The Mobile Generation: Global Transformations at the Cellular Level

Every year we see a new dimension of the ongoing Digital Revolution, which is enabling an abundance of information to move faster, cheaper, in more intelligible forms, in more directions, and across borders of every kind. The exciting new dimension on which the Aspen Institute focused its 2006 Roundtable on Information Technology was mobility, which is making the Digital Revolution ubiquitous. As of this writing, there are over two billion wireless subscribers worldwide and that number is growing rapidly. People are constantly innovating in the use of mobile technologies to allow them to be more interconnected. Almost a half century ago, Ralph Lee Smith conjured up "The Wired Nation," foretelling a world of interactive communication to and from the home that seems commonplace in developed countries today. Now we have a "Wireless World" of communications potentially connecting two billion people to each other with interactive personal communications devices. Widespead adoption of wireless handsets, the increasing use of wireless internet, and the new, on-the-go content that characterizes the new generation of users are changing behaviors in social, political and economic spheres. The devices are easy to use, pervasive and personal. The affordable cell phone has the potential to break down the barriers of poverty and accessibility previously posed by other communications devices. An entire generation that is dependant on ubiquitous mobile technologies is changing the way it works, plays and thinks. Businesses, governments, educational institutions, religious and other organizations in turn are adapting to reach out to this mobile generation via wireless technologies -- from SMS-enabled vending machines in Finland to tech-savvy priests in India willing to conduct prayers transmitted via cell phones. Cellular devices are providing developing economies with opportunities unlike any others previously available. By opening the lines of communication, previously disenfranchised groups can have access to information relating to markets, economic opportunities, jobs, and weather to name just a few. When poor village farmers from Bangladesh can auction their crops on a craigslist-type service over the mobile phone, or government officials gain instantaneous information on contagious diseases via text message, the miracles of mobile connectivity move us from luxury to necessity. And we are only in the early stages of what the mobile electronic communications will mean for mankind. We are now "The Mobile Generation." Aspen Institute Roundtable on Information Technology. To explore the implications of these phenomena, the Aspen Institute Communications and Society Program convened 27 leaders from business, academia, government and the non-profit sector to engage in three days of dialogue on related topics. Some are experts in information and communications technologies, others are leaders in the broader society affected by these innovations. Together, they examined the profound changes ahead as a result of the convergence of wireless technologies and the Internet. In the following report of the Roundtable meeting held August 1-4, 2006, J. D. Lasica, author of Darknet and co-founder of Ourmedia.org, deftly sets up, contextualizes, and captures the dialogue on the impact of the new mobility on economic models for businesses and governments, social services, economic development, and personal identity

Trajectory-Based Takeoff Time Predictions Applied to Tactical Departure Scheduling: Concept Description, System Design, and Initial Observations

Current aircraft departure release times are based on manual estimates of aircraft takeoff times. Uncertainty in takeoff time estimates may result in missed opportunities to merge into constrained en route streams and lead to lost throughput. However, technology exists to improve takeoff time estimates by using the aircraft surface trajectory predictions that enable air traffic control tower (ATCT) decision support tools. NASA s Precision Departure Release Capability (PDRC) is designed to use automated surface trajectory-based takeoff time estimates to improve en route tactical departure scheduling. This is accomplished by integrating an ATCT decision support tool with an en route tactical departure scheduling decision support tool. The PDRC concept and prototype software have been developed, and an initial test was completed at air traffic control facilities in Dallas/Fort Worth. This paper describes the PDRC operational concept, system design, and initial observations

Analysis and Mitigation of Recent Attacks on Mobile Communication Backend

2014 aasta viimases kvartalis demonstreeriti mitmeid edukaid rünnakuid mobiilsidevõrkude vastu. Need baseerusid ühe peamise signaaliprotokolli, SS7 väärkasutamisel. Ründajatel õnnestus positsioneerida mobiilseadmete kasutajaid ja kuulata pealt nii kõnesid kui ka tekstisõnumeid. Ajal mil enamik viimase aja ründeid paljastavad nõrkusi lõppkasutajate seadmete tarkvaras, paljastavad need hiljutised rünnakud põhivõrkude endi haavatavust. Teadaolevalt on mobiilsete telekommunikatsioonivõrkude tööstuses raskusi haavatavuste õigeaegsel avastamisel ja nende mõistmisel. Käesolev töö on osa püüdlusest neid probleeme mõista. Töö annab põhjaliku ülevaate ja analüüsib teadaolevaid rünnakuid ning toob välja võimalikud lahendused. Rünnakud võivad olla väga suurte tagajärgedega, kuna vaatamata SS7 protokolli vanusele, jääb see siiski peamiseks signaaliprotokolliks mobiilsidevõrkudes veel pikaks ajaks. Uurimustöö analüüs ja tulemused aitavad mobiilsideoperaatoritel hinnata oma võrkude haavatavust ning teha paremaid investeeringuid oma taristu turvalisusele. Tulemused esitletakse mobiilsideoperaatoritele, võrguseadmete müüjatele ning 3GPP standardi organisatsioonile.In the last quarter of 2014, several successful attacks against mobile networks were demonstrated. They are based on misuse of one of the key signaling protocol, SS7, which is extensively used in the mobile communication backend for signaling tasks such as call and mobility management. The attackers were able to locate the mobile users and intercept voice calls and text messages. While most attacks in the public eye are those which exploits weaknesses in the end-device software or radio access links, these recently demonstrated vulnerabilities exploit weaknesses of the mobile core networks themselves. Understandably, there is a scramble in the mobile telecommunications industry to understand the attacks and the underlying vulnerabilities. This thesis is part of that effort. This thesis presents a broad and thorough overview and analysis of the known attacks against mobile network signaling protocols and the possible mitigation strategies. The attacks are presented in a uniform way, in relation to the mobile network protocol standards and signaling scenarios. Moreover, this thesis also presents a new attack that enables a malicious party with access to the signaling network to remove lost or stolen phones from the blacklist that is intended to prevent their use. Both the known and new attacks have been confirmed by implementing them in a controlled test environment. The attacks are serious because SS7, despite its age, remains the main signaling protocol in the mobile networks and will still long be required for interoperability and background compatibility in international roaming. Moreover, the number of entities with access to the core network, and hence the number of potential attackers, has increased significantly because of changes in regulation and opening of the networks to competition. The analysis and new results of this thesis will help mobile network providers and operators to assess the vulnerabilities in their infrastructure and to make security-aware decisions regarding their future investments and standardization. The results will be presented to the operators, network-equipment vendors, and to the 3GPP standards body

Estimation of Travel Time using Temporal and Spatial Relationships in Sparse Data

Travel time is a basic measure upon which e.g. traveller information systems, traffic management systems, public transportation planning and other intelligent transport systems are developed. Collecting travel time information in a large and dynamic road network is essential to managing the transportation systems strategically and efficiently. This is a challenging and expensive task that requires costly travel time measurements. Estimation techniques are employed to utilise data collected for the major roads and traffic network structure to approximate travel times for minor links. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate a growing number of cars and provide accurate information for routing, e.g. self-driving vehicles. This study aims to address the aforementioned challenges by introducing a methodology able to estimate travel times in near-real-time by using historical sparse travel time data. To this end, an investigation of temporal and spatial dependencies between travel time of traffic links in the datasets is carefully conducted. Two novel methodologies are proposed, Neighbouring Link Inference method (NLIM) and Similar Model Searching method (SMS). The NLIM learns the temporal and spatial relationship between the travel time of adjacent links and uses the relation to estimate travel time of the targeted link. For this purpose, several machine learning techniques including support vector machine regression, neural network and multi-linear regression are employed. Meanwhile, SMS looks for similar NLIM models from which to utilise data in order to improve the performance of a selected NLIM model. NLIM and SMS incorporates an additional novel application for travel time outlier detection and removal. By adapting a multivariate Gaussian mixture model, an improvement in travel time estimation is achieved. Both introduced methods are evaluated on four distinct datasets and compared against benchmark techniques adopted from literature. They efficiently perform the task of travel time estimation in near-real-time of a target link using models learnt from adjacent traffic links. The training data from similar NLIM models provide more information for NLIM to learn the temporal and spatial relationship between the travel time of links to support the high variability of urban travel time and high data sparsity.Ministry of Education and Training of Vietna

Securing large cellular networks via a data oriented approach: applications to SMS spam and voice fraud defenses

University of Minnesota Ph.D. dissertation. December 2013. Major: Computer Science. Advisor: Zhi-Li Zhang. 1 computer file (PDF); x, 103 pages.With widespread adoption and growing sophistication of mobile devices, fraudsters have turned their attention from landlines and wired networks to cellular networks. While security threats to wireless data channels and applications have attracted the most attention, attacks through mobile voice channels, such as Short Message Service (SMS) spam and voice-related fraud activities also represent a serious threat to mobile users. In particular, it has been reported that the number of spam messages in the US has risen 45% in 2011 to 4.5 billion messages, affecting more than 69% of mobile users globally. Meanwhile, we have seen increasing numbers of incidents where fraudsters deploy malicious apps, e.g., disguised as gaming apps to entice users to download; when invoked, these apps automatically - and without users' knowledge - dial certain (international) phone numbers which charge exorbitantly high fees. Fraudsters also frequently utilize social engineering (e.g., SMS or email spam, Facebook postings) to trick users into dialing these exorbitant fee-charging numbers. Unlike traditional attacks towards data channels, e.g., Email spam and malware, both SMS spam and voice fraud are not only annoying, but they also inflict financial loss to mobile users and cellular carriers as well as adverse impact on cellular network performance. Hence the objective of defense techniques is to restrict phone numbers initialized these activities quickly before they reach too many victims. However, due to the scalability issues and high false alarm rates, anomaly detection based approaches for securing wireless data channels, mobile devices, and applications/services cannot be readily applied here. In this thesis, we share our experience and approach in building operational defense systems against SMS spam and voice fraud in large-scale cellular networks. Our approach is data oriented, i.e., we collect real data from a large national cellular network and exert significant efforts in analyzing and making sense of the data, especially to understand the characteristics of fraudsters and the communication patterns between fraudsters and victims. On top of the data analysis results, we can identify the best predictive features that can alert us of emerging fraud activities. Usually, these features represent unwanted communication patterns which are derived from the original feature space. Using these features, we apply advanced machine learning techniques to train accurate detection models. To ensure the validity of the proposed approaches, we build and deploy the defense systems in operational cellular networks and carry out both extensive off-line evaluation and long-term online trial. To evaluate the system performance, we adopt both direct measurement using known fraudster blacklist provided by fraud agents and indirect measurement by monitoring the change of victim report rates. In both problems, the proposed approaches demonstrate promising results which outperform customer feedback based defenses that have been widely adopted by cellular carriers today.More specifically, using a year (June 2011 to May 2012) of user reported SMS spam messages together with SMS network records collected from a large US based cellular carrier, we carry out a comprehensive study of SMS spamming. Our analysis shows various characteristics of SMS spamming activities. and also reveals that spam numbers with similar content exhibit strong similarity in terms of their sending patterns, tenure, devices and geolocations. Using the insights we have learned from our analysis, we propose several novel spam defense solutions. For example, we devise a novel algorithm for detecting related spam numbers. The algorithm incorporates user spam reports and identifies additional (unreported) spam number candidates which exhibit similar sending patterns at the same network location of the reported spam number during the nearby time period. The algorithm yields a high accuracy of 99.4% on real network data. Moreover, 72% of these spam numbers are detected at least 10 hours before user reports.From a different angle, we present the design of Greystar, a defense solution against the growing SMS spam traffic in cellular networks. By exploiting the fact that most SMS spammers select targets randomly from the finite phone number space, Greystar monitors phone numbers from the gray phone space (which are associated with data only devices like data cards and modems and machine-to-machine communication devices like point-of-sale machines and electricity meters) to alert emerging spamming activities. Greystar employs a novel statistical model for detecting spam numbers based on their footprints on the gray phone space. Evaluation using five month SMS call detail records from a large US cellular carrier shows that Greystar can detect thousands of spam numbers each month with very few false alarms and 15% of the detected spam numbers have never been reported by spam recipients. Moreover, Greystar is much faster than victim spam reports. By deploying Greystar we can reduce 75% spam messages during peak hours. To defend against voice-related fraud activities, we develop a novel methodology for detecting voice-related fraud activities using only call records. More specifically, we advance the notion of voice call graphs to represent voice calls from domestic callers to foreign recipients and propose a Markov Clustering based method for isolating dominant fraud activities from these international calls. Using data collected over a two year period from one of the largest cellular networks in the US, we evaluate the efficacy of the proposed fraud detection algorithm and conduct systematic analysis of the identified fraud activities. Our work sheds light on the unique characteristics and trends of fraud activities in cellular networks, and provides guidance on improving and securing hardware/software architecture to prevent these fraud activities

An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make these systems also sustainable from an energetic point of view. In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e.g., accuracy in object detection and frames processing rate) with energy consumption. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure that only needs a small number of empirical samples. The final set of configurations are selected using weighted gray relational analysis, and mapped to the operation modes of the self-adaptive application. We validate our approach on an AI-based application for pedestrian detection. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81\% of energy while loosing only between 2% and 6% in accuracy

COST-EFFECTIVE RASPBERRY PI BASED SURVEILLANCE SYSTEM USING INNOVATIVE TECHNOLOGY

Home security is paramount. Criminals break into houses and cart away valuables and sometimes injure/kill the property owners. Though the existing home security system is effective however it is not affordable to the majority. Thus, due to the high cost of CCTV or IP camera systems alike, there is a need to develop a cost-effective surveillance system using innovative technology. This study develops and builds a prototype of a low-cost security system based on Raspberry Pi microcomputer. The Raspberry Pi will interface with Pi camera without interfacing the device with a PIR sensor, remotely send an email to a prescribed mail hub and also send SMS alert to the facility owner and or security agency

IoT Anomaly Detection Methods and Applications: A Survey

Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.Comment: 22 page