Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective full and partial header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, even with full header blinding, FOG incurs a very small throughput hit of ≈5% with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

VM migration is an effective countermeasure against attempts at malicious co-residency. In this paper, our overarching objectives are: (a) to get an in-depth understanding of the ways and effectiveness with which an attacker can launch attacks toward achieving co-residency and (b) to design migration policies that are very effective in thwarting malicious co-residency, but are thrifty in terms of the bandwidth and downtime costs that are incurred with live migration. Toward achieving our goals, we first undertake an experimental study on Amazon EC2 to obtain an in-depth understanding of the side-channels, through which an attacker can use to ascertain co-residency with a victim. Here, in this paper, we identify a new set of stealthy side-channel attacks which we show to be more effective than the currently available attacks toward verifying co-residency. We also build a simple model that can be used for estimating co-residency times based on very few measurements on a given cloud platform, to account for varying attacker capabilities. Based on the study, we develop a set of guidelines to determine under what conditions the victim VM migrations should be triggered, given the performance costs in terms of bandwidth and downtime, which a user is willing to bear. Through extensive experiments on our private in-house cloud, we show that the migrations, using our guidelines, can limit the fraction of the time that an attacker VM co-resides with a victim VM to about 1% of the time with the bandwidth costs of a few MB and downtimes of a few seconds per day per VM migrated.

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, FOG incurs a very small throughput hit of ≈5 % with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

We have recently seen an increasing number of attacks that are distributed, and span an entire wide area network (WAN). Today, typically, intrusion detection systems (IDSs) are deployed at enterprise scale and cannot handle attacks that cover a WAN. Moreover, such IDSs are implemented at a single entity that expects to look at all packets to determine an intrusion. Transferring copies of raw packets to centralized engines for analysis in a WAN can significantly impact both network performance and detection accuracy. In this paper, we propose Jaal, a framework for achieving accurate network intrusion detection at scale. The key idea in Jaal is to monitor traffic and construct in-network packet summaries. The summaries are then processed centrally to detect attacks with high accuracy. The main challenges that we address are (a) creating summaries that are concise, but sufficient to draw highly accurate inferences and (b) transforming traditional IDS rules to handle summaries instead of raw packets. We implement Jaal on a large scale SDN testbed. We show that on average Jaal yields a detection accuracy of about 98%, which is the highest reported for ISP scale network intrusion detection. At the same time, the overhead associated with transferring summaries to the central inference engine is only about 35% of what is consumed if raw packets are transferred.

Attacker VMs try to co-reside with victim VMs on the same physical infrastructure as a precursor to launching attacks that target information leakage. VM migration is an effective countermeasure against attempts at malicious co-residency. In this paper, we first undertake an experimental study on Amazon EC2 to obtain an in-depth understanding of the side-channels an attacker can use to ascertain co-residency with a victim. Here, we identify a new set of stealthy side-channel attacks which, we show to be more effective than currently available attacks towards verifying co-residency. Based on the study, we develop a set of guidelines to determine under what conditions victim VM migrations should be triggered given performance costs in terms of bandwidth and downtime, that a user is willing to bear. Via extensive experiments on our private in-house cloud, we show that migrations using our guidelines can limit the fraction of the time that an attacker VM co-resides with a victim VM to about 1 % of the time with bandwidth costs of a few MB and downtimes of a few seconds, per day per VM migrated.

Network coding has been shown to offer significant throughput benefits over certain wireless network topologies. However, the application of network coding may not always improve the network performance. In this paper, we first provide an analytical study, which helps in assessing when network coding is preferable to a traditional store-and-forward approach. Interestingly, our study reveals that in many topological scenarios, network coding can in fact hurt the throughput performance; in such scenarios, applying the store-and-forward approach leads to higher network throughput. We validate our analytical findings via extensive testbed experiments. Guided by our findings as our primary contribution, we design and implement PACE, a Policy-Aware Coding Enforcement logic that enables network coding only when it is expected to offer performance benefits. Specifically, PACE leverages a minimal set of periodic link quality measurements in order to make per-flow online decisions with regards to when network coding should be activated, and when store-and-forward is preferable. It can be easily embedded into network-coding-aware routers as a user-level or kernel-level software utility. We evaluate the efficacy of PACE via: 1) ns-3 simulations, and 2) experiments on a wireless testbed. We observe that our scheme wisely activates network coding only when appropriate, thereby improving the total network throughput by as much as 350% in some scenarios.

Power line communications (PLC) offer an immediate means of providing high bandwidth connectivity in settings where there is no in-built network infrastructure. While there is recent work on understanding physical and MAC layer artifacts of PLC, its applicability and performance in multi-flow settings is not well understood. We first undertake an extensive measurement study that sheds light on the properties of PLC that significantly affect performance in multi-flow settings. Using the understanding gained, we design BOLT, a framework that adopts a learning-based approach to effectively manage and orchestrate flows in a PLC network. BOLT is flexible and is agnostic to standards; it can be used to implement scheduling algorithms that target different performance goals. We implement BOLT on three different testbeds using off-the-shelf PLC adapters and showcase its ability to effectively manage flows, delivering several folds throughput improvement over state-of-the-art solutions.

Streaming video content over cellular connectivity impacts the battery consumption of a client (e.g., a smartphone). The problem is exacerbated when the channel quality is poor because of a large number of retransmissions, moreover, streaming high quality video in such cases can negatively impact user experience (e.g., due to stalling). In this paper, we develop an analytical framework which can provide the user with an estimate of "how much" energy she can save by choosing to view a lower quality stream of the video she wishes to view. The framework takes as input the network conditions (in terms of packet error rate or PER) and a coarse characterization of the video to be viewed (slow versus fast motion, resolution), and yields as output the energy savings with different resolutions of the video to be viewed. Thus empowered, the user can then make a quick, educated decision on the version of the video to view. We validate that our framework is extremely accurate in estimating the energy consumption via both simulations, and experiments on smartphones (within ~ 5% of real measurements). We find that switching to a lower resolution video can potentially lead to ~ 418 mW (23.2%) decrease in the consumed power for slow motion video, and ~ 480 mW (26%) for fast motion video in bad channel conditions. This translates to an energy savings of 376.2 J and 432 J respectively, for video clips that are 15 minutes long.

Malicious interference injection or jamming is one of the simplest ways to disrupt wireless communications. Prior approaches can alleviate jamming interference to a limited extent; they are especially vulnerable to a reactive jammer i.e., a jammer that injects noise upon sensing a legitimate transmission or wideband jamming. In this paper, we leverage the inherent features of OFDM (Orthogonal Frequency Division Multiplexing) to cope with such attacks. Specifically, via extensive experiments, we observe that the jamming signal experiences differing levels of fading across the composite sub-carriers in its transmission bandwidth. Thus, if the legitimate transmitter were to somehow exploit the relatively unaffected sub-carriers to transmit data to the receiver, it could achieve reasonable throughputs, even in the presence of the active jammer. We design and implement JIMS, a Jamming Interference Mitigation Scheme that exploits the above characteristic by overcoming key practical challenges. Via extensive testbed experiments and simulations we show that JIMS achieves a throughput restoration of up to 75 percent in the presence of an active jammer.

Visible light communications (VLC) have gained popularity as an alternative to RF. Understanding the underlying communications is key to the design of MAC protocols for arbitrating access across lights in multiple rooms. We consider the interference across two rooms from VLC emitters. The emitters use Binary Pulse Position Modulation (BPPM); the pulse width is varied to provide different dimming levels. We use a modified ray-tracing algorithm to calculate the channel impulse response between the emitters and receivers that are located at different positions within a room. We analyze the performance observed at the receivers in the presence of (i) illumination and (ii) transmissions from an interfering VLC emitter. We find that in the former case, the VLC emissions from the interferer do not impact the reception at the target receiver. In the latter case, the performance is degraded and it depends on the position of the receiver.

Malicious interference injection or jamming is one of the simplest ways to disrupt wireless communications. Prior approaches can alleviate jamming interference to a limited extent; they are especially vulnerable to a reactive jammer i.e., a jammer that injects noise upon sensing a legitimate transmission or wideband jamming. In this paper, we leverage the inherent features of OFDM (Orthogonal Frequency Division Multiplexing) to cope with such attacks. Specifically, via extensive experiments, we observe that the jamming signal experiences differing levels of fading across the composite sub-carriers in its transmission bandwidth. Thus, if the legitimate transmitter were to somehow exploit the relatively unaffected sub-carriers to transmit data to the receiver, it could achieve reasonable throughputs, even in the presence of the active jammer. We design and implement JIMS, a Jamming Interference Mitigation Scheme that exploits the above characteristic by overcoming key practical challenges. Via extensive testbed experiments and simulations we show that JIMS achieves a throughput restoration of up to 75% in the presence of an active jammer.

Network coding has been shown to offer significant throughput benefits over store-and-forward routing in certain wireless network topologies. However, the application of network coding may not always improve the network performance. In this paper1, we provide a comprehensive analytical study, which helps in assessing when network coding is preferable to a traditional store-and-forward approach. Interestingly, our study reveals that in many topological scenarios, network coding can in fact hurt the throughput performance; in such scenarios, applying the store-and-forward approach leads to higher network throughput. We validate our analytical findings via extensive testbed experiments, and we extract guidelines on when network coding should be applied instead of store-and-forward.

In this paper, we aim at protecting the privacy of the communicating parties while ensuring the authenticity of source nodes. In particular, we exploit intra-flow network coding to preserve the anonymity of communicating parties. Towards this objective, we propose an anonymity preservation scheme, namely closed group anonymity (CGA) that preserves the anonymity of the communicating parties via mixing their flows. Afterwards, we explore an instance of the Authentication-Privacy Trade-off in the context of Network Coding. We analyze the trade-off with the aid of the proposed anonymity scheme and a previously proposed Source Authentication Scheme using Network Coding (SANC). We present simulation results showing that the proposed algorithm successfully leverages network coding to preserve anonymity against traffic analysis attacks. Finally, we not only confirm the fundamental authentication-privacy trade-off in the context of intra-flow network coding but also parameterize it via introducing a tunable parameter to dynamically control, and potentially balance, this trade-off depending on the security provisions dictated by the operational scenario and application of interest.