Secure Computing Corporation was a public company that developed and sold computer security appliances and hosted services to protect users and data. Macfee acquired the company in 2008.
Secure Computing consists of several merged units. one of the oldest being Enigma Logic Inc. which started around 1982. Bob Bosen the founder claims to create the first security token to provide challenge-response authentication. Bosen published a computer game for the TRS80 home computer in 1979 called space riders that used a simple challenge response mechanism for copy protection. People who use the mechanism encouraged him to repackage it for remote authentication. Bosen started Enigma Logic to do so and filed for patents in 1982 a patent was issued in the United Kingdom in 1986. The challenge portion of the challenge response eliminated to produce a one-time password token similar to the secure product.
Enigma Logic merged with Secure Computing Corporation in 1996.Secure Computing acquired the Smart Filter product line by purchasing Webster Network Strategies, the producer of the Web Track product, in 2006. The acquisition included the domain name webster.com which was eventually sold to the publishers of Webster's Dictionary
Shortly after acquiring the Webster Smart Filter product. Secure Computing merged with Border Network Technologies, a Canadian company selling the Border ware firewall. Border Network Technologies boasted an excellent product and a highly developed set of sales channels; some said that the sales channels were a major inducement for the merger. Although the plan was to completely merge the Border ware product with Sidewinder, and to offer a single product to existing users of both products, this never quite succeeded. The Border ware business unit was sold to a new company, Border ware Technologies Inc., formed by one of the original Border ware founders.
By this time, the mergers yielded a highly distributed company with an office in Minnesota, Florida, California, and two or three in Ontario. This proved unwieldy, and the company scaled back to offices in Minnesota and California. In 2002, the company took over the Gauntlet Firewall product from Network Associates.
In 2003, Secure Computing acquired N2H2, the makers of the Bess web filtering package. There has been some consolidation of Bess and Smart Filter, and Bess is now referred to as "Smart filter, Bess edition" in company literature.
An acquisition of Cyber Guard was announced in August 2005 and approved in January 2006. A year earlier, Cyber Guard had attempted to acquire Secure Computing, but the proposal had been rejected. The largest merger by Secure Computing at the time, it resulted in the addition of several product lines to the company, including three classes of firewalls, content and protocol filtering systems, and an enterprise-wide management system for controlling all of those products. Several offices were also added, including Cyber Guard's main facility in Deerfield Beach, Florida, as well as the Web washer development office in Paderborn, Germany, and a SnapGear development office in Brisbane, Australia.
In 2006, the company merged with Atlanta-based Cipher Trust, a developer of email security solutions. The merger was announced in July 2006 and completed in August 2006. On July 30, 2008, Secure Computing announced its intention to sell the Safe Word authentication product line to Aladdin Knowledge Systems, leaving the company with a business focused on web/mail security and firewalls. On September 22, 2008, McAfee announced its intention to acquire Secure Computing. The combined company would form the world's largest dedicated security company at the time.
1.1OVERVIEW
The application-level traffic analysis attacks target at disclosing sensitive information at the application level. Song et al. found that despite encryption and authentication mechanisms used in SSH, it is possible to obtain interkeystroke timing information from SSH packets since SSH sends out each keystroke in one separate packet during the interactive mode. Based on the interkeystroke timing information, they demonstrated that it was possible to reveal passwords used in SSH logins. Sun et al. gave a quantitative analysis for identifying a webpage even if encryption and anonym zing proxies are used. They took advantage of the fact that a number of HTTP features such as the number and size of objects can be used as signatures to identify webpages with some accuracy. Unless the anonymizer addresses this, these signatures are visible to the adversary. Herrmann et al. proposed to identify websites by applying common text mining techniques to the normalized frequency distribution of observable IP packet sizes. Lu et al. showed the feasibility of website fingerprinting based on packet ordering information. Wright et al. showed packet size information of VoIP packets can be used by an adversary to identify a spoken phrase in VoIP calls. In, it was shown that packet size information of VoIP packets could also be used to detect languages used in conversations even the conversations were encrypted.
1.2PROBLEM DEFINITION
In today's world the major contribution of networking is concentrated in the file encryption techniques and privacy techniques. Less technology is available to maintain the privacy of the speech file. Hence more advanced technique will spoil the originality of the file sufficient care should be taken in approaching the speech file transaction by the same time enormous amount of hacking is taken place to collapse the file. A modern approach is utilized to overcome the above real problems.
EXISTING SYSTEM
The application level traffic analysis attacks target at disclosing sensitive information at the application level. Song et al. Found that despite encryption and authentication mechanisms used in SSH, it is possible to obtain interkeystroke timing information from SSH packets. SSH sends out each keystroke in one separate packet during the interactive mode. Base on the interkeystroke timing information, they demonstrated that it was possible to reveal passwords used in SSH Logins. Herrmann et al. proposed to identify websites by applying common text mining techniques to the normalized frequency distribution of observable IP packet sizes. Lu et al. showed the feasibility of website fingerprinting based on packet ordering information. Wright et al. Showed packet size information of Voip packets can be used by an adversary to identify a spoken phrase in Voip calls. In, it was shown that packet size information of Voip packets could also be used to detect languages used in conversations even the conversations were encrypted.
The application level traffic analysis attack classified into two categories based on features of the network traffic used in these attacks. Most existing application level traffic analysis attacks are based on packet size information. proposed approaches to counter traffic analysis attacks on Voip calls and their approaches are based on modifying packet sizes. Only a few application level traffic analysis attacks are based on packet timing only. Example is the keystroke detection based on SSH packets.
Drawback:
The encrypted VoIP calls across anonymous communication system is cannot identify the correlation between the VoIP flows of the caller and the callee.
The main drawback of the existing approaches is based on the packet sizes.
CHAPTER 2
1.3 LITERATURE SURVEY
1.3.1 Internet Audiocassete
The audio cast included all the general sessions plus a few working group breakout sessions. Unlike listening to a radio broadcast, the remote participants could also talk back, as was demonstrated during a brief technical presentation on the experiment. Though the audio transmission was not perfect, it worked well enough in both directions that remote participants were able to ask cogent questions and engage in the discussions during the working group sessions.
This event was a demonstration of technology developed and tested in the DARTnet research test bed network. It was a pilot experiment that we hope will be expanded at future IETF meetings to reach more destinations and to include video, images, and shared whiteboards along with audio. This is a step toward a more distributed IETF, a goal Dave Farber and Jack Haverty challenged the community to pursue during a discussion on the IETF mailing list last fall.
Three key elements enabled the audio cast:
• Readily-available hardware and software to generate and receive audio packets at the endpoints.
• IP multicast routing to replicate the packets efficiently for distribution to a large number of recipients.
• Real-time network performance, in this case achieved only by selecting uncongested networks with sufficient bandwidth.
The first IETF audio cast was an interesting and valuable experiment both for the experimenters and the participants. Though there were some problems, the results were good enough to suggest that the experiment be continued for future IETF meetings. There are several open issues that provide promising areas for additional work:
• Better real-time performance measurement tools.
• New application hardware e.g. video cards, software e.g. shared whiteboards, and protocols.
• Real-time traffic support resource management.
• Ubiquitous multicast routing support.
• Meeting site networking and studio facilities.
Meanwhile, small-scale experiments with packet audio and video are encouraged in order to learn more about the protocol requirements. You can participate see the appendix for details.
Advantage:
It achieves efficient audio packet retrieval.
The results were good enough to suggest that the experiment be continued for future IETF meetings.
Drawbacks:
Audio transmission is difficult.
1.3.2 A Free Codec for Free Speech.
Speex is now evolving into a complete toolkit for voice over IP VoIP development, including algorithms for noise cancellation, acoustic echo cancellation and adaptive jitter buffering. This allows a developer without any signal processing knowledge to implement a VoIP client. In the meantime, Speex is being ported to architectures without a floating-point unit, allowing Speex to be used in embedded devices equipped with a fixed-point CPU or DSP.
The use of Speex for Voip imposes the following requirements:
The frame size and algorithmic delay must be small
Both the encoder and decoder must run in real-time with limited resources
The effect of lost packets during transmission must be minimized
The codec must support both narrowband and wideband
Multiple bit-rates and quality settings must be supported to take into account different connection speeds
Good compression must be achieved while avoiding known speech coding patents
Speex also has the following features, some of which are not found in other speech codecs.
Integration of narrowband and wideband using an embedded bit-stream.
Wide range of bit-rates available from 2 kbps to 44 kbps.
Dynamic bit-rate switching and Variable Bit-Rate .
Voice Activity Detection.
Variable complexity.
Ultra-wideband mode at 32 kHz.
Intensity stereo encoding option.
In this project, the origin and design goals of the Speex speech codec were presented. Also, a description of the CELP algorithm and its implementation in Speex was given. Some guidelines for programming with libspeex and choosing the right encoding options were provided in order to help developers make better use of Speex in applications.
Advantage:
Good compression is achieved
evolving into a complete toolkit for voice over IP
Drawbacks:
Packet loss is not minimized
Even though the bit stream itself has been frozen for more than two years, work on Speex is continuing. Recent improvements focus on bringing support for fixed point CPU and DSP architectures as well as providing additional functionality, such as echo cancellation and noise suppression that are useful for VoIP applications.
1.3.3 Timing Attacks in Low-Latency Mix-Based Systems.
A mix is a communication proxy that attempts to hide the correspondence between its incoming and outgoing messages. Routing communication through a chain of mixes is a powerful tool for providing unlink ability of senders and receivers despite observation of the network by a global eavesdropper and the corruption of many mix servers on the path. A mix can use a variety of techniques for hiding the relationships between its incoming and outgoing messages. In particular, it will typically transform them cryptographically, delay them, reorder them, and emit additional dummy messages in its output. But mainly for high-latency systems, Anonymous email or voting applications that do not require efficient processing. In practice, such systems may take hours to deliver a message to its intended destination.
Users desire anonymity for more interactive applications, such as web browsing, online chat, and file-sharing, all of which require a low-latency connection. A number of low-latency mix-based protocols for unlikable communications have been proposed, including ISDN-Mixes, Onion Routing, Tarzan, Web Mixes, and Freedom . Unfortunately, there are a number of known attacks on these systems that take advantage of weaknesses in mix-based protocols when they are used for low-latency applications.
The attack it considers here is timing analysis, where an attacker studies the timings of messages moving through the system to find correlations. This kind of analysis might make it possible for two attacker mixes owned or compromised by the attacker to determine that they are on the same communication path. In some systems, this allows these two attacker mixes to match the sender with her destination. Unfortunately, it is not known precisely how vulnerable these systems are in practice and whether an attacker can successfully use timing analysis for these types of attacks. For example, some research has assumed that timing analysis is possible when dummy messages are not used, though this has not been carefully examined.
This project significantly clarifies the threat posed to low-latency mix systems by timing attacks through detailed simulations and analysis. It shows that timing attacks are a serious threat and are easy to exploit by a well-placed attacker. It also measures the effectiveness of previously proposed defenses such as cover traffic and the impact of path length on the attack. Finally, it introduces a new variation of cover traffic that better defends against the attacks it considers, and demonstrates this through our analysis. Our results are based primarily on simulations of a set of attacking mixes that attempt to perform timing attacks in a realistic network setting.
Timing analysis against users of anonymous communications systems can be effective in a wide variety of network and system conditions, and therefore poses a significant challenge to the designer of such systems.
This project presented a study of both timing analysis attacks and defenses against such attacks. It has shown that, under certain assumptions, the conventional use of cover traffic is not effective against timing attacks. Furthermore, intentional packet dropping induced by attacker-controlled mixes can nullify the effect of cover traffic altogether. It proposed a new cover traffic technique, defensive dropping, to obstruct timing analysis. Our results show that end-to-end cover traffic augmented with defensive dropping is a viable and effective method to defend against timing analysis in low-latency systems.
Advantage:
It significantly clarifies the threat posed to low-latency mix systems.
Drawback:
Attacker studies the timings of messages moving through the system.
1.3.4 Tracking Anonymous Peer-to-Peer VoIP Calls on the Internet.
Voip is a technology that allows people to make phone calls through the public Internet rather than traditional Public Switched Telephone Network. Because Voip offers significant cost savings with more flexible and advanced features over Plain Old Telephone System POTS, more and more voice calls are now carried at least partially via Voip.
It chooses to investigate the popular Skype peer-to-peer VoIP calls in the context of the anonymous VPN provided. Skype offers free computer to computer VoIP calls based on KaZaa peer-to-peer technology. Several properties of Skype have made it an attractive candidate for the investigation of tracking anonymous VoIP calls on the Internet:
It is free and widely used. Since August 2003, there are over 100 million downloads of the Skype client. It is being actively used by millions of people all over the world. Skype is now included in Kazaa v3.0.
All the Skype is encrypted from end to end by 256-bit AES encryption.
Skype can automatically traverse most firewalls and NAT gateways with the help of intermediate peers.
Skype intelligently and dynamically routes the encrypted calls through peers to achieve low latency.
This means that the route and the intermediate peers of one VoIP call could be changed during a call.
It uses proprietary peer-to-peer signaling protocol to set up the VoIP calls.
Since most Skype calls are carried in UDP, it cannot directly use those anonym zing systems such as Onion Routing, Tor or anonymizer.com, who do not support anonymization of all UDP flows, to anonymize Skype VoIP calls. The author chooses to use the anonymous communication services by findnot.com that support anonymization of all IP protocols through point to point tunnel protocol.
The key challenge in tracking encrypted VoIP calls across anonymous communication system is how to identify the correlation between the VoIP flows of the caller and the caller. Since all the traffic of the peer-to-peer VoIP calls are encrypted, no signaling information is available for correlation. To be able to track encrypted, anonymous VoIP calls across the Internet, we use the timing characteristics of the anonymized VoIP flow. Unfortunately, the original inter packet arrival characteristics of VoIP flows are not distinct enough as the inter-packet timing arrival time of VoIP traffic is determined by the frame packetization interval used. This means that passive comparison of the original inters packet timing characteristics of VoIP flows will not be able to distinguish different VoIP calls.
Advantage:
The watermarked VoIP flows could be effectively identified.
Drawback:
Low latency anonimizing systems are susceptible to timing attack.
2. WORK DONE IN PHASE ONE
2.1 System Design
Speech privacy in a node
Silent suppression
Encryption
Speech data
Constant Bit Rate
Original voice
Decrypt the data using Hang over
2.11 ANALYSIS OF SYSTEM
FIGURE 1 ARCHITECTURE DIAGRAM
CHAPTER 3
SYSTEM ORGANISATION
3.1 Data Flow Diagram
Input packets
Silence Suppression
Traffic analysis attack
CBR codes
Decryption
Original Packets
Figure 2 Dataflow diagram
3.2 Sequence Diagram
CHAPTER 5
MODULES
Network Design
Voice Activity Detection
Cryptographic Technique
Hangover Mechanism
Attacker model
5. A. HMM dataset
5. B. Man in the middle attack
Performance Evaluation
4. MODULE DESCRIPTION
4.1 Network Design:
In speech communications, an analog voice signal is first converted into a voice data stream by a chosen codec. Typically in this step, compression is used to reduce the data rate. The voice data stream is then packetized in small units of typically tens of milliseconds of voice, and encapsulated in a packet stream over the Internet.
Voice data stream
Compressor
Voice signalLevel 0
Packets
Split
Voice data streamLevel1
4.2 Voice Activity Detection:
Voice Activity Detection (VAD), also called Silence suppression is designed to further save bandwidth. The main idea of the silence suppression technique is to disable voice packet transmissions when silence is detected. To prevent the receiving end of a speech communication from suspecting that the speech communication stops suddenly, comfort noise is generated at the receiving end. Silence suppression is a general feature supported in codec's, speech communication software, and protocols such as RTP.
Level 0:
Disable voice packets
Silence detection
Voice packets
Cryptographic Technique:
Voice packets generated by constant bit rate (CBR) codecs are of the same size. Encryption can pad voice packets to the same size during the encryption process, and packets in anonymity networks such as Tor are of the same size to prevent traffic analysis attacks based on packet size information.
Level
Encrypted packets
Encryption
Original packets
4.3 Modern Detection Approach
In modern detection approach a new processing technique has been be implemented for voice process model. The modern detection approach flows with various process.
4.3.1 Background suppression
In this phase the voice file is processed to separate the exact pitch from external.
4.3.2 Classification
From the output of above phase the noise, music and third party speech is classified using a modern approach called tandem algorithm.
4.3.3 Fake Traffic
From the classified approach a dummy file has been created using the silent file and classified file.
4.3.4 Rumor Riding
By implementing this mechanism the fake file and the suppressed file will transmit in different path. If the fake file is depend on noise then the packets are omitted as dead packets. If the file is based upon any music or third party file then it gets appended to the authenticated users at the end of session.
4.4 Hangover Mechanism
Hangover techniques are used in silence detectors to avoid sudden end-clipping of speeches. During hangover time, voice packets are still transmitted even when the frame energy is below the energy threshold. Traditional silence detectors use fixed-length hangover time. For modern silence detectors such as G.729B, the length of hangover time dynamically changes according to the energy of previous frames and noise.
4.5 Attacker Model
This module describes how the attacker intersecting using possible ways in our speech transaction and how the project reconfigure itself to tolerate the attacks.
5.5.1 HMM dataset
In this phase a HMM trained dataset is generated which is utilized to identify speaker dependency.
5.5.2 Man in the middle attack
In this project a man in the middle attack is embossed to describe the attacker effect in the network in hacking the file.
Tabulation For Algorithm
parameters
Forward algorithm
Baum-welch Algorithm
Voice-wavelength
128m/sec
Efficiency:33.8%
110m/sec
Efficiency:40.1%
Monotonic
20-30%
35-37%
Noise filtering
62%
69%
PROPOSED SYSTEM
In this proposed system a modern detection method is utilized to create high security and privacy value. In modern approach first background suppression is created, then the suppressed file is meant for classification separating noise and music.The classification process is done through the tandem algorithm. Through this a dummy file has been created to generate a dummy traffic.These steps will be controlled by rumour riding algorithm where it splits the suppressed file and dummy file in separate ways.If the dummy consits of noise fie means won't reach the destination .If the dummy file consists any music or any other speech means it will reach the authenticated person.
Performance Evaluation:
This module evaluates the detection performance with four metrics. Detection rate, false negative rate, false positive rate, and percentage of traces which can be tested. The two metrics, the false negative rate and the false positive rate used in performance evaluation, are calculated on the test traces. The last metric, percentage of traces which can be tested, is needed because for certain group of labeled traces, it is impossible to find a threshold .so that both the false negative rate and the false positive rate on the labeled traces are below a given tolerance.
Performance Evaluation
Percentage of Trace
False Positive Rate
False Negative Rate
Detection Rate
Figure 3 Performance Evaluation
CHAPTER 5
5.SYSTEM IMPLEMENTATION
5.1Algorithm
Modern Detection approach
In modern approach first background suppression is created, then the suppressed file is meant for classification separating noise and music.
5.1.1Background Suppression
5.1.2Classification
5.1.3fake traffic assignment
5.1.4Rumor riding
5.2 snapshot
Language Description
Java is a small, simple, safe, object oriented, interpreted or dynamically optimized, byte coded, architectural, garbage collected, multithreaded programming language with a strongly typed exception-handling for writing distributed and dynamically extensible programs.
Java is an object oriented programming language. Java is a high-level, third generation language like C, FORTRAN, Small talk, Pearl and many others. You can use java to write computer applications that crunch numbers, process words, play games, store data or do any of the thousands of other things computer software can do.
Special programs called applets that can be downloaded from the internet and played safely within a web browser. Java a supports this application and the follow features make it one of the best programming languages.
It is simple and object oriented
It helps to create user friendly interfaces.
It is very dynamic.
It supports multithreading.
It is platform independent
It is highly secure and robust.
It supports internet programming
Java is a programming language originally developed by Sun Microsystems and released in 1995 as a core component of Sun's Java platform. The language derives much of its syntax from C and C++ but has a simpler object model and fewer low-level facilities. Java applications are typically compiled to byte code which can run on any Java virtual machine JVM regardless of computer architecture.
The original and reference implementation Java compilers, virtual machines, and class libraries were developed by Sun from 1995. As of May 2007, in compliance with the specifications of the Java Community Process, Sun made available most of their Java technologies as free software under the GNU General Public License. Others have also developed alternative implementations of these Sun technologies, such as the GNU Compiler for Java and GNU Class path.
The Java language was created by James Gosling in June 1991 for use in a set top box project. The language was initially called Oak, after an oak tree that stood outside Gosling's office - and also went by the name Green - and ended up later being renamed to Java, from a list of random words. Gosling's goals were to implement a virtual machine and a language that had a familiar C/C++ style of notation.
Java Virtual Machine
The heart of the Java Platform is the concept of a "virtual machine" that executes Java byte code programs. This byte code is the same no matter what hardware or operating system the program is running under. There is a JIT compiler within the Java Virtual Machine, or JVM. The JIT compiler translates the Java byte code into native processor instructions at run-time and caches the native code in memory during execution.
The use of byte code as an intermediate language permits Java programs to run on any platform that has a virtual machine available. The use of a JIT compiler means that Java applications, after a short delay during loading and once they have warmed up by being all or mostly JIT-compiled, tend to run about as fast as native programs. Since JRE version 1.2, Sun's JVM implementation has included a just-in-time compiler instead of an interpreter.
Although Java programs are Platform Independent, the codes of the Java Virtual Machine JVM that execute these programs are not. Every Operating System has its own JVM.
Class libraries
In most modern operating systems, a large body of reusable code is provided to simplify the programmer's job. This code is typically provided as a set of dynamically loadable libraries that applications can call at runtime. Because the Java Platform is not dependent on any specific operating system, applications cannot rely on any of the existing libraries. Instead, the Java Platform provides a comprehensive set of standard class libraries, containing much of the same reusable functions commonly found in modern operating systems.
The Java class libraries serve three purposes within the Java Platform. Like other standard code libraries, they provide the programmer a well-known set of functions to perform common tasks, such as maintaining lists of items or performing complex string parsing. In addition, the class libraries provide an abstract interface to tasks that would normally depend heavily on the hardware and operating system. Tasks such as network access and file access are often heavily dependent on the native capabilities of the platform. The Java java.net and java.io libraries implement the required native code internally, then provide a standard interface for the Java applications to perform those tasks. Finally, when some underlying platform does not support all of the features a Java application expects, the class libraries can either emulate those features using whatever is available, or at least provide a consistent way to check for the presence of a specific feature.
Platform independence
One characteristic, platform independence, means that programs written in the Java language must run similarly on any supported hardware/operating-system platform. One should be able to write a program once, compile it once, and run it anywhere.
This is achieved by most Java compilers by compiling the Java language code halfway to Java byte code simplified machine instructions specific to the Java platform. The code is then run on a virtual machine VM, a program written in native code on the host hardware that interprets and executes generic Java byte code. In some JVM versions, byte code can also be compiled to native code, either before or during program execution, resulting in faster execution. Further, standardized libraries are provided to allow access to features of the host machines such as graphics, threading and networking in unified ways. Note that, although there is an explicit compiling stage, at some point, the Java byte code is interpreted or converted to native machine code by the JIT compiler.
The first implementations of the language used an interpreted virtual machine to achieve portability. These implementations produced programs that ran more slowly than programs compiled to native executable, for instance written in C or C++, so the language suffered a reputation for poor performance. More recent JVM implementations produce programs that run significantly faster than before, using multiple techniques.
This technique, known as just-in-time compilation JIT, translates the Java byte code into native code at the time that the program is run, which results in a program that executes faster than interpreted code but also incurs compilation overhead during execution. More sophisticated VMs use dynamic recompilation, in which the VM can analyze the behavior of the running program and selectively recompile and optimize critical parts of the program. Dynamic recompilation can achieve optimizations superior to static compilation because the dynamic compiler can base optimizations on knowledge about the runtime environment and the set of loaded classes, and can identify the hot spots parts of the program, often inner loops, that take up the most execution time. JIT compilation and dynamic recompilation allow Java programs to take advantage of the speed of native code without losing portability.
Another technique, commonly known as static compilation, is to compile directly into native code like a more traditional compiler. Static Java compilers, such as GCJ, translate the Java language code to native object code, removing the intermediate byte code stage. This achieves good performance compared to interpretation, but at the expense of portability; the output of these compilers can only be run on a single architecture. Some see avoiding the VM in this manner as defeating the point of developing in Java; however it can be useful to provide both a generic byte code version, as well as an optimized native code version of an application.
Performance
Java's performance has improved substantially since the early versions, and performance of JIT compilers relative to native compilers has in some tests been shown to be quite similar. The performance of the compilers does not necessarily indicate the performance of the compiled code; only careful testing can reveal the true performance issues in any system.
Java Runtime Environment
The Java Runtime Environment, or JRE, is the software required to run any application deployed on the Java Platform. End-users commonly use a JRE in software packages and Web browser plugins. Sun also distributes a superset of the JRE called the Java 2 SDK more commonly known as the JDK, which includes development tools such as the Java compiler, Javadoc, Jar and debugger.
One of the unique advantages of the concept of a runtime engine is that errors exceptions should not 'crash' the system. Moreover, in runtime engine environments such as Java there exist tools that attach to the runtime engine and every time that an exception of interest occurs they record debugging information that existed in memory at the time the exception was thrown stack and heap values. These Automated Exception Handling tools provide 'root-cause' information for exceptions in Java programs that run in production, testing or development environments.
CHAPTER 6
CONCLUSION& FUTURE WORK
There are many challenges in the speech communications over the Internet such as easy prediction and high bandwidth size. This project proposes a new class of passive traffic analysis attacks to compromise privacy of speech communications. In the existing, the application level traffic analysis attacks are only based on packet size information. The proposed traffic analysis attacks include not only packet size information but also comprise the timing information of the packet. The proposed traffic analysis attacks can detect speakers of encrypted speech communications with high detection rates. In proposed work the hidden markov model is used for detecting the speaker dependency and constant bit ratio is utilized to append the bit to convert the voice file into original voice format. Due to this the bandwidth and network traffic is not reduced. In future work in order to replace the HMM technique the Gaussian Mixture Model for gaining more privacy. Online Conversation also will be included in the future that means the speech will be packetized and encrypted at the time of talking without any manual operation. By utilizing the GMM technique the bandwidth utilization can be highly reduced further noise filtration, speech segmentation can be enhanced to overcome the limitation.
