Biblio

The idea to use multiple paths to transport TCP traffic seems very attractive due to its potential benefits it may offer for both redundancy and better utilization of available resources by load balancing. Fixed and mobile network providers employ frequently load-balancers that use multiple paths on either per-flow or per-destination level, but very seldom on per-packet level. Despite of the benefits of packet-level load balancing mechanisms (e.g., low computational complexity and high bandwidth utilization) network providers can't use them mainly because of TCP packet reorderings that harm TCP performance. Emerging network architectures also support multiple paths, but they face with the same obstacle in balancing their load to multiple paths. Indeed, packet level load balancing research is paralyzed by the reordering vulnerability of TCP.A couple of TCP variants exist that deal with TCP packet reordering problem, but due to lack of end-to-end transparency they were not widely deployed and adopted. In this paper, we revisit TCP's packet reorderings problem and present a transparent and light-weight algorithm, Out-of-Order Robustness for TCP with Transparent Acknowledgment (ACK) Intervention (ORTA), to deal with out-of-order deliveries.ORTA works as a transparent thin layer below TCP and hides harmful side-effects of packet-level load balancing. ORTA monitors all TCP flow packets and uses ACK traffic shaping, without any modifications to either TCP sender or receiver sides. Since it is transparent to TCP end-points, it can be easily deployed on TCP sender end-hosts (EHs), gateway (GW) routers, or access points (APs). ORTA opens a door for network providers to use per-packet load balancing.The proposed ORTA algorithm is implemented and tested in NS-2. The results show that ORTA can prevent TCP performance decrease when per-packet load balancing is used.

Manufacturers often buy and/or license communication ICs from third-party suppliers. These communication ICs are then integrated into a complex computational system, resulting in a wide range of potential hardware-software security issues. This work proposes a compact supervisory circuit to classify the Bluetooth profile operation of a Bluetooth System-on-Chip (SoC) at low frequencies by monitoring the radio frequency (RF) output power of the Bluetooth SoC. The idea is to inexpensively manufacture an RF envelope detector to monitor the RF output power and a profile classification algorithm on a custom low-frequency integrated circuit in a low-cost legacy technology. When the supervisory circuit observes unexpected behavior, it can shut off power to the Bluetooth SoC. In this preliminary work, we proto-type the supervisory circuit using off-the-shelf components to collect a sufficient data set to train 11 different Machine Learning models. We extract smart descriptive time-domain features from the envelope of the RF output signal. Then, we train the machine learning models to classify three different Bluetooth operation profiles: sensor, hands-free, and headset. Our results demonstrate 100% classification accuracy with low computational complexity.