Computer Science

Exploring the symbiotic nature of biological systems can result in valuable knowledge for computer networks. Biolog ically inspired approaches to security in networks are interesting to evaluate because of the analogies between network security and survival of human body under pathogenic attacks. Wireless Sensor Network (WSN) is a network based on multiple low cost, low-energy sensor nodes connected to physical signals. The network is made up of sensor nodes and gateways, where the server nodes acquire physical world data, while the gateway forwards the data to the end-user. While the spread of viruses in wired systems has been studied in-depth, applying trust in wireless sensor network nodes is an emerging area. This paper uses machine learning techniques to first dif1"erentiate between fraudulent and good nodes in the system. Next, it derives inspiration from the human immune system to present an idea of virtual antibodies in the system, to disable the fraudulent nodes in the system.

Biological Immune Systems have intelligent capabilities of detecting foreign bodies which attack our body. Moreover they have inherent insightful capabilities to remember them, when they hit the body again. Primary response is the initial response instantiated by the body to the attack and secondary response is the response hence forth. Secondary response is naturally faster because of its characteristic of remembering the cure of the attack. Similar type of perspicacious nature can be adapted in removal of fraudulent nodes in wireless sensor network. The work proposes a novel algorithm for the detection and removal of the fraudulent nodes. It first detects the fraudulent nodes by machine learning module and then removes these nodes by immune-inspired module. Eventually if the same type of malicious nature is seen again, analogy of secondary response of immune system is instigated in sensor network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy and robustness of the proposed model.

Exploring the symbiotic nature of biological systems can result in valuable knowledge for computer networks. Biologically inspired approaches to security in networks are interesting to evaluate because of the analogies between network security and survival of human body under pathogenic attacks. Wireless Sensor Network (WSN) is a network based on multiple low-cost communication and computing devices connected to sensor nodes which sense physical parameters. While the spread of viruses in wired systems has been studied in-depth, applying trust in WSN is an emerging research area. Security threats can be introduced in WSN through various means, such as a benevolent sensor node turning fraudulent after a certain period of time. The proposed research work uses biological inspirations and machine learning techniques for adding security against such threats. While it uses machine learning techniques to identify the fraudulent nodes, consecutively by deriving inspiration from human immune system it effectively nullify the impact of the fraudulent ones on the network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy, robustness of the proposed model.

Communicable disease models have been studied using classical mathematical differential equations for a long time now. However, these classical models do not always accurately represent the complex network behavior that governs the spread of any type of information, including communicable diseases. The paper presents a new modular network representation and shows how diseases generally spread in such a network. This paper also shows different simulation results, while looking at various observation parameters such as infection duration and number of modules. This paper then analyzes the 2009 H1N1 data for the Indian metropolitan cities of Delhi and Mumbai and states of Karnataka and TamilNadu in India and then discusses the correlation between the real-world data and the modular network results obtained through our simulations.

Social networks are susceptible to rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This paper describes a method for identifying highly connected nodes in a social network and using these nodes to build immunity against such malicious information. To describe this method, this paper draws inspiration from two well established topics in the area of biology; spread of communicable diseases in human population and how human body builds immunity against diseases. In case of communicable diseases, it would be very simplistic if we only consider that an infected node can transmit its disease to its nearest neighbors. More realistically speaking, it is possible that an infected node can develop random links with other nodes in the system. The spread of communicable diseases is controlled by both these factors. An infected node with capability to have several random links is capable of spreading the disease through the network faster. We postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. We present analytical tools based on our network simulation, to correctly identify such nodes. Once such nodes are identified, we introduce the concept of weighting functions that can be attached to messages passing through such nodes. This paper describes how the spread of malicious information can be controlled by a community of such highly connected nodes, using the concept of weighted functions.

Admission control is an important component for end-to-end Quality of Service (QoS) delivery in IP networks using resource reservation and determines how bandwidth and latency are allocated to streams with various requirements. Admission control schemes therefore need to be implemented between network edges and core to control the traffic entering the network. This paper compares the performance of four measurement based admission control Algorithms for controlled-load service. The measurement based algorithms are based on measured bandwidth, acceptance region, and equivalent bandwidth [1].

The increase of IEEE 802.11's bandwidth led to a deployment of many multimedia applications over wireless networks. Nevertheless, these applications impose rigorous discipline constraints in QoS. To support the varying Quality-of-Service (QoS) requirements of emerging applications, the QoS-capable standard IEEE 802.11e has been specified. Digital video delivered over wired-to-wireless networks is expected to suffer quality degradation from packet loss. Video traffic is widely expected to account for a large portion of the traffic in future hybrid network, as multimedia applications are becoming increasingly popular. Initially this paper attempted to discover optimal parameters such as bandwidth utilization and packet size in order to enhance video stream quality and proposed New Adaptive Crosslayer Algorithm, a novel scheme for QoS enhancement for IEEE 802.11 wireless networks.

Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation , relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.

Distracted pedestrians, akin to their distracted driver counterparts, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing effective pedestrian safety frameworks is possible with the availability of sophisticated mobile and wearable devices that are equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge in the design of such systems is accurate recognition of distractions with minimal resources in real-time, given the memory, computation and communication limitations of these devices. Several recently published works detect pedestrian activities by leveraging on complex activity recognition frameworks using mobile and wearable sensor data. The primary focus of these efforts, however, was on achieving high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient, and real-time pedestrian distraction detection technique that overcomes some of the shortcomings (of existing techniques). We demonstrate the practicality of the proposed technique by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from human subject participants in realistic pedestrian experiments. By means of these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption compared to some other techniques in the literature.

Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for the infusion of insulin to control blood glucose level. While these devices offer many clinical benefits, there has been a recent increase in the number of cases, wherein, the wireless communication channel of such devices has been compromised. This not only causes the device to malfunction but also potentially threatens the patient's life. In this paper, a neural networks-based multi-layer perceptron model was designed for real-time medical device security. Machine learning algorithms are among the most effective and broadly utilized systems for classification, identification, and segmentation. Although they are effective, they are both computationally and memory intensive, making them hard to be deployed on low-power embedded frameworks. In this paper, we present an on-chip neural system network for securing diabetic treatment. The model achieved 98.1% accuracy in classifying fake versus genuine glucose measurements. The proposed model was comparatively evaluated with a linear support vector machine which achieved only 90.17% accuracy with negligible precision and recall. Moreover, the proposal estimates the reliability of the framework through the use of the Bayesian network. The proposed approach enhances the reliability of the overall framework by 18% when only one device is secured, and over 90% when all devices are secured. INDEX TERMS Security, machine learning, insulin pumps, deep learning, implantable medical devices.

Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. It has...

Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer an...

We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. In mmMoReEdge, complex signal processing, typically required for 5G testing, is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. We present simulation results that show that mmMoReEdge is 50% to 70% faster as compared to an offline CPU-based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices.