Data Mining - Concepts and Techniques.

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Digital Coding of Waveforms

Computer communications

Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a transformative approach for providing numerous services. Compact smart devices constitute an essential part of IoT. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things into the standard Internet introduces several security challenges because the majority of Internet technologies and communication protocols were not designed to support IoT. Moreover, commercialization of IoT has led to public security concerns, including personal privacy issues, threat of cyber attacks, and organized crime. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this survey attempts to provide a comprehensive list of vulnerabilities and countermeasures against them on the edge-side layer of IoT, which consists of three levels: (i) edge nodes, (ii) communication, and (iii) edge computing. To achieve this goal, we first briefly describe three widely-known IoT reference models and define security in the context of IoT. Second, we discuss the possible applications of IoT and potential motivations of the attackers who target this new paradigm. Third, we discuss different attacks and threats. Fourth, we describe possible countermeasures against these attacks. Finally, we introduce two emerging security challenges not yet explained in detail in previous literature.

A Comprehensive Study of Security of Internet-of-Things

Worm attacks have been major security threats to the Internet. Detecting worms, especially new, unseen worms, is still a challenging problem. In this paper, we propose a new worm detection approach based on mining dynamic program executions. This approach captures dynamic program behavior to provide accurate and efficient detection against both seen and unseen worms. In particular, we execute a large number of real-world worms and benign programs (executables), and trace their system calls. We apply two classifier-learning algorithms (Naive Bayes and Support Vector Machine) to obtain classifiers from a large number of features extracted from the system call traces. The learned classifiers are further used to carry out rapid worm detection with low overhead on the end-host. Our experimental results clearly demonstrate the effectiveness of our approach to detect new worms in terms of a very high detection rate and a low false positive rate.

/pdf/detecting-worms-via-mining-dynamic-program-execution-127mqcoygx.pdf

Detecting worms via mining dynamic program execution

Active worms pose major security threats to the Internet. This is due to the ability of active worms to propagate in an automated fashion as they continuously compromise computers on the Internet. Active worms evolve during their propagation, and thus, pose great challenges to defend against them. In this paper, we investigate a new class of active worms, referred to as Camouflaging Worm (C-Worm in short). The C-Worm is different from traditional worms because of its ability to intelligently manipulate its scan traffic volume over time. Thereby, the C-Worm camouflages its propagation from existing worm detection systems based on analyzing the propagation traffic generated by worms. We analyze characteristics of the C-Worm and conduct a comprehensive comparison between its traffic and nonworm traffic (background traffic). We observe that these two types of traffic are barely distinguishable in the time domain. However, their distinction is clear in the frequency domain, due to the recurring manipulative nature of the C-Worm. Motivated by our observations, we design a novel spectrum-based scheme to detect the C-Worm. Our scheme uses the Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from background traffic. Using a comprehensive set of detection metrics and real-world traces as background traffic, we conduct extensive performance evaluations on our proposed spectrum-based detection scheme. The performance data clearly demonstrates that our scheme can effectively detect the C-Worm propagation. Furthermore, we show the generality of our spectrum-based scheme in effectively detecting not only the C-Worm, but traditional worms as well.

/pdf/modeling-and-detection-of-camouflaging-worm-57jrc7sjpz.pdf

Modeling and Detection of Camouflaging Worm

Securely authenticating a human user without assistance from any auxiliary device in the presence of powerful passive adversaries is an important and challenging problem. Passive adversaries are those that can passively monitor, intercept, and analyze every part of the authentication procedure, except for an initial secret shared between the user and the server. In this paper, we propose a new secure authentication scheme called predicate-based authentication service (PAS). In this scheme, for the first time, the concept of a predicate is introduced for authentication. We conduct analysis on the proposed scheme and implement its prototype system. Our analytical data and experimental data illustrate that the PAS scheme can simultaneously achieve a desired level of security and user friendliness.

/pdf/pas-predicate-based-authentication-services-against-powerful-gbdw7xavo8.pdf

PAS: Predicate-Based Authentication Services Against Powerful Passive Adversaries

The small form factor of the sensors, coupled with the unattended and distributed nature of their deployment expose sensors to physical attacks that physically destroy sensors in the network. In this paper, we study the modeling and analysis of search-based physical attacks in sensor networks. We define a search-based physical attack model, where the attacker walks through the sensor network using signal detecting equipment to locate active sensors, and then destroys them. We consider both flat and hierarchical sensor networks. The attacker in our model uses a weighted random selection based approach to discriminate multiple target choices (normal sensors and cluster-heads) to enhance sensor network performance degradation. Our performance metric in this paper is accumulative coverage (AC), which effectively captures coverage and lifetime of the sensor network. We then conduct detailed evaluations on the impacts of search-based physic attacks on sensor network performance. Our performance data clearly show that search-based physical attacks significantly reduce sensor network performance. We observe that attack related parameters, namely attacker movement speed, detection range and accuracy have significant impacts on the attack effectiveness. We also observe that the attack effectiveness is significantly impacted by sensor network parameters, namely the frequency of communication and frequency of cluster-head rotation. We believe that our work in this paper on modeling and analyzing search-based physical attacks is an important first step in understanding their overall impacts, and effectively defending against them in the future.

/pdf/search-based-physical-attacks-in-sensor-networks-9w52uoz12u.pdf

Search-based physical attacks in sensor networks

We address a resource constrained lifetime problem in sensor networks in an operating environment where nodes can be physically destroyed. Specifically, given a limited number of sensors and a hostile environment, our goal is to maximize the network lifetime and derive a node deployment plan. The problem of physical destruction, due to hostile environments and the small size of the sensors, is a viable threat and severely constrains the practical lifetime of sensor networks. The lifetime problem we define is representative, practical, and encompasses other versions of similar problems. We also define a representative physical attack model under which we study and solve the lifetime problem. Our solutions take into account both node constraints and the goal of energy minimization. An important observation based on this work is that network lifetime is greatly affected by the presence of physical attacks, highlighting the importance of our study. Our work has a broad and immediate impact for system designers deploying networks in hostile environments.

https://web.mst.edu/~chellaps/papers/05_icc_wgcsx.pdf

Xun Wang

Papers

Detecting worms via mining dynamic program execution

Modeling and Detection of Camouflaging Worm

PAS: Predicate-Based Authentication Services Against Powerful Passive Adversaries

Search-based physical attacks in sensor networks

Lifetime optimization of sensor networks under physical attacks