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Cloud computing is the cutting edge and has become inevitable in all forms of computing. This is due to its nature of elasticity, cost-effectiveness, availability, etc. The online applications like e-commerce, and e-healthcare applications are moving to the cloud to reduce their operational cost. These applications have the vulnerability of a HTTP flooding Distributed Denial of Service attack in the cloud. This flooding attack aims to overload the application, making it unable to process genuine requests and bring it down. So, these applications need to be secured and safeguarded against such attacks. This HTTP flooding attack is one of the key challenging issues as it shows normal behaviour with regard to all lower networking layers like TCP 3-way handshaking by mimicking genuine requests and it is even harder in the cloud due to the cloud properties. This article offers a solution for detecting a HTTP flooding attack in the cloud by using the novel TriZonal Linear Prediction (TLP) model. The solution was implemented using OpenStack and the FIFA Worldcup '98 data set for experimentation.

Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.

Denial of Service (DoS) attacks have evolved from volumetric attacks to target specific applications and can cripple different services with very limited effort. Hypertext Transfer Protocol (HTTP) is vulnerable to a slow rate DoS attack generated through prolonged connections which deliberately send incomplete requests to server. Simple detection methods which use x number of such connections in y time can be easily evaded. In this paper, we present SlowTrack which can detect slow rate DoS attacks against HTTP using a set of behavioral parameters. SlowTrack uses eight behavioral parameters which are validated to be useful in identifying the attack. We correlate these parameters to understand how their values change when attack is launched and subsequently use these observations to propose detection methods. SlowTrack is composed of three detection algorithms which make use of these observations for detecting attacks. We evaluate the detection performance of SlowTarck using experiments done in a testbed and also in a live network to show that these algorithms can detect the slow rate attacks effectively.

Robust 3D mesh watermarking is a traditional research topic in computer graphics, which provides an efficient solution to the copyright protection for 3D meshes. Traditionally, researchers need manually design watermarking algorithms to achieve sufficient robustness for the actual application scenarios. In this paper, we propose the first deep learning-based 3D mesh watermarking network, which can provide a more general framework for this problem. In detail, we propose an end-to-end network, consisting of a watermark embedding sub-network, a watermark extracting sub-network and attack layers. We employ the topology-agnostic graph convolutional network (GCN) as the basic convolution operation, therefore our network is not limited by registered meshes (which share a fixed topology). For the specific application scenario, we can integrate the corresponding attack layers to guarantee adaptive robustness against possible attacks. To ensure the visual quality of watermarked 3D meshes, we design the curvature consistency loss function to constrain the local geometry smoothness of watermarked meshes. Experimental results show that the proposed method can achieve more universal robustness while guaranteeing comparable visual quality.

Routing protocols transmit vast amounts of sensor data between the Wireless Sensor Network (WSN) and the Internet of Things (IoT) gateway. One of these routing protocols is Routing Protocol for Low Power and Lossy Networks (RPL). The Internet Engineering Task Force (IETF) defined RPL in March 2012 as a de facto distance-vector routing protocol for wireless communications with lower energy. Although RPL messages use a cryptographic algorithm for security protection, it does not help prevent internal attacks. These attacks drop some or all packets, such as blackhole or selective forwarding attacks, or change data packets, like grayhole attacks. The RPL protocol needs to be strengthened to address such an issue, as only a limited number of studies have been conducted on detecting internal attacks. Moreover, earlier research should have considered the mobility framework, a vital feature of the IoT. This article presents a novel lightweight system for anomaly detection of grayhole, blackhole, and selective forwarding attacks. The study aims to use a trust model in the RPL protocol, considering attack detection under mobility frameworks. The proposed system, anomaly detection of three RPL attacks (RPLAD3), is designed in four layers and starts operating immediately after the initial state of the network. The experiments demonstrated that RPLAD3 outperforms the RPL protocol when defeating attacks with high accuracy and a true positive ratio while lowering power and energy consumption. In addition, it significantly improves the packet delivery ratio and decreases the false positive ratio to zero.

Climate change has already proven its terrible effect on agriculture. Although greenhouse is already an established system for crop production, with technological advancement it is possible to apply automation in many parts of this greenhouse. Therefore, an automated smart greenhouse based on an adaptive neuro fuzzy inference system (ANFIS) and Internet of Things (IoT) could be the best solution to boost the crop production inside the house. Where, four kind of weather data such as temperature, humidity, sunlight and soil-moisture are being collected by using sensors in real time. These collected data are then feed as input variables to the fuzzy control system. The fuzzy control system manipulate the data and ANFIS then make prediction for optimum values of the weather parameters. Thus farmers can monitor all the data and can decide the best value for temperature and humidity. The end users (farmers) can visualize all the data by a simple mobile app installed on their cell phone. GSM or TCP/IP is being used for all kind of data transferring. The FIS node also utilizes same networks to transfer IoT perception layer data to application layer. To ensure the data security, four types of potential IoT perceptron layer attacks are considered and shown their probability to occur through the confusion matrix. Later, necessary steps are taken to prohibit the attacks. Here winter crops are considered in the final simulation, when the optimum temperature in winter is 24º Celsius and humidity is 76.00%. The system is 93.62% capable to detect any attack or security breach at perception layer with a Precision value of 0.83, recall of 0.78 and FI score is 0.81. In comparison to other recently proposed and available systems, this work also combines IoT technology for identifying data threat on a network transfer with fuzzy set. This approach improves learning efficiency, improves prediction accuracy, and proved to be a feasible and effective automated greenhouse maintenance system. Simultaneously, the data collecting module and presentation schema of data from various sensors, as well as the security subsystem module, achieve cloud data storage and format conversion that is compliant with protocol format data. As a result, it may provide data traceability and durability for customized indoor agriculture quality and safety. Thus this modern greenhouse maintenance system is efficient, cost effective, secure and easy to use.

The Hypertext Transfer Protocol (HTTP) is a common target of distributed denial-of-service (DDoS) attacks in today’s cloud computing environment (CCE). However, most existing datasets for Intrusion Detection System (IDS) evaluations are not suitable for CCEs. They are either self-generated or are not representative of CCEs, leading to high false alarm rates when used in real CCEs. Moreover, many datasets are inaccessible due to privacy and copyright issues. Therefore, we propose a publicly available benchmark dataset of HTTP-GET flood DDoS attacks on CCEs based on an actual private CCE. The proposed dataset has two advantages: (1) it uses CCE-based features, and (2) it meets the criteria for trustworthy and valid datasets. These advantages enable reliable IDS evaluations, tuning, and comparisons. Furthermore, the dataset includes both internal and external HTTP-GET flood DDoS attacks on CCEs. This dataset can facilitate research in the field and enhance CCE security against DDoS attacks.

The paper presents the existing possibilities of disrupting the operation of optical/optoelectronic telecommunication networks in a physical layer, distinguishing between passive and active attacks. The latter relay on jamming the operation of the optical network, ranging from the deterioration of the quality of service to the complete prevention of transmission. Passive attacks, on the other hand, are aimed at eavesdropping on transmissions. The paper discusses the various types of attacks, which are specific to the physical layer of optical networks, as well as capabilities of detection and prevention them based on the machine learning approach among others. Finally, a realistic scenario of an active attack by using of a clip-on coupler has been examined in the context of a local area optical network. The results confirm a very disruptive impact on the transmission quality if the power of the jamming signal is comparable with the power of useful signal.

The Mobile Ad Hoc Network (MANET) is formed by group of mobile nodes and such group of nodes is flexible in creating links with the other nodes in the network frequently. The routing protocols in the network layer helps in transmitting the data packets between the nodes in the network. The wireless devices use electromagnetic waves or the infrared waves as medium of transmission and each device have antennas for communication. This wireless channel is very unreliable and also unprotected from the interferences from outside. The optimized link state routing (OLSR) protocol is an optimization of pure link state routing protocol. In this paper, the focus is on the active denial of service (DoS) attacks in the network layer routing protocol OLSR. Fictitious node based detection of DoS attacks are proposed by varying the number of fictitious nodes for particular number of network nodes and the parameters throughput,delay,packet delivery ratio and average delay are evaluated using network simulator and the results are compared.The number of fictitious nodes required for the maximum throughput of the given network is finally evaluated.

Application‐layer distributed denial of service (DDoS) attacks have become the main threat to Web server security. Because application‐layer DDoS attacks have strong concealability and high authenticity, intrusion detection technologies that rely solely on judging client authenticity cannot accurately detect such attacks. In addition, application‐layer DDoS attacks are periodic and repetitive, and attack targets suddenly in a short period. In this study, we propose an efficient application‐layer DDoS detection system based on improved random forest. Firstly, the Web logs are preprocessed to extract the user session characteristics. Subsequently, we propose a Session Identification based on Separation and Aggregation (SISA) method to accurately capture user sessions. Lastly, we propose an improved random forest classification algorithm based on feature weighting to address the issue of an increasing number of features leading to prolonged calculation times in the random forest algorithm, and as the feature dimension increases, there might be instances where no subfeature is related to the category to be classified. More importantly, we compare the request source IP with the malicious IP in the threat intelligence library to deal with the periodicity and repetition of application‐layer DDoS attacks. We conducted a comprehensive experiment on the publicly available Web log dataset and the threat intelligence database of the laboratory as well as the simulated generated attack log dataset in the laboratory environment. The experimental results show that the proposed detection system can control the false alarm rate and false alarm rate within a reasonable range, improving the detection efficiency further, the detection rate is 99.85%. In secondary attack detection experiments, our proposed detection method achieves a higher detection rate in a shorter time.