IEEE/ACM Transactions on Networking\ud 29 November 2016\ud LineSwitch: Tackling Control Plane Saturation Attacks in Software-Defined Networking ( Articles not published yet, but available online Article in press About articles in press (opens in a new window) )\ud Ambrosin, M., \ud Conti, M., \ud De Gaspari, F., \ud Poovendran, R.\ud Abstract\ud \ud Software defined networking (SDN) is a new networking paradigm that in recent years has revolutionized network architectures. At its core, SDN separates the data plane, which provides data forwarding functionalities, and the control plane, which implements the network control logic. The separation of these two components provides a virtually centralized point of control in the network, and at the same time abstracts the complexity of the underlying physical infrastructure. Unfortunately, while promising, the SDN approach also introduces new attacks and vulnerabilities. Indeed, previous research shows that, under certain traffic conditions, the required communication between the control and data plane can result in a bottleneck. An attacker can exploit this limitation to mount a new, network-wide, type of denial of service attack, known as the control plane saturation attack. This paper presents LineSwitch, an efficient and effective data plane solution to tackle the control plane saturation attack. LineSwitch employs probabilistic proxying and blacklisting of network traffic to prevent the attack from reaching the control plane, and thus preserve network functionality. We implemented LineSwitch as an extension of the reference SDN implementation, OpenFlow, and run a thorough set of experiments under different traffic and attack scenarios. We compared LineSwitch to the state of the art, and we show that it provides at the same time, the same level of protection against the control plane saturation attack, and a reduced time overhead by up to 30%

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Recent progress in machine learning has led to promising results in behavioral malware detection. Behavioral modeling identifies malicious processes via features derived by their runtime behavior. Behavioral features hold great promise as they are intrinsically related to the functioning of each malware, and are therefore considered difficult to evade. Indeed, while a significant amount of results exists on evasion of static malware features, evasion of dynamic features has seen limited work. This paper examines the robustness of behavioral ransomware detectors to evasion and proposes multiple novel techniques to evade them. Ransomware behavior differs significantly from that of benign processes, making it an ideal best case for behavioral detectors, and a difficult candidate for evasion. We identify and propose a set of novel attacks that distribute the overall malware workload across a small set of independent, cooperating processes in order to avoid the generation of significant behavioral features. Our most effective attack decreases the accuracy of a state-of-the-art classifier from 98.6 to 0% using only 18 cooperating processes. Furthermore, we show our attacks to be effective against commercial ransomware detectors in a black-box setting. Finally, we evaluate a detector designed to identify our most effective attack, as well as discuss potential directions to mitigate our most advanced attack.

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Several cybersecurity domains, such as ransomware detection, forensics and data analysis, require methods to reliably identify encrypted data fragments. Typically, current approaches employ statistics derived from byte-level distribution, such as entropy estimation, to identify encrypted fragments. However, modern content types use compression techniques which alter data distribution pushing it closer to the uniform distribution. The result is that current approaches exhibit unreliable encryption detection performance when compressed data appear in the dataset. Furthermore, proposed approaches are typically evaluated over few data types and fragment sizes, making it hard to assess their practical applicability. This paper compares existing statistical tests on a large, standardized dataset and shows that current approaches consistently fail to distinguish encrypted and compressed data on both small and large fragment sizes. We address these shortcomings and design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data. We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB. Our results highlight that EnCoD outperforms current approaches by a wide margin, with accuracy ranging from $$\sim 82\%$$ ∼ 82 % for 512B fragments up to $$\sim 92\%$$ ∼ 92 % for 8KB data fragments. Moreover, EnCoD can pinpoint the exact format of a given data fragment, rather than performing only binary classification like previous approaches.

Reliable identification of encrypted file fragments is a requirement for several security applications, including ransomware detection, digital forensics, and traffic analysis. A popular approach consists of estimating high entropy as a proxy for randomness. However, many modern content types (e.g. office documents, media files, etc.) are highly compressed for storage and transmission efficiency. Compression algorithms also output high-entropy data, thus reducing the accuracy of entropy-based encryption detectors.Over the years, a variety of approaches have been proposed to distinguish encrypted file fragments from high-entropy compressed fragments. However, these approaches are typically only evaluated over a few, selected data types and fragment sizes, which makes a fair assessment of their practical applicability impossible. This paper aims to close this gap by comparing existing statistical tests on a large, standardized dataset. Our results show that current approaches cannot reliably tell apart encryption and compression, even for large fragment sizes. To address this issue, we design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data, starting with fragments as small as 512 bytes. We evaluate EnCoD against current approaches over a large dataset of different data types, showing that it outperforms current state-of-the-art for most considered fragment sizes and data types.

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Testing Vehicular Network protocols can be quite problematic. In fact, vehicular experiments are often expensive in both economic and temporal terms. As a consequence, researchers generally resort to software simulations which are a neccessary first step but still not sufficient. In this paper, we present two possible experimentation frameworks that could give a boost to the testing of protocols for vehicular networks: the first framework is based on Android smartphones, while the second is based on commodity laptop computers. In order to assess the feasibility and the performance of the proposed frameworks, we implemented through them a representative vehicle-to-vehicle multi-hop broadcast algorithm, i.e., the Fast Broadcast algorithm.