Poisoning Network Visibility in Software-Defined Networks: New Attacks and Countermeasures

Abstract—Software-Defined Networking (SDN) is a new net-working paradigm that grants a controller and its applications an omnipotent power to have holistic network visibility and flexible network programmability, thus enabling new innovations in network protocols and applications. One of the core advantages of SDN is its logically centralized control plane to provide the entire network visibility, on which many SDN applications rely. For the first time in the literature, we propose new attack vectors unique to SDN that seriously challenge this foundation. Our new attacks are somewhat similar in spirit to spoofing attacks in legacy networks (e.g., ARP poisoning attack), however with significant differences in exploiting unique vulnerabilities how current S-DN operates differently from legacy networks. The successful attacks can effectively poison the network topology information, a fundamental building block for core SDN components and topology-aware SDN applications. With the poisoned network visibility, the upper-layer OpenFlow controller services/apps may be totally misled, leading to serious hijacking, denial of service or man-in-the-middle attacks. According to our study, all current major SDN controllers we find in the market (e.g., Floodlight, OpenDaylight, Beacon, and POX) are affected, i.e., they are subject to the Network Topology Poisoning Attacks. We then investigate the mitigation methods against the Network Topology Poisoning Attacks and present TopoGuard, a new security exten-sion to SDN controllers, which provides automatic and real-time detection of Network Topology Poisoning Attacks. Our evaluation on a prototype implementation of TopoGuard in the Floodlight controller shows that the defense solution can effectively secure network topology while introducing only a minor impact on normal operations of OpenFlow controllers. I

BOTection: bot detection by building Markov Chain models of bots network behavior

This paper was presented at the 15th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2020), 5-9 October 2020, Taipei, Taiwan. This is the accepted manuscript version of the paper. The final version is available online from the Association for Computing Machinery at: https://doi.org/10.1145/3320269.3372202.Botnets continue to be a threat to organizations, thus various machine learning-based botnet detectors have been proposed. However, the capability of such systems in detecting new or unseen botnets is crucial to ensure its robustness against the rapid evolution of botnets. Moreover, it prolongs the effectiveness of the system in detecting bots, avoiding frequent and time-consuming classifier re-training. We present BOTection, a privacy-preserving bot detection system that models the bot network flow behavior as a Markov Chain. The Markov Chain state transitions capture the bots' network behavior using high-level flow features as states, producing content-agnostic and encryption resilient behavioral features. These features are used to train a classifier to first detect flows produced by bots, and then identify their bot families. We evaluate our system on a dataset of over 7M malicious flows from 12 botnet families, showing its capability of detecting bots' network traffic with 99.78% F-measure and classifying it to a malware family with a 99.09% F-measure. Notably, due to the modeling of general bot network behavior by the Markov Chains, BOTection can detect traffic belonging to unseen bot families with an F-measure of 93.03% making it robust against malware evolution.Accepted manuscrip

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

Vetting undesirable behaviors in android apps with permission use analysis

Android platform adopts permissions to protect sensitive resources from untrusted apps. However, after permissions are granted by users at install time, apps could use these permissions (sensitive resources) with no further restrictions. Thus, recent years have witnessed the explosion of undesirable behaviors in Android apps. An important part in the defense is the accurate analysis of Android apps. However, traditional syscall-based analysis techniques are not well-suited for Android, because they could not capture critical interactions between the application and the Android system. This paper presents VetDroid, a dynamic analysis platform for reconstructing sensitive behaviors in Android apps from a novel permission use perspective. VetDroid features a systematic frame-work to effectively construct permission use behaviors, i.e., how applications use permissions to access (sensitive) system resources, and how these acquired permission-sensitive resources are further utilized by the application. With permission use behaviors, security analysts can easily examine the internal sensitive behaviors of an app. Using real-world Android malware, we show that VetDroid can clearly reconstruct fine-grained malicious behaviors to ease malware analysis. We further apply VetDroid to 1,249 top free apps in Google Play. VetDroid can assist in finding more information leaks than TaintDroid [24], a state-of-the-art technique. In addition, we show howwe can use VetDroid to analyze fine-grained causes of information leaks that TaintDroid cannot reveal. Finally, we show that VetDroid can help identify subtle vulnerabilities in some (top free) applications otherwise hard to detect

Privacy-Preserving Matching Protocols for Attributes and Strings

In this technical report we present two new privacy-preserving matching protocols for singular attributes and strings, respectively. The first one is used for matching of common attributes without revealing unmatched ones to each other. The second protocol is used to discover the longest common sub-string of two input strings in a privacy-preserving manner. Compared with previous work, our solutions are efficient and suitable to implement for many different applications, e.g., discovery of common worm signatures, computation of similarity of IP payloads

Pathogenic Role of mTORC1 and mTORC2 in Pulmonary Hypertension.

Concentric lung vascular wall thickening due to enhanced proliferation of pulmonary arterial smooth muscle cells is an important pathological cause for the elevated pulmonary vascular resistance reported in patients with pulmonary arterial hypertension. We identified a differential role of mammalian target of rapamycin (mTOR) complex 1 and complex 2, two functionally distinct mTOR complexes, in the development of pulmonary hypertension (PH). Inhibition of mTOR complex 1 attenuated the development of PH; however, inhibition of mTOR complex 2 caused spontaneous PH, potentially due to up-regulation of platelet-derived growth factor receptors in pulmonary arterial smooth muscle cells, and compromised the therapeutic effect of the mTOR inhibitors on PH. In addition, we describe a promising therapeutic strategy using combination treatment with the mTOR inhibitors and the platelet-derived growth factor receptor inhibitors on PH and right ventricular hypertrophy. The data from this study provide an important mechanism-based perspective for developing novel therapies for patients with pulmonary arterial hypertension and right heart failure

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task