving a central controller. Nevertheless, managing stateful, distributed systems efficiently and correctly is known to be one of the most challenging programming problems. To simplify this new SDN problem, we introduce SNAP.

This paper starts by demonstrating the vulnerability of Deep Packet Inspection (DPI) mechanisms, which are at the core of security devices, to algorithmic complexity denial of service attacks, thus exposing a weakness in the first line of defense of enterprise networks and clouds. A system and a multi-core architecture to defend from these algorithmic complexity attacks is presented in the second part of the paper. The integration of this system with two different DPI engines is demonstrated and discussed. The vulnerability is exposed by showing how a simple low bandwidth cache-miss attack takes down the Aho-Corasick (AC) pattern matching algorithm that lies at the heart of most DPI engines. As a first step in the mitigation of the attack, we have developed a compressed variant of the AC algorithm that improves the worst case performance (under an attack). Still, under normal traffic its running-time is worse than classical AC implementations. To overcome this problem, we introduce MCA2-Multi-Core Architecture to Mitigate Complexity Attacks, which dynamically combines the classical AC algorithm with our compressed implementation, to provide a robust solution to mitigate this cache-miss attack. We demonstrate the effectiveness of our architecture by examining cache-miss algorithmic complexity attacks against DPI engines and show a goodput boost of up to 73%. Finally, we show that our architecture may be generalized to provide a principal solution to a wide variety of algorithmic complexity attacks.

This paper focuses on regular expression matching over compressed traffic. The need for such matching arises from two independent trends. First, the volume and share of compressed HTTP traffic is constantly increasing. Second, due to their superior expressibility, current Deep Packet Inspection engines use regular expressions more and more frequently. We present an algorithmic framework to accelerate such matching, taking advantage of information gathered when the traffic was initially compressed. HTTP compression is typically performed through the GZIP protocol, which uses back-references to repeated strings. Our algorithm is based on calculating (for every byte) the minimum number of (previous) bytes that can be part of a future regular expression matching. When inspecting a back-reference, only these bytes should be taken into account, thus enabling one to skip repeated strings almost entirely without missing a match. We show that our generic framework works with either NFA-based or DFA-based implementations and gains performance boosts of more than 70%. Moreover, it can be readily adapted to most existing regular expression matching algorithms, which usually are based either on NFA, DFA or combinations of the two. Finally, we discuss other applications in which calculating the number of relevant bytes becomes handy, even when the traffic is not compressed.

Middleboxes play a major role in contemporary networks, as forwarding packets is often not enough to meet operator demands, and other functionalities (such as security, QoS/QoE provisioning, and load balancing) are required. Traffic is usually routed through a sequence of such middleboxes, which either reside across the network or in a single, consolidated location. Although middleboxes provide a vast range of different capabilities, there are components that are shared among many of them.

A central component in all contemporary intrusion detection systems (IDSs) is their pattern matching algorithms, which are often based on constructing and traversing a deterministic finite automaton (DFA) that represents the patterns. While this approach ensures deterministic time guarantees, modern IDSs need to deal with hundreds of patterns, thus requiring to store very large DFAs, which usually do not fit in fast memory. This results in a major bottleneck on the throughput of the IDS, as well as its power consumption and cost. We propose a novel method to compress DFAs by observing that the name used by common DFA encoding is meaningless. While regular DFAs store separately each transition between two states, we use this degree of freedom and encode states in such a way that all transitions to a specific state are represented by a single prefix that defines a set of current states. Our technique applies to a large class of automata, which can be categorized by simple properties. Then, the problem of pattern matching is reduced to the well-studied problem of Longest Prefix Match (LPM), which can be solved either in ternary content-addressable memory (TCAM), in commercially available IP-lookup chips, or in software. Specifically, we show that with a TCAM our scheme can reach a throughput of 10 Gb/s with low power consumption.

This paper takes advantage of the emerging multi-core computer architecture to design a general framework for mitigating network-based complexity attacks. In complexity attacks, an attacker carefully crafts "heavy" messages (or packets) such that each heavy message consumes substantially more resources than a normal message. Then, it sends a sufficient number of heavy messages to bring the system to a crawl at best. In our architecture, called MCA2---Multi-Core Architecture for Mitigating Complexity Attacks---cores quickly identify such suspicious messages and divert them to a fraction of the cores that are dedicated to handle all the heavy messages. This keeps the rest of the cores relatively unaffected and free to provide the legitimate traffic the same quality of service as if no attack takes place.

Deep Packet Inspection (DPI) is the most time and resource consuming procedure in contemporary security tools such as Network Intrusion Detection/Prevention System (NIDS/IPS), Web Application Firewall (WAF), or Content Filtering Proxy. DPI consists of inspecting both the packet header and payload and alerting when signatures of malicious software appear in the traffic. These signatures are identified through pattern matching algorithms. The portion of compressed traffic of overall Internet traffic is constantly increasing. This paper focuses on traffic compressed using shared dictionary. Unlike traditional compression algorithms, this compression method takes advantage of the inter-response redundancy (e.g., almost the same data is sent over and over again) as in nowadays dynamic Data. Shared Dictionary Compression over HTTP (SDCH), introduced by Google in 2008, is the first algorithm of this type. SDCH works well with other compression algorithm (as Gzip), making it even more appealing. Performing DPI on any compressed traffic is considered hard, therefore today's security tools either do not inspect compressed data, alter HTTP headers to avoid compression, or decompress the traffic before inspecting it. We present a novel pattern matching algorithm that inspects SDCH-compressed traffic without decompressing it first. Our algorithm relies on offline inspection of the shared dictionary, which is common to all compressed traffic, and marking auxiliary information on it to speed up the online DPI inspection. We show that our algorithm works near the rate of the compressed traffic, implying a speed gain of SDCH's compression ratio (which is around 40%). We also discuss how to deal with SDCH compression over Gzip compression, and show how to perform regular expression matching with about the same speed gain.

Current security tools, using “signature-based” detection, do not handle compressed traffic, whose market-share is constantly increasing. This paper focuses on compressed HTTP traffic. HTTP uses GZIP compression and requires some kind of decompression phase before performing a string matching. We present a novel algorithm, Aho-Corasick-based algorithm for Compressed HTTP (ACCH), that takes advantage of information gathered by the decompression phase in order to accelerate the commonly used Aho-Corasick pattern-matching algorithm. By analyzing real HTTP traffic and real Web application firewall signatures, we show that up to 84% of the data can be skipped in its scan. Surprisingly, we show that it is faster to perform pattern matching on the compressed data, with the penalty of decompression, than on regular traffic. As far as we know, we are the first paper that analyzes the problem of “on-the-fly” multipattern matching on compressed HTTP traffic and suggest a solution.

Compressing web traffic using standard GZIP is becoming both popular and challenging due to the huge increase in wireless web devices, where bandwidth is limited. Security and other content based networking devices are required to decompress the traffic of tens of thousands concurrent connections in order to inspect the content for different signatures. The overhead imposed by the decompression inhibits most devices from handling compressed traffic, which in turn either limits traffic compression or introduces security holes and other dysfunctionalities. The ACCH algorithm was the first to present a unified approach to pattern matching and decompression, by taking advantage of information gathered in the decompression phase to accelerate the pattern matching. ACCH accelerated the DFA-based Aho-Corasick multi-pattern matching algorithm. In this paper, we present a novel algorithm, SPC (Shift-based Pattern matching for Compressed traffic) that accelerates the commonly used Wu-Manber pattern matching algorithm. SPC is simpler and has higher throughput and lower storage overhead than ACCH. Analysis of real web traffic and real security devices signatures shows that we can skip scanning up to 87.5% of the data and gain performance boost of more than 51% as compared to ACCH. Moreover, the additional storage requirement of the technique requires only 4KB additional information per connection as compared to 8KB of ACCH.

Pattern matching algorithms lie at the core of all contemporary Intrusion Detection Systems (IDS), making it intrinsic to reduce their speed and memory requirements. This paper focuses on the most popular class of pattern-matching algorithms, the Aho-Corasick-like algorithms, which are based on constructing and traversing a Deterministic Finite Automaton (DFA), representing the patterns. While this approach ensures deterministic time guarantees, modern IDSs need to deal with hundreds of patterns, thus requiring to store very large DFAs which usually do not fit in fast memory. This results in a major bottleneck on the throughput of the IDS, as well as its power consumption and cost. We propose a novel method to compress DFAs by observing that the name of the states is meaningless. While regular DFAs store separately each transition between two states, we use this degree of freedom and encode states in such a way that all transitions to a specific state can be represented by a single prefix that defines a set of current states. Our technique applies to a large class of automata, which can be categorized by simple properties. Then, the problem of pattern matching is reduced to the well-studied problem of Longest Prefix Matching (LPM) that can be solved either in TCAM, in commercially available IP-lookup chips, or in software. Specifically, we show that with a TCAM our scheme can reach a throughput of 10 Gbps with low power consumption.

One of the fundamental technique which is used today by network security tools to detect malicious activities is 'signature based' detection. Today, the performance of the security tools is dominated by the speed of the string-matching algorithms that detect these signatures. Currently these security tools do not deal with compressed traffic, which becomes more and more common in HTTP. HTTP protocol uses the GZIP compression, which first requires some kind of decompression phase before performing the multi-patterns matching task. Thus, there is a high performance penalty in pattern matching on compressed data. In this paper we present a novel algorithm, Aho-Corasick-based algorithm for compressed HTTP (ACCH) that takes advantage of information gathered by the decompression phase in order to accelerate the commonly used Aho-Corasick pattern matching algorithm. We show by analyzing real HTTP traffic and real WAF signatures patterns, that we can skip scanning up to 75% of the data. Surprisingly, we show that in some situations, it is faster to do pattern matching on the compressed data, with the penalty of decompression, than doing pattern matching on regular traffic. As far as we know we are the first paper, that analyzes the problem of 'on-the-fly' multi-patterns matching algorithms on compressed HTTP traffic and suggest a solution.