Hossam M. Faheem

A multi-agent based system for intrusion detection

This article describes a framework for intrusion detection using agent-based technology. Agents are ideally qualified due to their reactivity, interactivity, autonomy and intelligence. The system discussed is implemented in a TCP/IP LAN (local area network) environment. It represents a step towards a complete multi-agent based system for networking security.

Evaluating how well agent-based IDS perform

Intelligent agents - as a modern artificial intelligence concept - are now widely deployed in various software systems. The agent can be defined as a software entity which functions continuously and autonomously in a particular environment, able to carry out activities in a flexible and intelligent manner that is responsive to changes in the environment, and able to learn from its experience. An intrusion detection system can be decomposed into steps where an agent can perform a single or more step. But performance evaluation techniques of their use are still in the early stage. This infancy status holds especially true for agents used in intrusion detection systems (IDS) since they are new to this field. We believe that the two most important factors to measure for a given IDS are the risk time and the detection time. The risk time is the time at which the computer could be under risk of attack since the intrusion is not discovered yet. The smaller the detection time is the smaller the risk time. Thus, as a step towards a complete evaluation strategy, a simple IDS with agents was tested.

Multi-agent based framework for target tracking using a real time vision system

This paper presents a multi-agent based framework for target tracking. We exploit the agent-oriented software paradigm with its characteristics that provide intelligent autonomous behavior together with a real time computer vision system to achieve high performance real time target tracking. The framework consists of four layers; interface, strategic, management, and operation layers. Interface layer receives from the user the tracking parameters such as the number and type of trackers and targets and type of the tracking environment, and then delivers these parameters to the subsequent layers. Strategic (decision making) layer is provided with a knowledge base of target tracking methodologies that are previously implemented by researchers in diverse target tracking applications and are proven successful. And by inference in the knowledge base using the user input a tracking methodology is chosen. Management layer is responsible for pursuing and controlling the tracking methodology execution. Operation layer represents the phases in the tracking methodology and is responsible for communicating with the real-time computer vision system to execute the algorithms in the phases. The framework is presented with a case study to show its ability to tackle the target tracking problem and its flexibility to solve the problem with different tracking parameters. This paper describes the ability of the agent-based framework to deploy any real-time vision system that fits in solving the target tracking problem. It is a step towards a complete open standard, real-time, agent-based framework for target tracking.

A multiagent-based approach for managing security policy

This paper describes a framework for a multiagent-based system that can be used to manage security policies. The suggested framework consists of three layers they are: core security policy layer, multiagent-based abstraction layer, and security products layer. Core security policy and features of security products are collected and interpreted into well-defined security rules (from the IT point of view) and security features respectively. The multiagent-based abstraction layer is responsible for converting the row security policy into specific configuration profiles or scripts that can be used to configure different security products. This scenario eliminates the difficulty of configuring and implementing a given security policy. Examples that describe different agents behaviors and graphical user interfaces deployed in writing security policies are discussed. The system is dynamic and allows for new security policy updates to be implemented.

Accelerating Pairwise DNA Sequence Alignment using the CUDA Compatible GPU

present a novel implementation of the pairwise DNA sequence alignment problem other than the Dynamic programming solution presented by Smith Waterman Algorithm. The proposed implementation uses CUDA; the parallel computing platform and programming model invented by NVIDIA. The main idea of the proposed implementation is assigning different nucleotide weights then merging the sub- sequences of match using the GPU Architecture according to predefined rules to get the optimum local alignment. We parallelize the whole solution for the pairwise DNA sequence alignment using CUDA and compare the results against a similar semi-parallelized solution and a traditional Smith- Waterman implementation on traditional processors; Experimental results demonstrate a considerable reduction in the running time.

Design and implementation of a hybrid MPI-CUDA model for the Smith-Waterman algorithm

This paper provides a novel hybrid model for solving the multiple pair-wise sequence alignment problem combining message passing interface and CUDA, the parallel computing platform and programming model invented by NVIDIA. The proposed model targets homogeneous cluster nodes equipped with similar Graphical Processing Unit (GPU) cards. The model consists of the Master Node Dispatcher (MND) and the Worker GPU Nodes (WGN). The MND distributes the workload among the cluster working nodes and then aggregates the results. The WGN performs the multiple pair-wise sequence alignments using the Smith-Waterman algorithm. We also propose a modified implementation to the Smith-Waterman algorithm based on computing the alignment matrices row-wise. The experimental results demonstrate a considerable reduction in the running time by increasing the number of the working GPU nodes. The proposed model achieved a performance of about 12 Giga cell updates per second when we tested against the SWISS-PROT protein knowledge base running on four nodes.

Massively Parallelized DNA Motif Search on FPGA

Understanding the mechanisms that regulate gene expression is a major challenge in biology. Motif finding problem is considered an important task in this challenge. Addressing the complexity nature of the problem together with being very data intensive has encouraged introducing field programmable gate arrays (FPGAs) to the problem. FPGAs are very powerful in such computationally intensive tasks. Many Algorithms are introduced to solve this problem. They can be categorized into pattern-based and profile-based algorithms [1]. Pattern-based algorithms include PROJECTION[4], MULTIPROFILER[6], and MITRA[3]. Profile-based algorithms includes CONSENSUS[7], MEME[2] and Gibbs sampling[5]. Although these algorithms show good performance, they still can fail to identify all the possible motifs in the sequences. They also show poor performance when trying to solve the challenge problem presented by Pvzner and Sze[8]. Some of them fail due to local search, others which are based on statistical measures fail to separate the motif from the background sequences. We can also categorize Motif finding algorithms due to the solution they provide. Some algorithms provide exact solution others provide approximate one. Brute Force algorithm is an exact algorithm but it suffers from the intractability of its running time. It increases exponentially with the size of the required motif. This makes the Brute Force unsuitable for long motifs. Our enhanced Brute Force algorithm, skip Brute Force, can predict the quality of the computed motif. The algorithm skips those iterations which will lead to a poor scored motif, thus leads to a better running time than the original Brute Force. This enhancement guarantees the same exactness of the Brute Force. But, it still suffers from the intractable running time for long motifs. Many approaches can be applied to speed up the running time of any algorithm using hardware; examples include chip multiprocessors, graphics processing units (GPUs) and (FPGAs). GPUs are inexpensive, commodity parallel devices and have already been employed as powerful coprocessors for a large number of applications. However, GPUs have limited instructions and limited parallelism relative to FPGAs configurability. The research in [10] employed acceleration using GPU. Another approach uses clusters of workstations [12]. However, clusters typically have high maintenance and energy costs

A new standard security policy language

The article presents a standard security policy language (SSPL) that provides a flexible, formal, dynamic, and unambiguous language to allow the security officers developing their own security policies with the rules in a readable and formal format. The proposed SSPL simplifies the task of developing standard unambiguous policy statement. The policies can be developed in any specific domain free of any restriction (if the ontology exists the policy will be enforceable, or else it will not). The idioms of the domain ontology can be developed and added to the language at runtime. And the SSPL framework will refresh the new concepts. The proposed SSPL allows for policy automation since the framework will receive the policy configuration and send it to the best matched (after comparing the policy rules class with the registered applications classes) security solution.

A Multiagent-based Framework for Integrating Biological Data

Biological data has been rapidly increasing in volume in different Web data sources. To query multiple data sources manually on the internet is time consuming for biologists. Therefore, systems and tools that facilitate searching multiple biological data sources are needed. Traditional approaches to build distributed or federated systems do not scale well to the large, diverse, and the growing number of biological data sources. Internet search engines allow users to search through large numbers of data sources, but provide very limited capabilities for locating, combining, processing, and organizing information. A promising approach to this problem is to provide access to the large number of biological data sources through a multiagent-based framework where a set of agents can cooperate with each other to retrieve relevant information from different biological Web databases. The proposed system uses a mediator-based integration approach with domain ontology, which uses as a global schema. In this paper we propose a multiagent-based framework that responds to biological queries according to its biological domain ontology.

A Framework for Multiagent-Based System for Intrusion Detection

Networking security demands have been considerably increased during the last few years. One of the critical networking security applications is the intrusion detection system. Intrusion detection systems should be faster enough to catch different types of intruders. This paper describes a framework for multiagent-based system for intrusion detection using the agent-based technology. Agents are ideally qualified to play an important role in intrusion detection systems due to their reactivity, interactivity, autonomy, and intelligence. The system is implemented in a real TCP/IP LAN environment. It is considered a step towards a complete multiagent based system for networking security.