Melody Moh

Mobile IP telephony: mobility support of SIP

The Internet has recently become the most important, most popular way of communication. A significant new feature of the Internet is the support of telephony. Two main competing signaling protocol standards have been developed for this purpose: H.323, proposed by the ITU, and SIP, proposed by the IETF. Another new feature for the Internet is the support of terminal mobility. The IETF efforts in this field have resulted in the Mobile IP standard. Liao (1999) has shown that Mobile IP does not provide fast enough handoffs to support voice communications. Liao has also demonstrated how H.323 may be extended to offer a solution. In this paper we address several major issues for supporting mobility on SIP. We detail the mechanisms for extending SIP to support location management, registration of roaming mobiles, and handoffs. The mechanisms are either extensions of existing SIP, or based on Mobile IP, and thus may be readily employed over the Internet. We believe that the work presented here is an important step towards supporting mobile telephony over the Internet.

Dynamic Virtual Machine migration algorithms using enhanced energy consumption model for green cloud data centers

Cloud data centers consume an enormous amount of energy. Virtual Machine (VM) migration technology can be applied to reduce energy consumption by consolidating VMs onto the minimal number of servers and turn idle servers into power-saving modes. While most existing energy models consider mainly computing energy, an enhanced energy consumption model is formulated, which includes energy consumption for computation, for servers to switch from standby to active modes, and for communication during VM migrations. Next, two new dynamic VM migration algorithms are proposed. They apply a local regression method to predict potentially over-utilized servers, and the 0-1 knapsack dynamic programming to find the best-fit combination of VMs for migration. The time complexity of these algorithms is analyzed, which indicates that they are highly scalable. Performance is evaluated and compared with existing algorithms. The two new heuristics have significantly reduced the number of VM migration, the number of rebooted servers, and energy consumption. Furthermore, one of them has achieved the least overall SLA violations. We believe that the new energy formulation and the two new heuristics contribute significantly towards achieving green cloud computing.

Privacy and security in biomedical applications of wireless sensor networks

Wireless sensor network applications in healthcare and biomedical technology have received increasing attention, while associated security and privacy issues remain open areas of consideration. The relevance of this technology to our growing elderly population, as well as our increasingly over-crowded and attention-drained healthcare systems, is promising. However, prior to the emergence of these systems as a ubiquitous technology, healthcare providers and regulatory agencies must determine an acceptable level of security and privacy. This paper will review biomedical applications of wireless sensor networks, identify security and privacy issues to be addressed, and note some of the proposed methods for securing these systems.

Detecting Web Attacks Using Multi-stage Log Analysis

Web-based applications have gained universal acceptance in every sector of lives, including social, commercial, government, and academic communities. Even with the recent emergence of cloud technology, most of cloud applications are accessed and controlled through web interfaces. Web security has therefore continued to be fundamentally important and extremely challenging. One major security issue of web applications is SQL-injection attacks. Most existing solutions for detecting these attacks use log analysis, and employ either pattern matching or machine learning methods. Pattern matching methods can be effective, dynamic, they however cannot detect new kinds of attacks. Supervised machine learning methods can detect new attacks, yet they need to rely on an off-line training phase. This work proposes a multi-stage log analysis architecture, which combines both pattern matching and supervised machine learning methods. It uses logs generated by the application during attacks to effectively detect attacks and to help preventing future attacks. The architecture is described in detail, a proof-of-concept prototype is implemented and hosted on Amazon AWS, using Kibana for pattern matching and Bayes Net for machine learning. It is evaluated on 10,000 logs for detecting SQL injection attacks. Experiment results show that the two-stage system has combined the advantages of both systems, and has substantially improved the detection accuracy. The proposed work is significant in advancing web securities, while the multi-stage log analysis concept would be highly applicable to many intrusion detection applications.

Specification-based intrusion detection for H.323-based voice over IP

Voice over IP (VoIP) has been in a state of rapid development due to its economical advantage over traditional telephone services. Denial of service (DoS) attack has been a major security threat for many computer systems. This work introduces a specification-based intrusion detection system to protect H.323 gatekeepers from both external and internal DoS attacks. Based on the protocol for RAS (Registration, Admission Status) messages, a finite-state machine specification for correct behaviors between a gatekeeper and endpoints is produced. Security requirements against these DoS attacks are established, resulting in a formal protocol specification for secured gatekeepers. Developing the proposal into a practical solution, an intrusion detection module is built and incorporated into the open source software GNU Gatekeeper (also named OpenH323GK). A simple, proof-of-concept prototype has been built; the secured H. 323 gateway is able to fend off DoS attacks launched from GNU OpenPhone clients

Dual Trust Secure protocol for cluster-based wireless sensor networks

In addressing the unique security challenges faced by wireless sensor networks, this paper proposes a novel, two-level trust-based scheme. The solution is described on top of a well-known, cluster-based protocol, LEACH (Low Energy Adaptive Clustering Hierarchy), and is named DTSLEACH (Dual-Trust Secure LEACH). It includes authentication, confidentiality, integrity, and trust mechanisms. Performance evaluation shows that, compared with two other existing protocols, DTSLEACH provides strong performance in security, energy and memory efficiencies, as well as network throughput.

Evaluation of dynamic tree-based data gathering algorithms for wireless sensor networks

One major challenge in sensor networks is to maximize network life under the constraint of extremely limited power supply. Thus, two important design issues of routing and data-gathering protocols are 1) minimizing energy consumption in sensor nodes and 2) adapting to node failures. This paper studies two tree-based data gathering protocols, based on distributed versions of shortest path tree (SPT) and maximum leaf tree (MLT) algorithms. Furthermore, the two distributed algorithms are extended to be dynamic and robust. A localized tree-reconstruction scheme, localized flooding algorithm, is added to handle joining and leaving (death) of sensor nodes. Accurate energy consumption has been modeled for both leaf-nodes and intermediate nodes, when sending and receiving control and data packets. The resulting dynamic algorithms are fast to adapt network changes. Performance is evaluated through detailed simulation. Comparing with MLT, due to its simplicity and smaller number of control message exchanges, SPT achieves better energy efficiency and less delay in tree constructions, data transmissions, and dynamic tree reconstructions

Path-Based Reputation System for MANET Routing

Most existing reputation systems in mobile ad hoc networks (MANET) consider only node reputations when selecting routes. Therefore, reputation and trust are only ensured within a one-hop distance when routing decisions are made. This often fails to provide the most reliable, trusted route. In this paper we propose a system that is based on path reputation , which is computed from the reputation and trust values of each and every node in the route. This greatly enhances the reliability of the resulting routes. The system is simulated on top of the AODV (Ad-hoc On-demand Distance Vector) routing protocol. It is effective at detecting misbehaving nodes, including selfishness and worm-hole attacks. It greatly improves network throughput in the midst of malicious nodes and requires very limited message overhead. To our knowledge, this is the first path-based reputation system proposal that is applicable to non-source-based routing schemes.

Experimental evaluation of Linux TCP for adaptive video streaming over the cloud

With the popularity of video streaming on the Internet an entirely new type of media player has been created called the adaptive video player that dynamically adjusts video quality depending on available network bandwidth. When combined with cloud computing this new way of serving media has the potential to change the video streaming landscape by allowing content to be streamed reliably from anywhere in the world. In this paper we evaluate the use of different TCP congestion control algorithms over the Cloud including five major variants: Cubic, Bic, Vegas, H-TCP, and HighSpeed TCP. Both private (based on Eucalyptus) and public (Amazon EC2) cloud environments are considered. We evaluate video streaming performance, TCP friendliness, and for each criterion identify the top performer. We believe that this is an important step towards optimizing TCP or another transport protocol for supporting cloud computing.

On Multi-tier Sentiment Analysis Using Supervised Machine Learning

Document management and Information Retrieval tasks have rapidly increased due to the availability of digital documents anytime, any place. The need for automatic extraction of document information has become prominent in information organization and knowledge discovery. Text Classification is one such solution, where in the natural language text is assigned to one or more predefined categories based on the content. This work focuses on sentiment analysis, also known as opinion mining. It is a way of automatically extracting and analyzing the emotions and opinions, and not facts, of messages and posts. A multi-tier classification architecture is proposed, which consists of major modules such as data cleaning and pre-processing, feature selection, and classifier training that includes a multi-tier prediction model. The architecture and its components are carefully described. Four classifiers (Naïve Bayes, SVM, Random Forest, and SGD) are used in the experiments, which evaluate the performance of the proposed multi-tier architecture by analyzing the sentiments and opinions of 150,000 movie reviews. Results have shown that the multi-tier model is able to significantly improve prediction accuracy over the single-tier model by more than 10%, the improvement is significant when customized dictionary is used. We believe that the proposed multi-tier classification architecture, with the various feature selection techniques described and used, are significant, and are readily applicable to many other areas of sentiment analysis.