Timo Hämäläinen

Bio: Timo Hämäläinen is an academic researcher from University of Jyväskylä. The author has contributed to research in topics: Quality of service & Encoder. The author has an hindex of 38, co-authored 560 publications receiving 7648 citations. Previous affiliations of Timo Hämäläinen include Dalian Medical University & Nokia.

Design and implementation of an enhanced security layer for Bluetooth

Abstract: This paper proposes a new Enhanced Security Layer (ESL) for Bluetooth. The security level is increased by replacing the encryption with AES and adding in- tegrity protection. As ESL is placed on the top of the standard controller interface, it can be integrated into any Bluetooth implementation. A prototype implementation of ESL is presented. The security processing is implemented in hardware for high performance. The design consumes fewer resources and has higher throughput (214 Mb/s) than the standard design. The programming interface supports straightforward application development.

Mappings of SOM and LVQ on the partial tree shape neurocomputer

Abstract: Mappings of self-organizing map (SOM) and learning vector quantization (LVQ) networks are presented for a parallel neurocomputer system called PARNEU (partial tree shape neurocomputer). The partial tree shape architecture offers many mapping possibilities at several levels of parallelism for both execution and learning mode. In this paper we present both neuron and weight parallel mapping with online updating scheme. Computational complexity and the time required in each step are considered in order to compare mappings and to find out expected performance. About 8 MCUPS can be achieved with four PUs operating at the frequency of 40 MHz.

Delay guarantee and bandwidth allocation for network services

Abstract: This paper presents a packet scheduling scheme for ensuring delay and bandwidth as a quality of service (QoS) requirement. For customers, rightful service is given while optimizing revenue of the network service provider. A gradient and fixed point type algorithms for updating the weights of a packet scheduler are derived from a revenue-based optimization problem. In the linear pricing scenario, algorithms are simple to implement. We compared algorithms with optimal brute-force method. Especially fixed point algorithm converges very fast to the optimal solution, typically in one iteration and about 40 operations, when number of classes is three. The weight updating procedures are independent on the assumption of the connections' statistical behavior, and therefore they are robust against erroneous estimates of statistics. Also, a call admission control (CAC) is implemented in context of our scenario.

End-to-end QoS issues at the integrated WLAN and 3G environments

Abstract: The WLAN integrated mobile device is designed to extend the reach of enterprise applications and to create new collaboration environments This means that interconnection with WLAN 80211e and 3G technologies are needed Supporting quality of service is one of the most important issues in 3G In this paper end-to-end QoS issues of the cooperation of the WLAN and 3G are studied We analyze how well 80211e's QoS properties perform under the different kind of network scenarios when packet sizes and channel error rates are varied

On Application Layer DDoS Attack Detection in High-Speed Encrypted Networks

Abstract: Application-layer denial-of-service attacks have become a serious threat to modern high-speed computer networks and systems. Unlike network-layer attacks, application-layer attacks can be performed by using legitimate requests from legitimately connected network machines which makes these attacks undetectable for signature-based intrusion detection systems. Moreover, the attacks may utilize protocols that encrypt the data of network connections in the application layer making it even harder to detect attacker’s activity without decrypting users network traffic and violating their privacy. In this paper, we present a method which allows us to timely detect various application-layer attacks against a computer network. We focus on detection of the attacks that utilize encrypted protocols by applying an anomaly-detection-based approach to statistics extracted from network packets. Since network traffic decryption can violate ethical norms and regulations on privacy, the detection method proposed analyzes network traffic without decryption. The method involves construction of a model of normal user behavior by analyzing conversations between a server and clients. The algorithm is selfadaptive and allows one to update the model every time when a new portion of network traffic data is available. Once the model has been built, it can be applied to detect various types of application-layer denial-ofservice attacks. The proposed technique is evaluated with realistic end user network traffic generated in our virtual network environment. Evaluation results show that these attacks can be properly detected, while the number of false alarms remains very low.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Survey of Wireless Indoor Positioning Techniques and Systems

Abstract: Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.