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In recent years, the explosive growth of the Internet has led to an increment in the number of Distributed Denial of Service (DDoS) attacks. HTTP Flooding is a critical DDoS attack that targets HTTP servers to prohibit users from receiving HTTP services. Moreover, it saturates the link bandwidth and consumes network resources. Because the attack is launched at the application layer, it is difficult to defend against it using current countermeasures such as firewall or Intrusion Prevention System (IPS). In this paper, we propose SHFD , which leverages the Software-Defined Networking (SDN) paradigm to mitigate HTTP flooding attacks. We implement SHFD as a defender module on the SDN controller to detect and mitigate the attack in the first place. Experimental results gathered from Mininet confirm that SHFD brings a significant improvement of 13% in detection time and 29% in the number of blocked malicious flows compared to the state-of-the-art approaches. [Display omitted] • SHFD is proposed for mitigating slow and high rate HTTP flooding attacks in SDN-based networks. • Shannon's Entropy and Hellinger distance methods are used in SHFD to provide more accurate results. • Both slow/high rate HTTP flooding attack together with their side effects are explained in details. [ABSTRACT FROM AUTHOR]

Abstract In remote locations, the best way to establish a communication infrastructure is to deploy Peer-to-Peer (P2P) network over Mobile Ad-hoc Network (MANET). However, the node mobility and efficient cross-layer communication between the overlay and MANET routing protocols remains a challenge for reliable data transmission. In this paper, we propose a novel Mobility Aware Cross-layer Routing approach for Peer-to-Peer Networks (MACARON) over MANET. MACARON provides reliable communication and high lifetime routes to support efficient data transmission in the network. For this purpose, it uses cross-layer communications to share routing updates between MANET and overlay routing protocols. MACARON provides guaranteed routes with low path stretch by maintaining O ˜ (n) routing entries per node, where n is the number of nodes in the network. It provides scalability without using any landmark directory to store routing state, and it can effectively handle moderate mobility by using Last Encounter Routing (LER) protocol. [ABSTRACT FROM AUTHOR]

Recent Distributed Denial of Service (DDoS) attacks on cloud services demonstrate new attack effects, including collateral and economic losses. In this work, we show that DDoS mitigation methods may not provide the expected timely mitigation due to the heavy resource outage created by the attacks. We observe an important Operating System (OS) level “internal collateral damage”, in which the other critical services are also affected. We formulate the DDoS mitigation problem as an OS level resource management problem. We argue that providing extra resources to the victim’s server is only helpful if we can ensure the availability of other services. To achieve these goals, we propose a novel resource containment approach to enforce the victim’s resource limits. Our real-time experimental evaluations show that the proposed approach results in reduction in the attack reporting time and victim service downtime by providing isolated and timely resources to ensure availability of other critical services. [ABSTRACT FROM AUTHOR]

In this paper, we show a deep learning model for bot detection, named as DeBot, for industrial network traffic. DeBot uses a novel Cascade Forward Back Propagation Neural Network (CFBPNN) model with a subset of features using the Correlation-based Feature Selection (CFS) technique. A time series-based Nonlinear Auto-regressive Network with eXogenous inputs (NARX) technique analyzes the factors having a higher impact on the target variable and predict the behavioral pattern. To the best of our knowledge, we pioneer the use of optimal feature selection and integration with the cascading model of deep learning in bot detection of IIoTs. We conduct a thorough set of experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows. We compare CFBPNN with other existing neural network models. We observe that CFBPNN in DeBot shows 100% accuracy in all the datasets with subset evaluation and obtains optimum F1-score and zero precision. [Display omitted] • We address the multi-layered network model with IIoT features to enable bot attack detection. • Our DeBot model evaluates the subset with optimal features with high impact on attack class and predict the risk factor. • We pioneer the implementation of time series-based Nonlinear Auto-Regressive eXogenous (NARX) model to predict the risk in the specified time gap. • We introduce a new cascading model with recurrent feature to preserve the relationship between input and output variables. [ABSTRACT FROM AUTHOR]