This document discusses a proposed method for using honeypots and peer-to-peer (P2P) botnets to detect botnets. It begins by introducing the concepts of botnets, honeypots, and P2P botnets. The proposed method has three steps: 1) launching bots into a network to infect honeypots, 2) constructing a P2P botnet to detect and remove infected honeypots, and 3) using the remaining uninfected honeypots to detect the original bots. Simulation results show the number of detected botnets increases as more uninfected honeypots are used. The document concludes by discussing limitations of honeypot detection and the ongoing challenge of detecting botnets.
Guarding Against Large-Scale Scrabble In Social NetworkEditor IJCATR
Generally, the botnet is one of the most dangerous threats in the network. It has number attackers in the network. The
attacker consists of DDOS attack, remote attack, etc., Bots perform perform repetitive tasks automatically or on a schedule over the
internet, tasks that would be too mundane or time-consuming for an actual person. But the botnets have stealthy behavior as they are
very difficult to identify. These botnets have to be identified and the internet have to be protected. Also the the activity of botnets must
be prevented to provide the users, a reliable service. The past of botnet detection has a transaction process which is not secure. A
efficient stastical data classifier is required to train the botent preventions system. To provide the above features clustering based
analysis is done. our approach can detect and profile various P2P applications rather than identifying a specific P2P application.
Anomaly based detection technique is used to obtain this goal.
Nowadays, cyber-attacks from botnets are increasing at a faster rate than any other malware spread. Detecting the botmaster who commands the tasks has become more difficult. Most of the detecting methods are based on the features of any communication protocol or the history of the network traffic. In this paper, a rational approach is brought for the live detection of the botmaster in the internal network. The victim machine monitors its packets and compromises the bots in the network and finds the traces to the botmaster. This approach works independent of the structure of the botnet, and will be a better option for online detection of the botmaster.
Now-a-days, Internet has become an important part of human’s life, a person
can shop, invest, and perform all the banking task online. Almost, all the organizations have
their own website, where customer can perform all the task like shopping, they only have to
provide their credit card details. Online banking and e-commerce organizations have been
experiencing the increase in credit card transaction and other modes of on-line transaction.
Due to this credit card fraud becomes a very popular issue for credit card industry, it causes
many financial losses for customer and also for the organization. Many techniques like
Decision Tree, Neural Networks, Genetic Algorithm based on modern techniques like
Artificial Intelligence, Machine Learning, and Fuzzy Logic have been already developed for
credit card fraud detection. In this paper, an evolutionary Simulated Annealing algorithm is
used to train the Neural Networks for Credit Card fraud detection in real-time scenario.
This paper shows how this technique can be used for credit card fraud detection and
present all the detailed experimental results found when using this technique on real world
financial data (data are taken from UCI repository) to show the effectiveness of this
technique. The algorithm used in this paper are likely beneficial for the organizations and
for individual users in terms of cost and time efficiency. Still there are many cases which are
misclassified i.e. A genuine customer is classified as fraud customer or vise-versa.
A Dynamic Botnet Detection Model based on Behavior Analysisidescitation
Today different types of malware exist in the Internet. Among them one of the
malware is known as botnet which is frequently used for many cyber attacks and crimes in
the Internet. Currently botnets are the main rootcause for several illegal activities like
spamming, DDoS, click fraud etc. Botnets operate under the command and control(C&C)
infrastructure which makes its functioning unique. As long as the Internet exists botnet also
will exist. It can be used to perpetrate many Internet crimes. So fighting against them is a
challenging problem. The P2P-decentralized based botnets are more dangerous than
centralized botnets. In this paper a novel approach for the detection of P2P based botnet is
presented. The proposed approach for the detection of botnet in the network stream
analysis has been done in three phases. The first phase begins with the identification of P2P
node and the second phase deals with the clustering of the suspicious P2P node. Finally
botnet detection procedure has been applied which is based on stability of bots.
Experimental results show that the proposed approach detects more number of bots with
high accuracy.
Public Key Cryptosystem Approach for P2P Botnet Detection and PreventionIJERA Editor
Distributed (P2P) botnets have as of late been received by botmasters for their versatility against take-down
endeavors. Other than being harder to bring down, p2p botnets tend to be stealthier in the way they perform
vindictive exercises, making current discovery approaches ineffectual. In this paper, we simulate our proposal
by detecting a gray hole attack in an Ad Hoc network using NS2.The detected malicious node is listed in a black
hole list and notices all other nodes in the network to stop communicating with them. Our botnet location
framework has been equipped for identifying stealthy P2P botnets (Gray Hole nodes) and can reduce packet loss
caused by malicious nodes and have a better packet delivery ratio (PDR) within less period of time.
Towards botnet detection through features using network traffic classificationIJERA Editor
Botnet are becoming the most significant threat to the internet world. Botnet is the automated process of
attackers that interacts with network traffic and its services. Botnet are automatically updated into the
compromised system to collect the authenticated information. In this paper, we present a model to extract some
features which are helpful to analyze the behaviour of bot members present in the particular network traffic. On
the other hand, various superior methods are evaluated to extract weather network traffic contain bot or not. In
particularly, our evaluation shows that the particular traffic contain any bot member in their communication.
Guarding Against Large-Scale Scrabble In Social NetworkEditor IJCATR
Generally, the botnet is one of the most dangerous threats in the network. It has number attackers in the network. The
attacker consists of DDOS attack, remote attack, etc., Bots perform perform repetitive tasks automatically or on a schedule over the
internet, tasks that would be too mundane or time-consuming for an actual person. But the botnets have stealthy behavior as they are
very difficult to identify. These botnets have to be identified and the internet have to be protected. Also the the activity of botnets must
be prevented to provide the users, a reliable service. The past of botnet detection has a transaction process which is not secure. A
efficient stastical data classifier is required to train the botent preventions system. To provide the above features clustering based
analysis is done. our approach can detect and profile various P2P applications rather than identifying a specific P2P application.
Anomaly based detection technique is used to obtain this goal.
Nowadays, cyber-attacks from botnets are increasing at a faster rate than any other malware spread. Detecting the botmaster who commands the tasks has become more difficult. Most of the detecting methods are based on the features of any communication protocol or the history of the network traffic. In this paper, a rational approach is brought for the live detection of the botmaster in the internal network. The victim machine monitors its packets and compromises the bots in the network and finds the traces to the botmaster. This approach works independent of the structure of the botnet, and will be a better option for online detection of the botmaster.
Now-a-days, Internet has become an important part of human’s life, a person
can shop, invest, and perform all the banking task online. Almost, all the organizations have
their own website, where customer can perform all the task like shopping, they only have to
provide their credit card details. Online banking and e-commerce organizations have been
experiencing the increase in credit card transaction and other modes of on-line transaction.
Due to this credit card fraud becomes a very popular issue for credit card industry, it causes
many financial losses for customer and also for the organization. Many techniques like
Decision Tree, Neural Networks, Genetic Algorithm based on modern techniques like
Artificial Intelligence, Machine Learning, and Fuzzy Logic have been already developed for
credit card fraud detection. In this paper, an evolutionary Simulated Annealing algorithm is
used to train the Neural Networks for Credit Card fraud detection in real-time scenario.
This paper shows how this technique can be used for credit card fraud detection and
present all the detailed experimental results found when using this technique on real world
financial data (data are taken from UCI repository) to show the effectiveness of this
technique. The algorithm used in this paper are likely beneficial for the organizations and
for individual users in terms of cost and time efficiency. Still there are many cases which are
misclassified i.e. A genuine customer is classified as fraud customer or vise-versa.
A Dynamic Botnet Detection Model based on Behavior Analysisidescitation
Today different types of malware exist in the Internet. Among them one of the
malware is known as botnet which is frequently used for many cyber attacks and crimes in
the Internet. Currently botnets are the main rootcause for several illegal activities like
spamming, DDoS, click fraud etc. Botnets operate under the command and control(C&C)
infrastructure which makes its functioning unique. As long as the Internet exists botnet also
will exist. It can be used to perpetrate many Internet crimes. So fighting against them is a
challenging problem. The P2P-decentralized based botnets are more dangerous than
centralized botnets. In this paper a novel approach for the detection of P2P based botnet is
presented. The proposed approach for the detection of botnet in the network stream
analysis has been done in three phases. The first phase begins with the identification of P2P
node and the second phase deals with the clustering of the suspicious P2P node. Finally
botnet detection procedure has been applied which is based on stability of bots.
Experimental results show that the proposed approach detects more number of bots with
high accuracy.
Public Key Cryptosystem Approach for P2P Botnet Detection and PreventionIJERA Editor
Distributed (P2P) botnets have as of late been received by botmasters for their versatility against take-down
endeavors. Other than being harder to bring down, p2p botnets tend to be stealthier in the way they perform
vindictive exercises, making current discovery approaches ineffectual. In this paper, we simulate our proposal
by detecting a gray hole attack in an Ad Hoc network using NS2.The detected malicious node is listed in a black
hole list and notices all other nodes in the network to stop communicating with them. Our botnet location
framework has been equipped for identifying stealthy P2P botnets (Gray Hole nodes) and can reduce packet loss
caused by malicious nodes and have a better packet delivery ratio (PDR) within less period of time.
Towards botnet detection through features using network traffic classificationIJERA Editor
Botnet are becoming the most significant threat to the internet world. Botnet is the automated process of
attackers that interacts with network traffic and its services. Botnet are automatically updated into the
compromised system to collect the authenticated information. In this paper, we present a model to extract some
features which are helpful to analyze the behaviour of bot members present in the particular network traffic. On
the other hand, various superior methods are evaluated to extract weather network traffic contain bot or not. In
particularly, our evaluation shows that the particular traffic contain any bot member in their communication.
Lightweight C&C based botnet detection using Aho-Corasick NFAIJNSA Journal
Botnet distinguishes itself from the previous malware by having the characteristics of a C&C channel, using which a Botmaster can control the constituents of the botnet. Even though protocols like IRC, HTTP and DNS are exploited to incorporate C&C channels, previous analysis have shown that the majority of the botnets are usually based on IRC. Consequently in this paper the Aho-Corasick NFA based detection is proposed to detect the C&C instructions which is exchanged in IRC run botnets. However the ability to detect botnet is limited to the existing bot commands. Therefore a counting process which analyses every IRC messages is introduced to detect the existence of malicious codes. This detection method and various existing methods have been evaluated using real-world network traces. The results show that the proposed C&C Instruction based IRC detection method can detect real-world botnets with high accuracy.
A Botnet Detecting Infrastructure Using a Beneficial BotnetTakashi Yamanoue
A beneficial botnet, which tries to cope with technology of malicious botnets such as peer to peer (P2P) networking and Domain Generation Algorithm (DGA), is discussed. In order to cope with such botnets’ technology, we are developing a beneficial botnet as an anti-bot measure, using our previous beneficial bot. The beneficial botnet is a group of beneficial bots. The P2P communication of malicious botnet is hard to detect by a single Intrusion Detection System (IDS). Our beneficial botnet has the ability to detect P2P communication, using collaboration of our beneficial bots. The beneficial bot could detect communication of the pseudo botnet which mimics malicious botnet communication. Our beneficial botnet may also detect communication using DGA. Furthermore, our beneficial botnet has ability to cope with new technology of new botnets, because our beneficial botnet has the ability to evolve, as same as malicious botnets.
Botnet detection using ensemble classifiers of network flow IJECEIAES
Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence.
Classifying IoT malware delivery patterns for attack detectionFabrizio Farinacci
The Internet of Things (IoT) is one of the most promising technology vision of the most recent years. It will really impact the way economy, industry and govern- ments perform their operation, with all the benefits coming from the increase of connectivity among devices pushed to the limit. Unfortunately, the availability of countless number of Internet connected devices having a small but usable computing power is the Holy Grail of cybercriminals, especially if (like in the most of the cases) those devices are also particularly vulnerable to attacks. The enormous growth of IoT malware spotted in the wild is the proof that shows how the situation is already dramatic and that it is worsening everyday more and more. Designing strategies to defend, detect and mitigate those attacks it is critical to secure the IoT environment and to realize the vision of the Internet of Things. In this work, an approach for recognizing and classifying the attacks targeting IoT devices is presented. The approach leverages the fact that attacks and particularly malware (and even more in the case of the IoT) are characterized by extensive code reuse, enabling the identification of attack patterns characterizing the device compromise stage. Furthermore, the definition of those patterns enables the profiling, grouping and classification of the attacks in an effective and robust way against the small changes performed by attackers to evade signature-based approaches.
Optimal remote access trojans detection based on network behaviorIJECEIAES
RAT is one of the most infected malware in the hyper-connected world. Data is being leaked or disclosed every day because new remote access Trojans are emerging and they are used to steal confidential data from target hosts. Network behavior-based detection has been used to provide an effective detection model for Remote Access Trojans. However, there is still short comings: to detect as early as possible, some False Negative Rate and accuracy that may vary depending on ratio of normal and malicious RAT sessions. As typical network contains large amount of normal traffic and small amount of malicious traffic, the detection model was built based on the different ratio of normal and malicious sessions in previous works. At that time false negative rate is less than 2%, and it varies depending on different ratio of normal and malicious instances. An unbalanced dataset will bias the prediction model towards the more common class. In this paper, each RAT is run many times in order to capture variant behavior of a Remote Access Trojan in the early stage, and balanced instances of normal applications and Remote Access Trojans are used for detection model. Our approach achieves 99 % accuracy and 0.3% False Negative Rate by Random Forest Algorithm.
Tor Hidden Services enables server anonymity, which may lead potentially to illegal and criminal activities. In this presentation, state-of-the-art literature method against hidden service anonymity are presented to overcome this issue.
Paper Presentation - "Your Botnet is my Botnet : Analysis of a Botnet Takeover"Jishnu Pradeep
Presentation based on Paper titled: "Your botnet is my botnet: Analysis of a botnet takeover". The original authors are Brett Stone-Gross, Marco Cova, Lorenzo Cavallaro, Bob Gilbert, Martin Szydlowski,
Richard Kemmerer, Christopher Kruegel, and Giovanni Vigna.
Internet threats have increased manifold with the
arrival of botnets. Many organizations worldwide and the
social networks have been affected by botnets. Numerous
researches have been carried to understand the concept of
bots, C&C channels, botnet and botmasters. These botnets
have been able to update itself regularly which makes them
very difficult to be detected. The purpose of this paper is to
understand the of behavior of botnets and its affect on the
virtual world. The paper has also analyzed the types of
botnets, lifecycle and elements of botnets.
Botminer Clustering Analysis Of Network Traffic For Protocol And Structure...ncct
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
The objective of this is to develop a Fuzzy aided Application layer Semantic Intrusion Detection System (FASIDS) which works in the application layer of the network stack. FASIDS consist of semantic IDS and Fuzzy based IDS. Rule based IDS looks for the specific pattern which is defined as malicious. A non-intrusive regular pattern can be malicious if it occurs several times with a short time interval. For detecting such malicious activities, FASIDS is proposed in this paper. At application layer, HTTP traffic’s header and payload are analyzed for possible intrusion. In the proposed misuse detection module, the semantic intrusion detection system works on the basis of rules that define various application layer misuses that are found in the network. An attack identified by the IDS is based on a corresponding rule in the rule-base. An event that doesn’t make a ‘hit’ on the rule-base is given to a Fuzzy Intrusion Detection System (FIDS) for further analysis.
Detection of the botnets’ low-rate DDoS attacks based on self-similarity IJECEIAES
An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system–BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.
Face expressions, facial features, kinect sensor, face tracking SDK, neural n...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Lightweight C&C based botnet detection using Aho-Corasick NFAIJNSA Journal
Botnet distinguishes itself from the previous malware by having the characteristics of a C&C channel, using which a Botmaster can control the constituents of the botnet. Even though protocols like IRC, HTTP and DNS are exploited to incorporate C&C channels, previous analysis have shown that the majority of the botnets are usually based on IRC. Consequently in this paper the Aho-Corasick NFA based detection is proposed to detect the C&C instructions which is exchanged in IRC run botnets. However the ability to detect botnet is limited to the existing bot commands. Therefore a counting process which analyses every IRC messages is introduced to detect the existence of malicious codes. This detection method and various existing methods have been evaluated using real-world network traces. The results show that the proposed C&C Instruction based IRC detection method can detect real-world botnets with high accuracy.
A Botnet Detecting Infrastructure Using a Beneficial BotnetTakashi Yamanoue
A beneficial botnet, which tries to cope with technology of malicious botnets such as peer to peer (P2P) networking and Domain Generation Algorithm (DGA), is discussed. In order to cope with such botnets’ technology, we are developing a beneficial botnet as an anti-bot measure, using our previous beneficial bot. The beneficial botnet is a group of beneficial bots. The P2P communication of malicious botnet is hard to detect by a single Intrusion Detection System (IDS). Our beneficial botnet has the ability to detect P2P communication, using collaboration of our beneficial bots. The beneficial bot could detect communication of the pseudo botnet which mimics malicious botnet communication. Our beneficial botnet may also detect communication using DGA. Furthermore, our beneficial botnet has ability to cope with new technology of new botnets, because our beneficial botnet has the ability to evolve, as same as malicious botnets.
Botnet detection using ensemble classifiers of network flow IJECEIAES
Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence.
Classifying IoT malware delivery patterns for attack detectionFabrizio Farinacci
The Internet of Things (IoT) is one of the most promising technology vision of the most recent years. It will really impact the way economy, industry and govern- ments perform their operation, with all the benefits coming from the increase of connectivity among devices pushed to the limit. Unfortunately, the availability of countless number of Internet connected devices having a small but usable computing power is the Holy Grail of cybercriminals, especially if (like in the most of the cases) those devices are also particularly vulnerable to attacks. The enormous growth of IoT malware spotted in the wild is the proof that shows how the situation is already dramatic and that it is worsening everyday more and more. Designing strategies to defend, detect and mitigate those attacks it is critical to secure the IoT environment and to realize the vision of the Internet of Things. In this work, an approach for recognizing and classifying the attacks targeting IoT devices is presented. The approach leverages the fact that attacks and particularly malware (and even more in the case of the IoT) are characterized by extensive code reuse, enabling the identification of attack patterns characterizing the device compromise stage. Furthermore, the definition of those patterns enables the profiling, grouping and classification of the attacks in an effective and robust way against the small changes performed by attackers to evade signature-based approaches.
Optimal remote access trojans detection based on network behaviorIJECEIAES
RAT is one of the most infected malware in the hyper-connected world. Data is being leaked or disclosed every day because new remote access Trojans are emerging and they are used to steal confidential data from target hosts. Network behavior-based detection has been used to provide an effective detection model for Remote Access Trojans. However, there is still short comings: to detect as early as possible, some False Negative Rate and accuracy that may vary depending on ratio of normal and malicious RAT sessions. As typical network contains large amount of normal traffic and small amount of malicious traffic, the detection model was built based on the different ratio of normal and malicious sessions in previous works. At that time false negative rate is less than 2%, and it varies depending on different ratio of normal and malicious instances. An unbalanced dataset will bias the prediction model towards the more common class. In this paper, each RAT is run many times in order to capture variant behavior of a Remote Access Trojan in the early stage, and balanced instances of normal applications and Remote Access Trojans are used for detection model. Our approach achieves 99 % accuracy and 0.3% False Negative Rate by Random Forest Algorithm.
Tor Hidden Services enables server anonymity, which may lead potentially to illegal and criminal activities. In this presentation, state-of-the-art literature method against hidden service anonymity are presented to overcome this issue.
Paper Presentation - "Your Botnet is my Botnet : Analysis of a Botnet Takeover"Jishnu Pradeep
Presentation based on Paper titled: "Your botnet is my botnet: Analysis of a botnet takeover". The original authors are Brett Stone-Gross, Marco Cova, Lorenzo Cavallaro, Bob Gilbert, Martin Szydlowski,
Richard Kemmerer, Christopher Kruegel, and Giovanni Vigna.
Internet threats have increased manifold with the
arrival of botnets. Many organizations worldwide and the
social networks have been affected by botnets. Numerous
researches have been carried to understand the concept of
bots, C&C channels, botnet and botmasters. These botnets
have been able to update itself regularly which makes them
very difficult to be detected. The purpose of this paper is to
understand the of behavior of botnets and its affect on the
virtual world. The paper has also analyzed the types of
botnets, lifecycle and elements of botnets.
Botminer Clustering Analysis Of Network Traffic For Protocol And Structure...ncct
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
The objective of this is to develop a Fuzzy aided Application layer Semantic Intrusion Detection System (FASIDS) which works in the application layer of the network stack. FASIDS consist of semantic IDS and Fuzzy based IDS. Rule based IDS looks for the specific pattern which is defined as malicious. A non-intrusive regular pattern can be malicious if it occurs several times with a short time interval. For detecting such malicious activities, FASIDS is proposed in this paper. At application layer, HTTP traffic’s header and payload are analyzed for possible intrusion. In the proposed misuse detection module, the semantic intrusion detection system works on the basis of rules that define various application layer misuses that are found in the network. An attack identified by the IDS is based on a corresponding rule in the rule-base. An event that doesn’t make a ‘hit’ on the rule-base is given to a Fuzzy Intrusion Detection System (FIDS) for further analysis.
Detection of the botnets’ low-rate DDoS attacks based on self-similarity IJECEIAES
An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system–BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.
Face expressions, facial features, kinect sensor, face tracking SDK, neural n...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Presentatie van Peter Heine, deeljezorg.nl gehouden tijdens het NPCF congres 'Met toetsenbord en muis zelf je zorg regelen' in de sessie 'eHealth voor Dummies'
quantitative structure activity relationship studies of anti proliferative ac...IJEAB
Many studies have focused on indole derivatives mainly their antiproliferative effect. The therapeutic effect of this group of molecule is very important. Quantitative structure–activity relationships (QSAR) have been applied for development relationships between physicochemical properties and their biological activities. A series of 30 molecules derived from indole is based on the quantitative structure-activity relationship (QSAR). This study was carried out using the principal component analysis (PCA) method, the multiple linear regression method (MLR), non-linear regression (RNLM), the artificial neural network (ANN) and it was validated using cross validation analysis (CV). We accordingly propose a quantitative model and we try to interpret the activity of the compounds relying on the multivariate statistical analyses. A theoretical study of series was studied using density functional theory (DFT) calculations at B3LYP/6-31G(d) level of theory for employing to calculate electronic descriptors when, the topological descriptors were computed with ACD/ChemSketch and ChemDraw 8.0 programs. The best QSAR model was found in agreement with the experimental by ANN (R = 0,99).
Media and Information Literacy (MIL) - 9. Current and Future Trends in Media ...Arniel Ping
Learning Competencies
Students will be able to…
1. describe massive open on-line (MIL11/12CFT-IIIi-26)
2. evaluate current trends in media and information and how it will affect/how they affect individuals and the society as a whole (MIL11/12CFT-IIIi-26)
3. predict future media innovation (MIL11/12CFT-IIIi-27)
4. synthesize the overall knowledge about media and information with skills for producing a prototype of what the learners think is a future media innovation (MIL11/12CFT-IIIi-28)
I- Current and Future Trends in Media and Cummunication
A. Ubiquitous Learning
B. Massive Open Online Course
C. Wearable Technology
D. 3D Environment
II- Performance Task: Project
A. Prototyping for Empathy
Botnets are emerging as the most serious threat against cyber-security as they provide a distributed platform for several illegal activities such as launching distributed denial of service attacks against critical targets, malware dissemination, phishing, and click fraud. The defining characteristic of botnets is the use of command and control channels through which they can be updated and directed. Recently, botnet detection has been an interesting research topic related to cyber-threat and cyber-crime prevention. This paper is a survey of botnet and botnet detection. The survey clarifies botnet phenomenon and discusses botnet detection techniques. This survey classifies botnet detection techniques into four classes: signature-based, anomaly-based, DNS-based, and mining-base.
Lab3/code.c
#include <stdio.h>
#include <stdlib.h>
#include <windows.h>
#include <wchar.h>
void dga(int year, int month, int day)
{
char alphabets[]={'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'};
printf("domain is syn-%c%c%c.com\n",alphabets[year-2000],alphabets[month*2],alphabets[10]);
}
int main()
{
char test[]="Simple DGA example for CIT406";
SYSTEMTIME st;
GetLocalTime(&st);
dga(st.wYear, st.wMonth, st.wDay);
return 0;
}
Lab3/Lab 3.docx
Lab 3 Domain Generation Algorithm Reverse Engineering
Scenario:
The university has caught a malware operator on campus and found a domain generating algorithm (DGA) on the campus-owned computer which this person was using. The campus has asked you to figur out how it works so that they can potentially use the command and control server for research like is described here in the attached pdf. (PDF attached it the folder called stone-gross)
The file you will need to figure out is here (this is written in the computer language C, not malware)
(this file is attached in the folder called code.c)
One way to find out how something works is using IDA in Kali Linux information about kali can be found here: kali.org. Some basic stuff about IDA can be found here: http://securityxploded.com/reversing-basics-ida-pro.php (Links to an external site.). These resources will not be sufficient to get you all the way through the lab, because they were not designed as a step by step walkthrough of the lab, you will need to take the knowledge from these resources and others that you find to complete the lab.
I attached the exe file. You have to unzip the file. The exe file has to be run from command line (in a SAFE environment i.e. a virtual machine).
When reverse engineering the exe file, you should be looking at _dga/dga function. You can use IDA Pro.
For this lab, you will need to: find out what you can from the files attached, following a lab report format outlined in lab 2. Take a lot of screenshots.
Lab3/Lab 4.docx
You will need to download the Kali 2.0 iso from kali.org and create a virtual machine in Virtualbox. Create a virtual machine with INTERNAL networking (tHIS IS IMPORTANT for the security of your network) using the Kali live iso.
Then you will need to look on the Penetration Tester Academy (or pentester academy) for an iso with vulnerable web applications OR find DVL (Damn Vulnerable Linux) from distrowatch.com. With this iso, create a second VM with INTERNAL networking ONLY (for the security of your network).
- Take screenshots (with a notepad file open in the background with your name).
- Exploit the vulnerable VM (whether you are using the vulnerable web applications or a vulnerable virtual machine) in at least 2 different ways: 1 should be remote code execution or Cross-Site Scripting (also CSS or XSS).
Lab3/stone-gross.pdf
Your Botnet is My Botnet: Analysis of a Botnet ...
lab3/cdga.zip
lab3/code.c
#include <stdio.h>
#include <stdlib.h>
#include <windows.h>
#include <wchar.h>
void dga(int year, int month, int day)
{
char alphabets[]={'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'};
printf("domain is syn-%c%c%c.com\n",alphabets[year-2000],alphabets[month*2],alphabets[10]);
}
int main()
{
char test[]="Simple DGA example for CIT406";
SYSTEMTIME st;
GetLocalTime(&st);
dga(st.wYear, st.wMonth, st.wDay);
return 0;
}
lab3/Domain Generation Algorithm Reverse Engineering.docx
Scenario:
The university has caught a malware operator on campus and found a domain generating algorithm (DGA) on the campus-owned computer which this person was using. The campus has asked you to figure out how it works so that they can potentially use the command and control server for research like is described here in the attached pdf. (Attached in folder)
The file you will need to figure out is here (this is written in the computer language C, not malware) (this file is attached in folder).
One way to find out how something works is using IDA in Kali Linux information about kali can be found here: kali.org. Some basic stuff about IDA can be found here:
http://securityxploded.com/reversing-basics-ida-pro.php (Links to an external site.). These resources will not be sufficient to get you all the way through the lab, because they were not designed as a step by step walkthrough of the lab, you will need to take the knowledge from these resources and others that you find to complete the lab.
I attached the exe file. You have to unzip the file. The exe file has to be run from command line (in a SAFE environment i.e. a virtual machine).
When reverse engineering the exe file, you should be looking at _dga/dga function. You can use IDA Pro. (The .exe file is attached in folder)
For this lab, you will need to: find out what you can from the files attached, following a lab report format outlined which is abstract, discussion, and conclusion. Take a lot of screenshots
lab3/stone-gross+cova+cavallaro+gilbert+szydlowski+kemmerer+kruegel+vigna+yourBotnetIsMyBotnet.pdf
Your Botnet is My Botnet: Analysis of a Botnet Takeover
Brett Stone-Gross, Marco Cova, Lorenzo Cavallaro, Bob Gilbert, Martin Szydlowski,
Richard Kemmerer, Christopher Kruegel, and Giovanni Vigna
Department of Computer Science, University of California, Santa Barbara
{bstone,marco,sullivan,rgilbert,msz,kemm,chris,vigna}@cs.ucsb.edu
ABSTRACT
Botnets, networks of malware-infected machines that are controlled
by an adversary, are the root cause of a large number of security
problems on the Internet. A particularly sophisticated and insidi-
ous type of bot is Torpig, a malware program that is designed to
harvest sensitive information (such as bank account and credit card
data) from its victims. In this paper, we report on our effo ...
How To Protect Your Website From Bot Attacks is a one-hour continuing education course. After successfully completing the course and final exam, you will be awarded a certificate of completion that you can use towards fulfilling your continuing education requirements.
Botnet Detection in Online-social NetworkRubal Sagwal
Botnet, Bot master, Command and Control Server, States for Bots, Types of attacks, most wanted bots, Botnet life cycle, botnet topology, Social botnet.
Botnets became one of the most observed and dangerous threat in the malware landscape. For this reason, it is crucial to design
techniques able to detect bot-infected hosts at dierent levels (enterprise, ISP, etc.). In this presentation, a possible taxonomy showing how different approaches to botnet detection can be categorized, enlightening the different advantages and disadvantages of the design choices. In addition, possible future research directions are presented.
Botnet Attacks How They Work and How to Defend Against Them.pdfuzair
What is a Botnet?
How Botnets Work
Types of Botnets
1. IRC Botnets
2. HTTP-Based Botnets
3. P2P Botnets
4. Zombie Botnets
Common Uses of Botnets
How to Detect a Botnet
How to Defend Against Botnets
1. Keep Your Software Up-to-Date
2. Install Antivirus and Anti-Malware Software
3. Use Strong Passwords and Two-Factor Authentication
4. Educate Yourself and Your Staff
5. Use Network Segmentation and Firewall Rules
6. Monitor Your Network for Unusual Activity
Conclusion
FAQs
Table of Contents
Introduction
What is a Botnet?
How Botnets Work
Types of Botnets
IRC Botnets
HTTP-Based Botnets
P2P Botnets
Zombie Botnets
Common Uses of Botnets
How to Detect a Botnet
How to Defend Against Botnets
Keep Your Software Up-to-Date
Install Antivirus and Anti-Malware Software
Use Strong Passwords and Two-Factor Authentication
Educate Yourself and Your Staff
Use Network Segmentation and Firewall Rules
Monitor Your Network for Unusual Activity
Conclusion
FAQs
Introduction
Botnets are networks of infected computers, servers, and other devices that are controlled by cybercriminals to carry out a variety of malicious activities. These activities can range from sending spam emails and launching DDoS attacks to stealing sensitive data and spreading malware.
Botnets are highly organized and can consist of hundreds or even thousands of infected devices. They are often used to launch attacks on large organizations, but individuals can also be targeted.
In this article, we will look at how botnets work, the different types of botnets, and what you can do to defend against them.
What is a Botnet?
A botnet is a network of computers, servers, and other internet-connected devices that have been infected with malware. Once infected, these devices can be controlled by the botnet operator, who can use them to carry out a variety of malicious activities.
Botnets are created using a variety of techniques, including exploiting security vulnerabilities in software and tricking users into downloading malware.
How Botnets Work
Botnets are controlled by a command and control (C&C) server, which is used by the botnet operator to send instructions to the infected devices. These instructions can range from sending spam emails to launching DDoS attacks on a target.
The infected devices in a botnet are known as bots, zombies, or drones. These devices are typically compromised without the knowledge of the owner and can be controlled remotely by the botnet operator.
Botnets can also use a peer-to-peer (P2P) architecture, where infected devices communicate with each other instead of relying on a central C&C server. TWhat is a Botnet?
How Botnets Work
Types of Botnets
1. IRC Botnets
2. HTTP-Based Botnets
3. P2P Botnets
4. Zombie Botnets
Common Uses of Botnets
How to Detect a Botnet
How to Defend Against Botnets
1. Keep Your Software Up-to-Date
2. Install Antivirus and Anti-Malware Software
3. Use Strong Passwords and Two-Factor Authentication
4. Educate Yourself and Your Staff
5. Use Network S
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Detection of Botnets using Honeypots and P2P Botnets
1. Rajab Challoo & Raghavendra Kotapalli
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 496
Detection of Botnets Using Honeypots and P2P Botnets
Rajab Challoo kfrc000@tamuk.edu
Dept. of Electrical Engineering & Computer Science
Texas A&M University Kingsville
Kingsville, 78363-8202, USA
Raghavendra Kotapalli raghavsan@gmail.com
Dept. of Electrical Engineering & Computer Science
Texas A&M University Kingsville
Kingsville, 78363-8202, USA
Abstract
A “botnet” is a group of compromised computers connected to a network, which can be used for
both recognition and illicit financial gain, and it is controlled by an attacker (bot-herder). One of
the counter measures proposed in recent developments is the “Honeypot”. The attacker who
would be aware of the Honeypot, would take adequate steps to maintain the botnet and hence
attack the Honeypot (Infected Honeypot). In this paper we propose a method to remove the
infected Honeypot by constructing a peer-to-peer structured botnet which would detect the
uninfected Honeypot and use it to detect botnets originally used by the attacker. Our simulation
results show that our method is very effective and can detect the botnets that are intended to
malign the network.
Keywords: Peer-to-peer network, Botnet, Honeypot, Hijacking.
1. INTRODUCTION
The Increase in the Internet malware in the recent attacks have attracted considerable amount of
attraction towards botnets. Some of them include Email spamming, Key logging, click fraud and
traffic sniffing [1]. Recently detected dangerous botnets include Mariposa (2008), officla (2009)
and TDSS (2010). The scatter attacks done by the bot controllers using a program called bot
which communicates with other botnets and receive the commands from Command and Control
servers [3].
As the traditional botnets, which are designed to operate from a central source (bot-attackers
machine) which can be shutdown if the source is pin-pointed by the security agencies, bot
masters use or resort to peer to peer (P2P) botnets which do not have a centralized source and
can grow at an alarming speed. For example, botnet Oficla can spam up to 3.6 billion targets per
day [4].
In this paper we show how the use of a combination of Honeypots and Peer to Peer botnet to
defend the attacks from other botnets. In order to improve the efficacy in defending against such
malicious attacks, one needs to analyze the botnets from a bot-attackers perspective. This would
require a study of basic structure of botnet and the network. The antivirus approach, of signature
based detection of removing one bot or virus at a time works at host level but when bot-attackers
use polymorphic methods creating new instances using the botcodes, evasion from antivirus
becomes complicated. Security experts monitor Command and Control (C&C) traffic so as to
detect an entire network which is infected, this is done to extenuate the botnet problem on a large
scale by identifying the C&C channel[5]. Once a C&C channel is identified by the defenders,
entire botnet could be captured by the defenders[3]. After botnet is captured, botmasters move to
an advanced technique.
2. Rajab Challoo & Raghavendra Kotapalli
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 497
2. BACKGROUND
To mitigate the botnet problem, the command and control mechanism has been under study
which determines the structure of C&C botnets that can monitor, hijack and shutdown the
network. Defenders can however shutdown the entire C&C channel and prevent the attack [5]. In
P2P botnets there is no central point for controlling the botnets. The servant bots act as client and
servers [6], and accept both incoming and outgoing connections whereas the client bots do not
accept incoming connections. Servant bots alone are added to the peer-lists. All bots including
both client and server bots contact the servant bots to retrieve the commands [4].
2.1 Types of Botnets
Bots are basically classified into three types based on botnet topologies:
centralized, peer to peer (P2P) and random .
As described earlier, centralized bot has a point of control which shuts down the entire botnet if
affected. In random botnet, one bot knows no more than the other [7]. This type of botnet has no
guarantee of delivering what is required, hence the topology of random botnet is not discussed in
this paper.
Peer-to-peer botnet has no central point of control and can be used by botmasters (if not in use
already). Let us consider the P2P botnet constraints from a botmasters perspective [3].
1) Generate a botnet that is capable of controlling remaining bots in the network, even after
considerable portion of botnet population has been removed by defenders.
2) Prevent significant exposure of the network topology when some bots are captured by
defenders.
3) Monitor and obtain the complete information of a botnet by its botmaster with ease.
4) Prevent (or make it harder for) defenders from detecting bots via their
communication traffic patterns.
2.2 Monitoring Botnets
Currently, there are two techniques to monitor botnets
(1) Allow Honeypots to be compromised by the botnet [2, 8], behave as normal ‘bot” in the
botnet, and these Honeypot spies provide all the required information to monitor botnet
activities [2].
(2) To hijack the bot controllers to monitor Command and Control
communications in Botnets.
The architecture of a P2P botnet is shown in Figure 1 [2].
FIGURE 1: C&C Architecture of a P2P Botnet
3. Rajab Challoo & Raghavendra Kotapalli
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 498
3. PROPOSED APPROACH
Command and Control botnets, P2P botnets and Honeypots (Honeynets) are used in our
approach. Consider bots A, B, C, D and E that are introduced into the network as shown in Figure
2. Honeypots are denoted as H, IH denotes the Infected Honeypot from botnets either C&C or
P2P or both.
FIGURE 2: Block diagram (Using P2P botnets to detect Honeypots).
Proposed method has three steps. Explained as follows:
• Bots A, B, C, D, and E are launched into the network creating a Honeypot-aware attack,
• Bots infect the Honeypots in the network, thereby leaving infected Honeypots (IH) and
uninfected Honeypots (H) in the cluster.
• Third step involves removing the Infected Honeypots (IH) using P2P botnet. Hence,
uninfected Honeypots (H) can now be used in detecting bots A to E.
The P2P botnet which is constructed using peer list updating procedure, can be constructed in
two parts,
The first part consists of a host which is vulnerable and later decides whether this is Honeypot or
not, the second part contains the block of information and the authorization component allowing
the infected host to join in the botnet.
Another honeypot-based monitoring occurrence happens during peer-list updating procedure.
First, defenders could let their honeypot bots claim to be servant bots in peer-list updating. By
doing this, these honeypots will be connected by many bots in the botnet, and hence, defenders
are able to monitor a large fraction of the botnet. Second, during peer-list updating, each
honeypot bot could get a fresh peer-list, which means the number of bots revealed to each
honeypot could be doubled.
A honeypot could be configured to route all its outgoing traffic to other honeypots; at the same
time, the trapped malicious code still believes that it has contacted some real machines. The P2P
botnet constructed as introduced above is easy for attackers to control when facing monitoring
and defense from security defenders. First, an attacker can easily learn how many zombie
machines have been collected in the botnet and their IP addresses. The attacker can connect to
several known infected computers, asking them to issue a command to let all bots sending a
specific service request to the attacker’s sensor. On the other hand, security professionals cannot
use this technique for monitoring, even if they know how to send such a command, due to their
liability constraint. Second, an attacker can randomly choose any one or several bots to infill
commands into the botnet—it is very hard for security defenders to cut off the control channel
unless they hijack the botnet and take control of it by themselves. Such an active defense
requires security professionals to issue commands to the botnet and update bot code on all
(potentially hundreds or even thousands) compromised computers, which clearly puts a heavy
A B C D E
H IH H IH H
P2P
4. Rajab Challoo & Raghavendra Kotapalli
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 499
liability burden on security professionals. Third, suppose security professionals remove many
infected computers in a botnet. The attacker still has control over the remaining P2P botnet, even
if the remaining botnet is broken into many separated smaller ones.
Security defenders could also try to distinguish which outgoing traffic is for honeypot detection
and which outgoing traffic is for a real attack. If this could be done, then honeypots could be
configured to allow the honeypot-detection traffic to be sent while blocking all other malicious
traffic. For this purpose, security defenders will need to conduct more research on practically
implementing automatic binary code analysis in honeypots. Internet security attack and defense
is an endless war. From the attackers’ perspective, there is a trade-off between detecting
honeypots in their botnets and avoiding bot removal by security professionals. If an attacker
conducts honeypot-aware test on a botnet frequently, honeypots in the botnet can be detected
and removed quickly. But at the same time, the bots in the botnet will generate more outgoing
traffic, and hence, they have more chance to be detected and removed by their users or security
staff. In the end, we should emphasize that even if attackers can successfully detect and remove
honeypots based on the methodology presented in the paper, there is still significant value in
honeypot research and deployment for detecting the infection vector and the source of attacks. It
may not be possible for honeypots to join the botnet, but the security hole used to facilitate the
infection can be quickly discovered and patched.
4.0 DEFENSES AGAINST BOTNETS
Defense from Botnets can be divided to: prevention, detection and removal. In this section we
provide information on how the user’s system can be protected from botnet attacks. The botnet
detected in the system must be removed immediately, otherwise it might cause other problems
such as slow-down the performance, loss of data and leaking of information to the web.
Applying a patch if any damage occurs, is out of the context of this method. This method can
recognize (fingerprint) botnets from the traffic and block the traffic, both upstream and
downstream. The role of Honeypots here is to behave like servant bots to the botnets that intrude
into the network. The moment the Honeypot bot is included into the botnet architecture, the
monitoring of botnet activity is initiated by the defenders. The defenders can get many spying
botnets into their hands so that they can monitor the commands given by the botmasters or send
fake commands to the botmasters, leading to a trap since a remote code authentication (the
problem of identifying a remote code program) [3] cannot distinguish the honeypot bots from the
botnets from the botmaster’s point of view.
4.1 Simulation and Analysis Using P2P botnets and Multiple Honeypots
Multiple Honeypots are used in synchronization with P2P botnets. It should be noted that there is
a limitation of how many honeypots can be used in the cluster for efficient monitoring and
detection. The number can be calculated based on an average of 50 to 100 simulation. The
Simulation results are shown in Figure 3.
An analytical model can be derived by estimating the mean value of tracked bots, which is
denoted by T [Btracked]. There are h number of uninfected honeypots joining the cluster before the
peer-list is updated [1]. Let the size of the peer-list be P, the final botnet has I number of botnets,
and the number of servant bots used in peer-list updating procedure is Q.
Since the botnet has I bots, the average number of tracked bots can be calculated by
T [Btracked] = I
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International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 500
FIGURE 3: Simulation results for botnets tracked, total number of honeypots and uninfected honeypots
In Figure 3, number of Honeypots (H+IH) versus number of botnets tracked represented in the
green curve and the number of uninfected honeypots denoted by H versus number of botnets
tracked is represented in black curve. The number of botnets tracked versus the uninfected
honeypots, H represented in red curve shows a better performance in detection after botnets are
tracked.
4.2 Discussion
From the simulation and the above description on defenses against botnets, we can see that
Honeypot based detection plays a vital role in the detection of botnets. The use of a robust P2P
botnet to filter the infected honeypots from the network adds more to the defenders advantage as
shown in Figure 3. In the future, Botmasters could design advanced ways to detect the honeypot
defense system which could include fingerprinting (recognition). Attackers could also exploit the
legal and ethical constraints held by the defenders [2]. The proposed method works owing to
remote code authentication [3] which helps in not distinguishing the botnet from the honeypot bot.
In the future, if remote authentication method is compromised then the war between botmasters
and security community would intensify. The research done until now in botnets shows “the
worms” and botnets in the internet can be monitored but it gets harder to stop the attacks even
after the presence of the threat is known, i.e. it should be detected without inflicting any damage
to the data. Ethical and legal reasons in the prevention of botnet attacks turn out to be resource
consuming [1]. The other methods which involve detection of botnets without Honeypots by using
a botnet monitoring sensor is also considered which gives a clear picture on botnet activity but
once the botmasters destroy the sensor, the machine or target will be infected. Our proposed
method illustrates the design is practical and can be implemented by defenders with little
complexities.
6. Rajab Challoo & Raghavendra Kotapalli
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 501
5 : CONCLUSION
Due to their potential for illicit financial gain, “botnets” have become popular among Internet
attackers in recent years. As security defenders build more honeypot-based detection and
defense systems, attackers will find ways to avoid honeypot traps in their botnets. Attackers can
use software or hardware specific codes to detect the honeypot virtual environment [6, 7, 16], but
they can also rely on a more general principle to detect honeypots: security professionals using
honeypots have liability constraints such that their honeypots cannot be configured in a way that
would allow them to send out real malicious attacks or too many malicious attacks. In this paper,
we introduced a means for defending the network from botnets, when Honeypots are infected and
then deploy a P2P botnet which would act as a filter to remove the infected Honeypots which
remain in the Network Cluster and hence the uninfected Honeypots can be used with efficacy to
defend the network. Honeypot research and deployment is important and should continue for the
security community, but we hope this paper will remind honeypot researchers of the importance
of studying ways to build covert honeypots, and the limitation in deploying honeypots in security
defense. The current popular research focused on finding effective honeypot-based detection and
defense approaches will be for naught if honeypots remain as easily detectible as they are
presently.
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