Biblio

A smart city uses information technology to integrate and manage physical, social, and business infrastructures in order to provide better services to its dwellers while ensuring efficient and optimal utilization of available resources. With the proliferation of technologies such as Internet of Things (IoT), cloud computing, and interconnected networks, smart cities can deliver innovative solutions and more direct interaction and collaboration between citizens and the local government. Despite a number of potential benefits, digital disruption poses many challenges related to information security and privacy. This paper proposes a security framework that integrates the blockchain technology with smart devices to provide a secure communication platform in a smart city.

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

In this paper, we propose a new color image encryption and compression algorithm based on the DNA complementary rule and the Chinese remainder theorem, which combines the DNA complementary rule with quantum chaotic map. We use quantum chaotic map and DNA complementary rule to shuffle the color image and obtain the shuffled image, then Chinese remainder theorem from number theory is utilized to diffuse and compress the shuffled image simultaneously. The security analysis and experiment results show that the proposed encryption algorithm has large key space and good encryption result, it also can resist against common attacks.

In recent years, more and more multimedia data are generated and transmitted in various fields. So, many encryption methods for multimedia content have been put forward to satisfy various applications. However, there are still some open issues. Each encryption method has its advantages and drawbacks. Our main goal is expected to provide a solution for multimedia encryption which satisfies the target application constraints and performs metrics of the encryption algorithm. The Advanced Encryption Standard (AES) is the most popular algorithm used in symmetric key cryptography. Furthermore, chaotic encryption is a new research direction of cryptography which is characterized by high initial-value sensitivity and good randomness. In this paper we propose a hybrid video cryptosystem which combines two encryption techniques. The proposed cryptosystem realizes the video encryption through the chaos and AES in CTR mode. Experimental results and security analysis demonstrate that this cryptosystem is highly efficient and a robust system for video encryption.

The network intrusion detection problem domain is described with mathematical knowledge in this paper, and a novel IDS detection model based on immune mechanism is designed. We study the key modules of IDS system, detector tolerance module and the algorithms of IDS detection intensively. Then, the continuous bit matching algorithm for computing affinity is improved by further analysis. At the same time, we adopt controllable variation and random variation, as well as dynamic demotion to improve the dynamic clonal selection algorithm. Finally the experimental simulations verify that the novel artificial immune algorithm has better detection rate and lower noise factor.

We propose a multi-level CSI quantization and key reconciliation scheme for physical layer security. The noisy wireless channel estimates obtained by the users first run through a transformation, prior to the quantization step. This enables the definition of guard bands around the quantization boundaries, tailored for a specific efficiency and not compromising the uniformity required at the output of the quantizer. Our construction results in an better key disagreement and initial key generation rate trade-off when compared to other level-crossing quantization methods.

The manufacturing process of electrical machines influences the geometric dimensions and material properties, e.g. the yoke thickness. These influences occur by statistical variation as manufacturing tolerances. The effect of these tolerances and their potential impact on the mechanical torque output is not fully studied up to now. This paper conducts a sensitivity analysis for geometric and material parameters. For the general approach these parameters are varied uniformly in a range of 10 %. Two dimensional finite element analysis is used to simulate the influences at three characteristic operating points. The studied object is an internal permanent magnet machine in the 100 kW range used for hybrid drive applications. The results show a significant dependency on the rotational speed. The general validity is studied by using boundary condition variations and two further machine designs. This procedure offers the comparison of matching qualitative results for small quantitative deviations. For detecting the impact of the manufacturing process realistic tolerance ranges are used. This investigation identifies the airgap and magnet remanence induction as the main parameters for potential torque fluctuation.

The development of cloud computing has brought a lot of advantages, such as reducing the hardware cost and a more convenient storage solution. Because of the convenient and cheap storage solution, a large number of users put their valuable data onto the cloud. There have been more and more outsourcing data security and privacy issues. Several schemes using attribute-based encryption (ABE) have been proposed in cloud computing outsourcing data access control; However, most of them have stubborn in complex access control policy. To implement scalable, flexible and fine-grained access control in cloud storage, this paper proposes an attribute-based solution with time restriction delegate by extending the Ciphertext-policy attribute-based encryption (CP-ABE). This scheme not only realizes the scalability and fine-grained access control, but also gives a solution for the data delegate. Our delegate mechanism can let the users entrusted the data which in their visit range to others, and the ability to set a time limit. Finally, we prove the security of our scheme based on the security of the Ciphertext-policy attribute-based encryption (CP-ABE) by Bethencourt et al. and analyze its performance and computational complexity. Experiments for our scheme are implemented and the result shows that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing.

Embedded Systems (ES) are an integral part of Cyber-Physical Systems (CPS), the Internet of Things (IoT), and consumer devices like smartphones. ES often have limited resources, and - if used in CPS and IoT - have to satisfy real time requirements. Therefore, ES rarely employ the security measures established for computer systems and networks. Due to the growth of both CPS and IoT it is important to identify ongoing attacks on ES without interfering with realtime constraints. Furthermore, security solutions that can be retrofit to legacy systems are desirable, especially when ES are used in Industrial Control Systems (ICS) that often maintain the same hardware for decades. To tackle this problem, several researchers have proposed using side-channels (i.e., physical emanations accompanying cyber processes) to detect such attacks. While prior work focuses on the anomaly detection approach, this might not always be sufficient, especially in complex ES whose behavior depends on the input data. In this paper, we determine whether one of the most common attacks - a buffer overflow attack - generates distinct side-channel signatures if executed on a vulnerable ES. We only consider the power consumption side-channel. We collect and analyze power traces from normal program operation and four cases of buffer overflow attack categories: (i) crash program execution, (ii) injection of executable code, (iii) return to existing function, and (iv) Return Oriented Programming (ROP) with gadgets. Our analysis shows that for some of these cases a power signature-based detection of a buffer overflow attack is possible.

The popularity of Android OS has dramatically increased malware apps targeting this mobile OS. The daily amount of malware has overwhelmed the detection process. This fact has motivated the need for developing malware detection and family attribution solutions with the least manual intervention. In response, we propose Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building an efficient and scalable similarity network infrastructure of malicious apps. Our detection method is based on a novel concept, namely malicious community, in which we consider, for a given family, the instances that share common features. Under this concept, we assume that multiple similar Android apps with different authors are most likely to be malicious. Cypider leverages this assumption for the detection of variants of known malware families and zero-day malware. It is important to mention that Cypider does not rely on signature-based or learning-based patterns. Alternatively, it applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and most likely malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a learning model for each malicious community. Cypider shows excellent results by detecting about 50% of the malware dataset in one detection iteration. Besides, the preliminary results of the community fingerprint are promising as we achieved 87% of the detection.

Delegating computation, which is applicable to many practical contexts such as cloud computing or pay-TV system, concerns the task where a computationally weak client wants to securely compute a very complex function f on a given input with the help of a remote computationally strong but untrusted server. The requirement is that the computation complexity of the client is much more efficient than that of f, ideally it should be in constant time or in NC0. This task has been investigated in several contexts such as instance hiding, randomized encoding, fully homomorphic encryption, garbling schemes, and verifiable scheme. In this work, we specifically consider the context where only the client has an input and gets an output, also called instance hiding. Concretely, we first give a survey of delegating computation, we then propose an efficient instance hiding scheme with passive input privacy. In our scheme, the computation complexity of the client is in NC0 and that of the server is exactly the same as the original function f. Regarding communication complexity, the client in our scheme just needs to transfer 4textbarftextbar + textbarxtextbar bits to the server, where textbarftextbar is the size of the circuit representing f and textbarxtextbar is the length of the input of f.

Several technologies, such as WiFi, Ethernet and power-line communications (PLC), can be used to build residential and enterprise networks. These technologies often co-exist; most networks use WiFi, and buildings are readily equipped with electrical wires that can offer a capacity up to 1 Gbps with PLC. Yet, current networks do not exploit this rich diversity and often operate far below the available capacity. We design, implement, and evaluate EMPoWER, a system that exploits simultaneously several potentially-interfering mediums. It operates at layer 2.5, between the MAC and IP layers, and combines routing (to find multiple concurrent routes) and congestion control (to efficiently balance traffic across the routes). To optimize resource utilization and robustness, both components exploit the heterogeneous nature of the network. They are fair and efficient, and they operate only within the local area network, without affecting remote Internet hosts. We demonstrate the performance gains of EMPoWER, by simulations and experiments on a 22-node testbed. We show that PLC/WiFi, benefiting from the diversity offered by wireless and electrical mediums, provides significant throughput gains (up to 10x) and improves coverage, compared to multi-channel WiFi.

Face recognition has attained a greater importance in bio-metric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. The second contribution is to show empirically the performance of face recognition for two age groups. Thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (≤ 15years and ≥ 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. The obtained evaluation results shows higher recognition rate for age groups ≥ 20years than ≤ 15years, which indicates the variation in face recognition across the different age groups.

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.

The Internet of Things (IoT) architecture is expected to evolve into a model containing various open systems, integrated environments, and platforms, which can be programmed and can provide secure services on demand. However, not much effort has been devoted towards the security of such an IoT architecture. In this paper, we present an IoT architecture that supports deploying dynamic security policies for IoT services. In this approach, IoT devices, gateways, and data are open and programmable to IoT application developers and service operators. Fine-grained security policies can be programmed and dynamically adjusted according to users' requirements, devices' capabilities and networking environments. The implementation and test results show that new security policies can be created and deployed rapidly and demonstrate the feasibility of the architecture.

Spoofing is a serious threat to the widespread use of Global Navigation Satellite Systems (GNSSs) such as GPS and can be expected to play an important role in the security of many future IoT systems that rely on time, location, or navigation information. In this paper, we focus on the technique of multi-receiver GPS spoofing detection, so far only proposed theoretically. This technique promises to detect malicious spoofing signals by making use of the reported positions of several GPS receivers deployed in a fixed constellation. We scrutinize the assumptions of prior work, in particular the error models, and investigate how these models and their results can be improved due to the correlation of errors at co-located receiver positions. We show that by leveraging spatial noise correlations, the false acceptance rate of the countermeasure can be improved while preserving the sensitivity to attacks. As a result, receivers can be placed significantly closer together than previously expected, which broadens the applicability of the countermeasure. Based on theoretical and practical investigations, we build the first realization of a multi-receiver countermeasure and experimentally evaluate its performance both in authentic and in spoofing scenarios.

Dynamic Searchable Symmetric Encryption (DSSE) allows a client to perform keyword searches over encrypted files via an encrypted data structure. Despite its merits, DSSE leaks search and update patterns when the client accesses the encrypted data structure. These leakages may create severe privacy problems as already shown, for example, in recent statistical attacks on DSSE. While Oblivious Random Access Memory (ORAM) can hide such access patterns, it incurs significant communication overhead and, therefore, it is not yet fully practical for cloud computing systems. Hence, there is a critical need to develop private access schemes over the encrypted data structure that can seal the leakages of DSSE while achieving practical search/update operations. In this paper, we propose a new oblivious access scheme over the encrypted data structure for searchable encryption purposes, that we call textlessutextgreaterDtextless/utextgreateristributed textlessutextgreaterOtextless/utextgreaterblivious textlessutextgreaterDtextless/utextgreaterata structure textlessutextgreaterDSSEtextless/utextgreater (DOD-DSSE). The main idea is to create a distributed encrypted incidence matrix on two non-colluding servers such that no arbitrary queries on these servers can be linked to each other. This strategy prevents not only recent statistical attacks on the encrypted data structure but also other potential threats exploiting query linkability. Our security analysis proves that DOD-DSSE ensures the unlink-ability of queries and, therefore, offers much higher security than traditional DSSE. At the same time, our performance evaluation demonstrates that DOD-DSSE is two orders of magnitude faster than ORAM-based techniques (e.g., Path ORAM), since it only incurs a small-constant number of communication overhead. That is, we deployed DOD-DSSE on geographically distributed Amazon EC2 servers, and showed that, a search/update operation on a very large dataset only takes around one second with DOD-DSSE, while it takes 3 to 13 minutes with Path ORAM-based methods.

This paper presents a supervisory control and data acquisition (SCADA) testbed recently built at the University of New Orleans. The testbed consists of models of three industrial physical processes: a gas pipeline, a power transmission and distribution system, and a wastewater treatment plant–these systems are fully-functional and implemented at small-scale. It utilizes real-world industrial equipment such as transformers, programmable logic controllers (PLC), aerators, etc., bringing it closer to modeling real-world SCADA systems. Sensors, actuators, and PLCs are deployed at each physical process system for local control and monitoring, and the PLCs are also connected to a computer running human-machine interface (HMI) software for monitoring the status of the physical processes. The testbed is a useful resource for cybersecurity research, forensic research, and education on different aspects of SCADA systems such as PLC programming, protocol analysis, and demonstration of cyber attacks.

Embedded devices with constrained computational resources, such as wireless sensor network nodes, electronic tag readers, roadside units in vehicular networks, and smart watches and wristbands, are widely used in the Internet of Things. Many of such devices are deployed in untrustable environments, and others may be easy to lose, leading to possible capture by adversaries. Accordingly, in the context of security research, these devices are running in the white-box attack context, where the adversary may have total visibility of the implementation of the built-in cryptosystem with full control over its execution. It is undoubtedly a significant challenge to deal with attacks from a powerful adversary in white-box attack contexts. Existing encryption algorithms for white-box attack contexts typically require large memory use, varying from one to dozens of megabytes, and thus are not suitable for resource-constrained devices. As a countermeasure in such circumstances, we propose an ultra-lightweight encryption scheme for protecting the confidentiality of data in white-box attack contexts. The encryption is executed with secret components specialized for resource-constrained devices against white-box attacks, and the encryption algorithm requires a relatively small amount of static data, ranging from 48 to 92 KB. The security and efficiency of the proposed scheme have been theoretically analyzed with positive results, and experimental evaluations have indicated that the scheme satisfies the resource constraints in terms of limited memory use and low computational cost.

In last twenty years, use of internet applications, web hacking activities have exaggerated speedily. Organizations facing very significant challenges in securing their web applications from rising cyber threats, as compromise with the protection issues don't seem to be reasonable. Vulnerability Assessment and Penetration Testing (VAPT) techniques help them to go looking out security loopholes. These security loopholes could also be utilized by attackers to launch attacks on technical assets. Thus it is necessary ascertain these vulnerabilities and install security patches. VAPT helps organization to determine whether their security arrangements are working properly. This paper aims to elucidate overview and various techniques used in vulnerability assessment and penetration testing (VAPT). Also focuses on making cyber security awareness and its importance at various level of an organization for adoption of required up to date security measures by the organization to stay protected from various cyber-attacks.

In this work we present a study that evaluates and compares two block ciphers, AES and PRESENT, in the context of lightweight cryptography for smartphones security applications. To the best of our knowledge, this is the first comparison between these ciphers using a smartphone as computing platform. AES is the standard for symmetric encryption and PRESENT is one of the first ultra-lightweight ciphers proposed in the literature and included in the ISO/IEC 29192-2. In our study, we consider execution time, voltage consumption and memory usage as metrics for comparison purposes. The two block ciphers were evaluated through several experiments in a low-cost smartphone using Android built in tools. From the results we conclude that, for general purpose encryption AES performs statistically better although block-to-block PRESENT delivers better results.

The second wave of change presented by the age of mobility, wearables, and IoT focuses on how organizations and enterprises, from a wide variety of commercial areas and industries, will use and leverage the new technologies available. Businesses and industries that don't change with the times will simply cease to exist. Applications need to be powered by cognitive and contextual technologies to support real-time proactive decisions. These decisions will be based on the mobile context of a specific user or group of users, incorporating location, time of day, current user task, and more. Driven by the huge amounts of data produced by mobile and wearables devices, and influenced by privacy concerns, the next wave in computing will need to exploit data and computing at the edge of the network. Future mobile apps will have to be cognitive to 'understand' user intentions based on all the available interactions and unstructured data. Mobile applications are becoming increasingly ubiquitous, going beyond what end users can easily comprehend. Essentially, for both business-to-client (B2C) and business-to-business (B2B) apps, only about 30% of the development efforts appear in the interface of the mobile app. For example, areas such as the collaborative nature of the software or the shortened development cycle and time-to-market are not apparent to end users. The other 70% of the effort invested is dedicated to integrating the applications with back-office systems and developing those aspects of the application that operate behind the scenes. An important, yet often complex, part of the solution and mobile app takes place far from the public eye-in the back-office environment. It is there that various aspects of customer relationship management must be addressed: tracking usage data, pushing out messaging as needed, distributing apps to employees within the enterprise, and handling the wide variety of operational and management tasks-often involving the collection and monitoring of data from sensors and wearable devices. All this must be carried out while addressing security concerns that range from verifying user identities, to data protection, to blocking attempted breaches of the organization, and activation of malicious code. Of course, these tasks must be augmented by a systematic approach and vigilant maintenance of user privacy. The first wave of the mobile revolution focused on development platforms, run-time platforms, deployment, activation, and management tools for multi-platform environments, including comprehensive mobile device management (MDM). To realize the full potential of this revolution, we must capitalize on information about the context within which mobile devices are used. With both employees and customers, this context could be a simple piece of information such as the user location or time of use, the hour of the day, or the day of the week. The context could also be represented by more complex data, such as the amount of time used, type of activity performed, or user preferences. Further insight could include the relationship history with the user and the user's behavior as part of that relationship, as well as a long list of variables to be considered in various scenarios. Today, with the new wave of wearables, the definition of context is being further extended to include environmental factors such as temperature, weather, or pollution, as well as personal factors such as heart rate, movement, or even clothing worn. In both B2E and B2C situations, a context-dependent approach, based on the appropriate context for each specific user, offers a superior tool for working with both employees and clients alike. This mode of operation does not start and end with the individual user. Rather, it takes into account the people surrounding the user, the events taking place nearby, appliances or equipment activated, the user's daily schedule, as well as other, more general information, such as the environment and weather. Developing enterprise-wide, context-dependent, mobile solutions is still a complex challenge. A system of real added-value services must be developed, as well as a comprehensive architecture. These four-tier architectures comprise end-user devices like wearables and smartphones, connected to systems of engagement (SoEs), and systems of record (SoRs). All this is needed to enable data analytics and collection in the context where it is created. The data collected will allow further interaction with employees or customers, analytics, and follow-up actions based on the results of that analysis. We also need to ensure end-to-end (E2E) security across these four tiers, and to keep the data and application contexts in sync. These are just some of the challenges being addressed by IBM Research. As an example, these technologies could be deployed in the retail space, especially in brick-and-mortar stores. Identifying a customer entering a store, detecting her location among the aisles, and cross-referencing that data with the customer's transaction history, could lead to special offers tailor-made for that specific customer or suggestions relevant to her purchasing process. This technology enables real-world implementation of metrics, analytics, and other tools familiar to us from the online realm. We can now measure visits to physical stores in the same way we measure web page hits: analyze time spent in the store, the areas visited by the customer, and the results of those visits. In this way, we can also identify shoppers wandering around the store and understand when they are having trouble finding the product they want to purchase. We can also gain insight into the standard traffic patterns of shoppers and how they navigate a store's floors and departments. We might even consider redesigning the store layout to take advantage of this insight to enhance sales. In healthcare, the context can refer to insight extracted from data received from sensors on the patient, from either his mobile device or wearable technology, and information about the patient's environment and location at that moment in time. This data can help determine if any assistance is required. For example, if a patient is discharged from the hospital for continued at-home care, doctors can continue to remotely monitor his condition via a system of sensors and analytic tools that interpret the sensor readings. This approach can also be applied to the area of safety. Scientists at IBM Research are developing a platform that collects and analyzes data from wearable technology to protect the safety of employees working in construction, heavy industry, manufacturing, or out in the field. This solution can serve as a real-time warning system by analyzing information gathered from wearable sensors embedded in personal protective equipment, such as smart safety helmets and protective vests, and in the workers' individual smartphones. These sensors can continuously monitor a worker's pulse rate, movements, body temperature, and hydration level, as well as environmental factors such as noise level, and other parameters. The system can provide immediate alerts to the worker about any dangers in the work environment to prevent possible injury. It can also be used to prevent accidents before they happen or detect accidents once they occur. For example, with sophisticated algorithms, we can detect if a worker falls based on a sudden difference in elevations detected by an accelerometer, and then send an alert to notify her peers and supervisor or call for help. Monitoring can also help ensure safety in areas where continuous exposure to heat or dangerous materials must be limited based on regulated time periods. Mobile technologies can also help manage events with massive numbers of participants, such as professional soccer games, music festivals, and even large-scale public demonstrations, by sending alerts concerning long and growing lines or specific high-traffic areas. These technologies can be used to detect accidents typical of large-scale gatherings, send warnings about overcrowding, and alert the event organizers. In the same way, they can alleviate parking problems or guide public transportation operators- all via analysis and predictive analytics. IBM Research - Haifa is currently involved in multiple activities as part of IBM's MobileFirst initiative. Haifa researchers have a special expertise in time- and location-based intelligent applications, including visual maps that display activity contexts and predictive analytics systems for mobile data and users. In another area, IBM researchers in Haifa are developing new cognitive services driven from the unique data available on mobile and wearable devices. Looking to the future, the IBM Research team is further advancing the integration of wearable technology, augmented reality systems, and biometric tools for mobile user identity validation. Managing contextual data and analyzing the interaction between the different kinds of data presents fascinating challenges for the development of next-generation programming. For example, we need to rethink when and where data processing and computations should occur: Is it best to leave them at the user-device level, or perhaps they should be moved to the back-office systems, servers, and/or the cloud infrastructures with which the user device is connected? New-age applications are becoming more and more distributed. They operate on a wide range of devices, such as wearable technologies, use a variety of sensors, and depend on cloud-based systems. As a result, a new distributed programming paradigm is emerging to meet the needs of these use-cases and real-time scenarios. This paradigm needs to deal with massive amounts of devices, sensors, and data in business systems, and must be able to shift computation from the cloud to the edge, based on context in close to real-time. By processing data at the edge of the network, close to where the interactions and processing are happening, we can help reduce latency and offer new opportunities for improved privacy and security. Despite all these interactions, data collection, and the analytic insights based upon them-we cannot forget the issues of privacy. Without a proper and reliable solution that offers more control over what personal data is shared and how it is used, people will refrain from sharing information. Such sharing is necessary for developing and understanding the context in which people are carrying out various actions, and to offer them tools and services to enhance their actions. In the not-so-distant future, we anticipate the appearance of ad-hoc networks for wearable technology systems that will interact with one another to further expand the scope and value of available context-dependent data.

IP tracking and cloaking are practices for identifying users which are used legitimately by websites to provide services and content tailored to particular users. However, it is believed that these practices are also used by malicious websites to avoid detection by anti-virus companies crawling the web to find malware. In addition, malicious websites are also believed to use IP tracking in order to deliver targeted malware based upon a history of previous visits by users. In this paper we empirically investigate these beliefs and collect a large dataset of suspicious URLs in order to identify at what level IP tracking takes place that is at the level of an individual address or at the level of their network provider or organisation (Network tracking). Our results illustrate that IP tracking is used in a small subset of domains within our dataset while no strong indication of network tracking was observed.

In this work, we introduce a new Markov operator associated with a digraph, which we refer to as a nonlinear Laplacian. Unlike previous Laplacians for digraphs, the nonlinear Laplacian does not rely on the stationary distribution of the random walk process and is well defined on digraphs that are not strongly connected. We show that the nonlinear Laplacian has nontrivial eigenvalues and give a Cheeger-like inequality, which relates the conductance of a digraph and the smallest non-zero eigenvalue of its nonlinear Laplacian. Finally, we apply the nonlinear Laplacian to the analysis of real-world networks and obtain encouraging results.

In this special session, members of the ACM Joint Task Force on Cyber Education to Develop Undergraduate Curricular Guidance will provide an overview of the task force mission, objectives, and work plan. After the overview, task force members will engage session participants in the curricular development process.