Biblio
Advanced Metering Infrastructure (AMI) forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication within an AMI needs to be secure, key management becomes an issue due to overhead and limited resources. While using public-keys eliminate some of the overhead of key management, there is still challenges regarding certificates that store and certify the public-keys. In particular, distribution and storage of certificate revocation list (CRL) is major a challenge due to cost of distribution and storage in AMI networks which typically consist of wireless multi-hop networks. Motivated by the need of keeping the CRL distribution and storage cost effective and scalable, in this paper, we present a distributed CRL management model utilizing the idea of distributed hash trees (DHTs) from peer-to-peer (P2P) networks. The basic idea is to share the burden of storage of CRLs among all the smart meters by exploiting the meshing capability of the smart meters among each other. Thus, using DHTs not only reduces the space requirements for CRLs but also makes the CRL updates more convenient. We implemented this structure on ns-3 using IEEE 802.11s mesh standard as a model for AMI and demonstrated its superior performance with respect to traditional methods of CRL management through extensive simulations.
Due to its low cost and availability, magnetic sensors nowadays are often incorporated into security systems to detect or localize threats. This paper, with the help of a correlated pre-published work, describes preliminary steps to ensure reliable results that could help in reducing inaccuracies/ errors in case of considering a security system that detects Magnetic IEDs employing AMR-based magnetic field sensors.
Botnets are a growing threat to the security of data and services on a global level. They exploit vulnerabilities in networks and host machines to harvest sensitive information, or make use of network resources such as memory or bandwidth in cyber-crime campaigns. Bot programs by nature are largely automated and systematic, and this is often used to detect them. In this paper, we extend upon existing work in this area by proposing a network event correlation method to produce graphs of flows generated by botnets, outlining the implementation and functionality of this approach. We also show how this method can be combined with statistical flow-based analysis to provide a descriptive chain of events, and test on public datasets with an overall success rate of 94.1%.
Deep Learning has been proven more effective than conventional machine-learning algorithms in solving classification problem with high dimensionality and complex features, especially when trained with big data. In this paper, a deep learning binomial classifier for Network Intrusion Detection System is proposed and experimentally evaluated using the UNSW-NB15 dataset. Three different experiments were executed in order to determine the optimal activation function, then to select the most important features and finally to test the proposed model on unseen data. The evaluation results demonstrate that the proposed classifier outperforms other models in the literature with 98.99% accuracy and 0.56% false alarm rate on unseen data.
Physically unclonable functions (PUFs) are used to uniquely identify electronic devices. Here, we introduce a hybrid silicon CMOS-nanotube PUF circuit that uses the variations of nanotube transistors to generate a random response. An analog silicon circuit subsequently converts the nanotube response to zero or one bits. We fabricate an array of nanotube transistors to study and model their device variability. The behavior of the hybrid CMOS-nanotube PUF is then simulated. The parameters of the analog circuit are tuned to achieve the desired normalized Hamming inter-distance of 0.5. The co-design of the nanotube array and the silicon CMOS is an attractive feature for increasing the immunity of the hybrid PUF against an unauthorized duplication. The heterogeneous integration of nanotubes with silicon CMOS offers a new strategy for realizing security tokens that are strong, low-cost, and reliable.
The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action recognition and event detection. A key shortcoming of such techniques is the inability to learn from unlabeled video streams. The entire video stream is unlabeled when the requirement is to detect irregular, unforeseen and abnormal behaviors, anomalies. Recent developments in intelligent high-level video analysis have been successful in identifying individual elements in a video frame. However, the detection of anomalies in an entire video feed requires incremental and unsupervised machine learning. This paper presents a novel approach that incorporates high-level video analysis outcomes with incremental knowledge acquisition and self-learning for autonomous video surveillance. The proposed approach is capable of detecting changes that occur over time and separating irregularities from re-occurrences, without the prerequisite of a labeled dataset. We demonstrate the proposed approach using a benchmark video dataset and the results confirm its validity and usability for autonomous video surveillance.
Securing Internet of Things is a challenge because of its multiple points of vulnerability. In particular, Distributed Denial of Service (DDoS) attacks on IoT devices pose a major security challenge to be addressed. In this paper, we propose a DNS query-based DDoS attack mitigation system using Software-Defined Networking (SDN) to block the network traffic for DDoS attacks. With some features provided by SDN, we can analyze traffic patterns and filter suspicious network flows out. To show the feasibility of the proposed system, we particularly implemented a prototype with Dirichlet process mixture model to distinguish benign traffic from malicious traffic and conducted experiments with the dataset collected from real network traces. We demonstrate the effectiveness of the proposed method by both simulations and experiment data obtained from the real network traffic traces.
As an extension of cloud computing, fog computing is proving itself more and more potentially useful nowadays. Fog computing is introduced to overcome the shortcomings of cloud computing paradigm in handling the massive amount of traffic caused by the enormous number of Internet of Things devices being increasingly connected to the Internet on daily basis. Despite its advantages, fog architecture introduces new security and privacy threats that need to be studied and solved as soon as possible. In this work, we explore two privacy issues posed by the fog computing architecture and we define privacy challenges according to them. The first challenge is related to the fog's design purposes of reducing the latency and improving the bandwidth, where the existing privacy-preserving methods violate these design purposed. The other challenge is related to the proximity of fog nodes to the end-users or IoT devices. We discuss the importance of addressing these challenges by putting them in the context of real-life scenarios. Finally, we propose a privacy-preserving fog computing paradigm that solves these challenges and we assess the security and efficiency of our solution.
Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize the instability of these methods by examining the solution set of the style transfer objective. We show that the trace of the Gram matrix representing style is inversely related to the stability of the method. Then, we present a recurrent convolutional network for real-time video style transfer which incorporates a temporal consistency loss and overcomes the instability of prior methods. Our networks can be applied at any resolution, do not require optical flow at test time, and produce high quality, temporally consistent stylized videos in real-time.
Software Defined Networking (SDN) support several administrators for quicker access of resources due to its manageability, cost-effectiveness and adaptability. Even though SDN is beneficial it also exists with security based challenges due to many vulnerable threats. Participation of such threats increases their impact and risk level. In this paper a multi-level security mechanism is proposed over SDN architecture design. In each level the flow packet is analyzed using different metric and finally it reaches a secure controller for processing. Benign flow packets are differentiated from non-benign flow by means of the packet features. Initially routers verify user, secondly policies are verified by using dual-fuzzy logic design and thirdly controllers are authenticated using signature based authentication before assigning flow packets. This work aims to enhance entire security of developed SDN environment. SDN architecture is implemented in OMNeT++ simulation tool that supports OpenFlow switches and controllers. Finally experimental results show better performances in following performance metrics as throughput, time consumption and jitter.
In recent years, Moving Target Defense (MTD) has emerged as a potential game changer in the security landscape, due to its potential to create asymmetric uncertainty that favors the defender. Many different MTD techniques have then been proposed, each addressing an often very specific set of attack vectors. Despite the huge progress made in this area, there are still some critical gaps with respect to the analysis and quantification of the cost and benefits of deploying MTD techniques. In fact, common metrics to assess the performance of these techniques are still lacking and most of them tend to assess their performance in different and often incompatible ways. This paper addresses these gaps by proposing a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for the determination of the highest possible reconfiguration rate, and thus smallest probability of attacker's success, that meets performance and stability constraints. Finally, we present an experimental validation of the proposed approach.
Traffic classification, i.e. associating network traffic to the application that generated it, is an important tool for several tasks, spanning on different fields (security, management, traffic engineering, R&D). This process is challenged by applications that preserve Internet users' privacy by encrypting the communication content, and even more by anonymity tools, additionally hiding the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide (repeatable) classification results with the aim of investigating to what degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of the other considered anonymity tools, using machine learning approaches based on the sole statistical features. To this end, four classifiers are trained and tested on the dataset: (i) Naïve Bayes, (ii) Bayesian Network, (iii) C4.5, and (iv) Random Forest. Results show that the three considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.99%), telling even the specific application generating the traffic (with an accuracy of 98.00%).
PHP is one of the most popular web development tools in use today. A major concern though is the improper and insecure uses of the language by application developers, motivating the development of various static analyses that detect security vulnerabilities in PHP programs. However, many of these approaches do not handle recent, important PHP features such as object orientation, which greatly limits the use of such approaches in practice. In this paper, we present OOPIXY, a security analysis tool that extends the PHP security analyzer PIXY to support reasoning about object-oriented features in PHP applications. Our empirical evaluation shows that OOPIXY detects 88% of security vulnerabilities found in micro benchmarks. When used on real-world PHP applications, OOPIXY detects security vulnerabilities that could not be detected using state-of-the-art tools, retaining a high level of precision. We have contacted the maintainers of those applications, and two applications' development teams verified the correctness of our findings. They are currently working on fixing the bugs that lead to those vulnerabilities.
Because the Internet makes human lives easier, many devices are connected to the Internet daily. The private data of individuals and large companies, including health-related data, user bank accounts, and military and manufacturing data, are increasingly accessible via the Internet. Because almost all data is now accessible through the Internet, protecting these valuable assets has become a major concern. The goal of cyber security is to protect such assets from unauthorized use. Attackers use automated tools and manual techniques to penetrate systems by exploiting existing vulnerabilities and software bugs. To provide good enough security; attack methodologies, vulnerability concepts and defence strategies should be thoroughly investigated. The main purpose of this study is to show that the patches released for existing vulnerabilities at the operating system (OS) level and in software programs does not completely prevent cyber-attack. Instead, producing specific patches for each company and fixing software bugs by being aware of the software running on each specific system can provide a better result. This study also demonstrates that firewalls, antivirus software, Windows Defender and other prevention techniques are not sufficient to prevent attacks. Instead, this study examines different aspects of penetration testing to determine vulnerable applications and hosts using the Nmap and Metasploit frameworks. For a test case, a virtualized system is used that includes different versions of Windows and Linux OS.
The size of counterfeiting activities is increasing day by day. These activities are encountered especially in electronics market. In this paper, a countermeasure against counterfeiting on intellectual properties (IP) on Field-Programmable Gate Arrays (FPGA) is proposed. FPGA vendors provide bitstream ciphering as an IP security solution such as battery-backed or non-volatile FPGAs. However, these solutions are secure as long as they can keep decryption key away from third parties. Key storage and key transfer over unsecure channels expose risks for these solutions. In this work, physical unclonable functions (PUFs) have been used for key generation. Generating a key from a circuit in the device solves key transfer problem. Proposed system goes through different phases when it operates. Therefore, partial reconfiguration feature of FPGAs is essential for feasibility of proposed system.
Over the last years, the number of rather simple interconnected devices in nonindustrial scenarios (e.g., for home automation) has steadily increased. For ease of use, the overall system security is often neglected. Before the Internet of Things (IoT) reaches the same distribution rate and impact in industrial applications, where security is crucial for success, solutions that combine usability, scalability, and security are required. We develop such a security system, mainly targeting sensor modules equipped with Radio Frequency IDentification (RFID) tags which we leverage to increase the security level. More specifically, we consider a network based on Message Queue Telemetry Transport (MQTT) which is a widely adopted protocol for the IoT.
In this paper, we present AnomalyDetect, an approach for detecting anomalies in cloud services. A cloud service consists of a set of interacting applications/processes running on one or more interconnected virtual machines. AnomalyDetect uses the Kalman Filter as the basis for predicting the states of virtual machines running cloud services. It uses the cloud service's virtual machine historical data to forecast potential anomalies. AnomalyDetect has been integrated with the AutoMigrate framework and serves as the means for detecting anomalies to automatically trigger live migration of cloud services to preserve their availability. AutoMigrate is a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. We conducted a number of experiments to analyze the performance of the proposed AnomalyDetect approach. The experimental results highlight the feasibility of AnomalyDetect as an approach to autonomic cloud availability.
The UHF Radiofrequency Identification technology offers nowadays a viable technological solution for the implementation of low-level environmental monitoring of connected critical infrastructures to be protected from both physical threats and cyber attacks. An RFID sensor network was developed within the H2020 SCISSOR project, by addressing the design of both hardware components, that is a new family of multi-purpose wireless boards, and of control software handling the network topology. The hierarchical system is able to the detect complex, potentially dangerous, events such as the un-authorized access to a restricted area, anomalies of the electrical equipments, or the unusual variation of environmental parameters. The first real-world test-bed has been deployed inside an operational smart-grid on the Favignana Island. Currently, the network is fully working and remotely accessible.