Ratan K. Guha

Effective intrusion detection using multiple sensors in wireless ad hoc networks

In this paper we propose a distributed intrusion detection system for ad hoc wireless networks based on mobile agent technology. Wireless networks are particularly vulnerable to intrusion, as they operate in open medium, and use cooperative strategies for network communications. By efficiently merging audit data from multiple network sensors, we analyze the entire ad hoc wireless network for intrusions and try to inhibit intrusion attempts. In contrast to many intrusion detection systems designed for wired networks, we implement an efficient and bandwidth-conscious framework that targets intrusion at multiple levels and takes into account distributed nature of ad hoc wireless network management and decision policies.

Intrusion detection using mobile agents in wireless ad hoc networks

In this paper we propose a distributed intrusion detection system for ad hoc wireless networks based on mobile agent technology. Wireless networks are particularly vulnerable to intrusion, as they operate in open medium, and use cooperative strategies for network communications. By efficiently merging audit data from multiple network sensors, we analyze the entire ad hoc wireless network for intrusions and try to inhibit intrusion attempts. In contrast to many intrusion detection systems designed for wired networks, we implement an efficient and bandwidth-conscious framework that targets intrusion at multiple levels and takes into account the distributed nature of ad hoc wireless network management and decision policies.

Evolving content in the Galactic Arms Race video game

Video game content includes the levels, models, items, weapons, and other objects encountered and wielded by players during the game. In most modern video games, the set of content shipped with the game is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly renewed, players would remain engaged longer in the evolving stream of novel content. To realize this ambition, this paper introduces the content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) algorithm, which automatically evolves game content based on player preferences, as the game is played. To demonstrate this approach, the Galactic Arms Race (GAR) video game is also introduced. In GAR, players pilot space ships and fight enemies to acquire unique particle system weapons that are evolved by the game. As shown in this paper, players can discover a wide variety of content that is not only novel, but also based on and extended from previous content that they preferred in the past. The implication is that it is now possible to create games that generate their own content to satisfy players, potentially significantly reducing the cost of content creation and increasing the replay value of games.

Automatic Content Generation in the Galactic Arms Race Video Game

Simulation and game content includes the levels, models, textures, items, and other objects encountered and possessed by players during the game. In most modern video games and in simulation software, the set of content shipped with the product is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly and automatically renewed, players would remain engaged longer. This paper introduces two novel technologies that take steps toward achieving this ambition: 1) a new algorithm called content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) is introduced that automatically generates graphical and game content while the game is played, based on the past preferences of the players, and 2) Galactic Arms Race (GAR), a multiplayer video game, is constructed to demonstrate automatic content generation in a real online gaming platform. In GAR, which is available to the public and playable online, players pilot space ships and fight enemies to acquire unique particle system weapons that are automatically evolved by the cgNEAT algorithm. A study of the behavior and results from over 1000 registered online players shows that cgNEAT indeed enables players to discover a wide variety of appealing content that is not only novel, but also based on and extended from previous content that they preferred in the past. Thus, GAR is the first demonstration of evolutionary content generation in an online multiplayer game. The implication is that with cgNEAT it is now possible to create applications that generate their own content to satisfy users, potentially reducing the cost of content creation and increasing entertainment value from single-player to massively multiplayer online games (MMOGs) with a constant stream of evolving content.

Detecting Obfuscated Viruses Using Cosine Similarity Analysis

Virus writers are getting smarter by the day. They are coming up with new, innovative ways to evade signature detection by anti-virus software. One such evasion technique used by polymorphic and metamorphic viruses is their ability to morph code so that signature based detection techniques fail. These viruses change form such that every new infected file has different strings, rendering string based signature detection practically useless against such viruses. Our work is based on the premise that given a variant of morphed code, we can detect any obfuscated version of this code with high probability using some simple statistical techniques. We use the cosine similarity function to compare two files based on static analysis of the portable executable (PE) format. Our results show that for certain evasion techniques, it is possible to identify polymorphic/metamorphic versions of files based on cosine similarity

Efficient Virus Detection Using Dynamic Instruction Sequences

In this paper, we present a novel approach to detect unknown virus using dynamic instruction sequences mining techniques. We collect runtime instruction sequences from unknown executables and organize instruction sequences into basic blocks. We extract instruction sequence patterns based on three types of instruction associations within derived basic blocks. Following a data mining process, we perform feature extraction, feature selection and then build a classification model to learn instruction association patterns from both benign and malicious dataset automatically. By applying this classification model, we can predict the nature of an unknown program. We also build a program monitor which is able to capture runtime instruction sequences of an arbitrary program. The monitor utilizes the derived classification model to make an intelligent guess based on the information extracted from instruction sequences to decide whether the tested program is benign or malicious. Our result shows that our approach is accurate, reliable and efficient.

NEAT Particles: Design, Representation, and Animation of Particle System Effects

Particle systems are a representation, computation, and rendering method for special effects such as fire, smoke, explosions, electricity, water, magic, and many other phenomena. This paper presents NEAT particles, a new design, representation, and animation method for particle systems tailored to real-time effects in video games and simulations. In NEAT particles, the neuroevolution of augmenting topologies (NEAT) method evolves artificial neural networks (ANN) that control the appearance and motion of particles. NEAT particles affords three primary advantages over traditional particle effect development methods. First, it decouples the creation of new particle effects from mathematics and programming, enabling users with little knowledge of either to produce complex effects. Second, it allows content designers to evolve a broader range of effects than typical development tools through a form of interactive evolutionary computation (IEC). And finally, it acts as a concept generator, allowing users to interactively explore the space of possible effects. In the future such a system may allow content to be evolved in the game itself, as it is played

Interactive Evolution of Particle Systems for Computer Graphics and Animation

Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are the de facto method in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation.

Adaptive Connection Admission Control and Packet Scheduling for QoS Provisioning in Mobile WiMAX

The authors propose a connection admission control (CAC) scheme and a packet scheduling algorithm for the IEEE 802.16e-2005 standard for fixed and mobile broadband wireless access systems. CAC reserves an adaptive temporal channel bandwidth for mobile subscriber stations based on most recent requests to assure seamless handoff of connections, while the scheduler allocates physical layer slots to user packets based on the corresponding applications data rate and latency characteristics. The effectiveness of the proposed architecture is evaluated though simulations. It is shown that 1) when the system is moderately loaded, the proposed CAC performs better than a fixed guard channel scheme in terms of reducing handoff dropping and new call blocking probabilities; 2) the proposed packet scheduling scheme prioritizes real-time over non real-time traffic in accordance with the quality of service (QoS) parameters of service flows defined in the IEEE standard.

The Two's Complement Quasi-Serial Multiplier

This correspondence develops a multiplier for twos complement numbers, similar to the quasi-serial multiplier which operates on sign magnitude numbers. The method offers an alternative to add shift techniques for low cost twos complement multiplication.