An advanced routing worm is presented that can propagate faster than traditional worms. It does this by only scanning addresses that are routable based on Border Gateway Protocol (BGP) routing tables, reducing the scanning space. This allows the routing worm to spread up to three times faster than worms that scan the entire IPv4 address space. Additionally, the geographic information from BGP tables enables selective attacks targeting specific countries, companies, or networks. Routing worms pose new security challenges and are important to study for simulating internet-scale worm propagation.
An improved ip traceback mechanism for network securityeSAT Journals
This document summarizes several existing IP traceback techniques and proposes a new hybrid approach. It discusses disadvantages of current techniques like high storage and bandwidth overhead. The proposed approach aims to reduce these overheads while maintaining single-packet traceability. It would reduce the number of routers queried and storage required by 2/3 compared to existing approaches. The approach was analyzed using the CAIDA dataset but has not been tested in a real-time network. The document concludes future work could develop a real-time traceback mechanism to identify attacks within a network.
This document discusses internet networks and cyber security. It covers topics like social media attacks, asymmetric cyber security issues, how social media can be used for cyber attacks, Google's bug bounty program, two-factor authentication services, prospects for future attacks on cyber security, the historical development of microprocessors, projected development of machine intelligence, the topology and components of internet networks, advantages and liabilities of the internet, problems with networks and servers, internet protocols and protocol stacks, what is contained in an IPv4 packet header, factors driving computing to the edge, examples of latency in internet transmissions, the need to transition from IPv4 to IPv6, and features of virtual private networks like VPN protocols.
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.
This document summarizes a research paper that proposes improvements to the probabilistic packet marking (PPM) algorithm for detecting the path of distributed denial-of-service attacks. The PPM algorithm allows routers to mark attack packets with identification information based on a predetermined probability. However, its termination condition is not well-defined, which can result in an incorrectly constructed attack path. The paper proposes a modified PPM algorithm called rectified PPM (RPPM) that defines a precise termination condition to guarantee the constructed attack path is correct with a specified level of confidence. An experimental framework is designed to test the RPPM algorithm under different packet marking probabilities and network structures.
Identifying Malicious Data in Social MediaIRJET Journal
This document discusses two approaches for identifying malicious data in social media: Shannon entropy and power law distribution. The Shannon entropy approach calculates the entropy of features like source/destination IP addresses and port numbers to detect anomalous network traffic patterns. The power law distribution approach models malware propagation across networks and finds that malware distribution transitions from exponential to power law over time. Experimental results on social media datasets found the Shannon entropy approach could detect malware based on the number of applications installed, while power law distribution identified good and malicious files shared between users. Both techniques aim to improve detection of malicious content shared over social networks.
Internet Worm Classification and Detection using Data Mining Techniquesiosrjce
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.
CONTROLLING IP FALSIFYING USING REALISTIC SIMULATIONIJNSA Journal
This document discusses a proposal to develop a new distributed Internet simulator to study large-scale network events like distributed denial-of-service (DDoS) attacks and worm propagation. Existing network simulators have limited scalability and lack realistic Internet models. The proposed simulator would have a built-in Internet topology model and customizeable modules to simulate specific events while cutting down on unnecessary details. It aims to make large-scale network simulation more accessible to researchers and improve the realism of simulations compared to simplified models currently used. The simulator could help study defenses against problems like IP spoofing, DDoS attacks, and worms.
An effective architecture and algorithm for detecting worms with various scan...UltraUploader
The document proposes and evaluates an architecture and algorithm for detecting worm infections that use various scanning techniques. It analyzes different scan methods worms could use, such as random scanning, scanning only addresses in routing tables, and hitlist scanning. It then presents a generic worm detection architecture that monitors for malicious activities by analyzing statistics on scan traffic, such as the number of source addresses and traffic volume. The paper introduces an algorithm called the victim number based algorithm that relies solely on increases in the number of source addresses to detect infections. Simulation results show this algorithm can detect a Code Red-like worm when only 4% of machines are infected.
An improved ip traceback mechanism for network securityeSAT Journals
This document summarizes several existing IP traceback techniques and proposes a new hybrid approach. It discusses disadvantages of current techniques like high storage and bandwidth overhead. The proposed approach aims to reduce these overheads while maintaining single-packet traceability. It would reduce the number of routers queried and storage required by 2/3 compared to existing approaches. The approach was analyzed using the CAIDA dataset but has not been tested in a real-time network. The document concludes future work could develop a real-time traceback mechanism to identify attacks within a network.
This document discusses internet networks and cyber security. It covers topics like social media attacks, asymmetric cyber security issues, how social media can be used for cyber attacks, Google's bug bounty program, two-factor authentication services, prospects for future attacks on cyber security, the historical development of microprocessors, projected development of machine intelligence, the topology and components of internet networks, advantages and liabilities of the internet, problems with networks and servers, internet protocols and protocol stacks, what is contained in an IPv4 packet header, factors driving computing to the edge, examples of latency in internet transmissions, the need to transition from IPv4 to IPv6, and features of virtual private networks like VPN protocols.
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.
This document summarizes a research paper that proposes improvements to the probabilistic packet marking (PPM) algorithm for detecting the path of distributed denial-of-service attacks. The PPM algorithm allows routers to mark attack packets with identification information based on a predetermined probability. However, its termination condition is not well-defined, which can result in an incorrectly constructed attack path. The paper proposes a modified PPM algorithm called rectified PPM (RPPM) that defines a precise termination condition to guarantee the constructed attack path is correct with a specified level of confidence. An experimental framework is designed to test the RPPM algorithm under different packet marking probabilities and network structures.
Identifying Malicious Data in Social MediaIRJET Journal
This document discusses two approaches for identifying malicious data in social media: Shannon entropy and power law distribution. The Shannon entropy approach calculates the entropy of features like source/destination IP addresses and port numbers to detect anomalous network traffic patterns. The power law distribution approach models malware propagation across networks and finds that malware distribution transitions from exponential to power law over time. Experimental results on social media datasets found the Shannon entropy approach could detect malware based on the number of applications installed, while power law distribution identified good and malicious files shared between users. Both techniques aim to improve detection of malicious content shared over social networks.
Internet Worm Classification and Detection using Data Mining Techniquesiosrjce
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.
CONTROLLING IP FALSIFYING USING REALISTIC SIMULATIONIJNSA Journal
This document discusses a proposal to develop a new distributed Internet simulator to study large-scale network events like distributed denial-of-service (DDoS) attacks and worm propagation. Existing network simulators have limited scalability and lack realistic Internet models. The proposed simulator would have a built-in Internet topology model and customizeable modules to simulate specific events while cutting down on unnecessary details. It aims to make large-scale network simulation more accessible to researchers and improve the realism of simulations compared to simplified models currently used. The simulator could help study defenses against problems like IP spoofing, DDoS attacks, and worms.
An effective architecture and algorithm for detecting worms with various scan...UltraUploader
The document proposes and evaluates an architecture and algorithm for detecting worm infections that use various scanning techniques. It analyzes different scan methods worms could use, such as random scanning, scanning only addresses in routing tables, and hitlist scanning. It then presents a generic worm detection architecture that monitors for malicious activities by analyzing statistics on scan traffic, such as the number of source addresses and traffic volume. The paper introduces an algorithm called the victim number based algorithm that relies solely on increases in the number of source addresses to detect infections. Simulation results show this algorithm can detect a Code Red-like worm when only 4% of machines are infected.
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.
This document discusses the detection of "smart worms", which are malicious software programs that can intelligently manipulate their scanning behavior to avoid detection. The authors propose a novel spectrum-based scheme to detect smart worms using power spectral density analysis of traffic volumes. Their scheme analyzes the spectral flatness measure of worm traffic compared to background traffic. Evaluation results demonstrate the scheme can effectively detect smart worm propagation and outperforms existing detection methods. The authors also show it can detect traditional worms.
Self-propagating malware (e.g., an Internet worm) exploits security loopholes in software to infect servers and then use them to scan the Internet for more vulnerable servers. While the mechanisms of worm infection and their propagation models are well understood, defense against worms remains an open problem. One branch of defense research investigates the behavioral difference between worm-infected hosts and normal hosts to set them apart. One particular observation is that a worm-infected host, which scans the Internet with randomly selected addresses, has a much higher connection-failure rate than a normal host. Rate-limit algorithms have been proposed to control the spread of worms by traffic shaping based on connection failure rate. However, these rate-limit algorithms can work properly only if it is possible to measure failure rates of individual hosts efficiently and accurately. This paper points out a serious problem in the prior method. To address this problem, we first propose a solution based on a highly efficient double-bitmap data structure, which places only a small memory footprint on the routers, while providing good measurement of connection failure rates whose accuracy can be tuned by system parameters. Furthermore, we propose another solution based on shared register array data structure, achieving better memory efficiency and much larger estimation range than our double-bitmap solution.
This document proposes a novel method to defend against IP spoofing attacks using packet filtering and marking techniques. It involves a network architecture model with trusted nodes that can access each other after authentication. The proposed method uses packet tracing and cooperation between trusted adjacent nodes to detect and block spoofed packets entering the trusted network from external sources. It aims to effectively defend against distributed denial of service attacks and IP spoofing attacks.
This document summarizes a research paper that proposes techniques to detect and localize multiple spoofing attackers in wireless networks using received signal strength (RSS). It begins by introducing the problem of spoofing attacks and outlines three goals: detecting attacks, determining the number of attackers, and localizing multiple adversaries. It then reviews related work on secure routing protocols and key management schemes. An overview of the proposed techniques is provided, including a generalized attack detection model, determining the number of attackers as a multiclass detection problem, and an integrated detection and localization framework (IDOL). Several localization algorithms are also summarized. Experimental results showed the proposed methods can achieve over 90% accuracy in determining the number of attackers.
Passive IP Traceback: Disclosing the Locations of IP Spoofers from Path Backs...1crore projects
PIT is a passive IP traceback technique that tracks the locations of IP spoofers using path backscatter messages generated by routers. When spoofing traffic fails to be forwarded, routers may generate ICMP error messages containing path information and send them to the spoofed source address. PIT collects these path backscatter messages and uses topology and routing data to infer the locations of spoofers without any additional deployment. By applying PIT to an existing path backscatter dataset, several ASes where spoofers were located were identified, representing the first public disclosure of spoofer locations.
Trusted Routing Path Selection in WSNs through TARFEditor IJMTER
In wireless Sensor Networks data transfer is insecure, because the intruders may use
duplicate IP address to hack the confidential data. Hop by hop authentication is necessary for secured
communication to prevent such confidentiality Multi hop routing in Wireless sensor networks
(WSNs) offers little protection against the identity deception through replaying routing information.
This defect may take a chance of an adversary to misdirect significant network traffic, resulting in
disastrous consequences attacks against the routing protocols including Sinkhole, Worm hole and
Sybil attacks. The situation is further aggravated by mobile & harsh network condition. It cannot be
solved by traditional encryption or authentication techniques or efforts at developing trust aware
routing protocols do not effectively address this severe problem. Secure the WSNs against
adversaries misdirecting the multi-hop routing. So proposed a method is “Trusted Routing Path
Selection in WSNs through TARF”, a robust trust-aware routing framework for dynamic WSNs.
Without tight time synchronization or known geographic information. TARF provides trustworthy,
secure, time efficient & energy efficient route. Most importantly TARF proves effective against
those harmful attacks developed out of identity deception; the resilience of TARF is verified through
extensive evaluation with both simulation and empirical experiments on large scale WSNs under
various scenarios.
Identity Based Detection of Spoofing Attackers in Wireless Networks and Pract...Kumar Goud
Abstract: Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple adversaries masquerading as a same node identity; and (3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multi-class detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data is available, we explore using Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90% Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.
Keywords: Wifi, Spoofing, Wireless, RSS, MAX, WEP, WPA, ISP
IRJET- Detection of Spoofing and Jamming Attacks in Wireless Smart Grid Netwo...IRJET Journal
This document discusses detecting spoofing and jamming attacks in wireless smart grid networks using RSS (Received Signal Strength) algorithms. It proposes a traffic analysis method using RSS to infer contextual information from jamming and spoofing attacks. The method relies only on packet transmission times and eavesdropper locations. It is intended as a baseline for evaluating protection mechanisms with different assumptions. RSS values are correlated with physical location and widely used in localization algorithms. The proposed system aims to reduce communication overhead and delay compared to existing approaches by limiting injected dummy traffic through constructing minimum connected dominating sets and shortest path trees.
This document discusses a novel method called Early Detection of SYN Flooding Attack by Adaptive Thresholding (EDSAT) to detect SYN flooding attacks in mobile ad hoc networks. SYN flooding is a denial of service attack that exploits weaknesses in TCP by flooding a target with spoofed SYN requests, overwhelming its resources. EDSAT uses an optimized adaptive threshold algorithm that monitors the SYN arrival rate and raises an alarm if it increases above an adaptive threshold based on a moving average of past rates. This helps detect attacks early by accounting for normal variations in traffic. The paper aims to optimize tuning parameters to improve detection performance compared to standard adaptive thresholding methods.
Utilizing Data Mining Approches in the Detection of Intrusion in IPv6 Network...IDES Editor
The development of Internet protocols are greatly
needed as the network security becomes one of the most
important issues. This brings the need to develop IPv4 into
IPv6 in order to proceed towards increasing the network
capacity.
Now Intruders are considered as one of the most serious
threats to the internet security. Data mining techniques have
been successfully utilized in many applications. Many
research projects have applied data mining techniques to
intrusion detection. Furthermore different types of data
mining algorithms are very much useful to intrusion detection
such as Classification, Link Analysis and Sequence Analysis.
Moreover, one of the major challenges in securing fast
networks is the online detection of suspicious anomalies in
network traffic pattern. Most of the current security solutions
failed to perform the security task in online mode because of
the time needed to capture the packets and making decision
about it.
Practically, this study provides alliterative survey for the
enhancement associated with IPv6 in terms of its security
related functions. It is worthy mentioned that this study is
concurred with the data mining approaches that have been
used to detect intrusions.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Combined Approach for Worm-Hole and Black-Hole Attack Detection in MANETIJERA Editor
Mobile ad hoc network is a kind of wireless network, in this network all nodes are connected through the wireless links and perform cooperative communication.Due to limited radio range of these devices any time can leave or join the network. Therefore the routing techniques are responsible for the network organization and communication flow. Due to this the performance of MANET is low as compared with the traditional wired communication networks. In addition of that network is suffers from the probability of attacks. Thus in this paper MANET routing strategy and their attacks are investigated and learned. In addition of that in order to secure the communication recent approaches of security in MANET also investigated. Finally a new algorithm for prevention of malicious attack in MANET is suggested. Additionally the based on the concluded facts, future extension of the proposed work is also suggested.
ASYMTOTIC ANALYSIS IN SECURED MESSAGE DELIVERYAM Publications
Wireless networking is a method by which homes, telecommunications networks and enterprise (business) installations avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. For such a reasons this technology has become popular. Though it is familiar, its wireless channel is vulnerable to the eavesdroppers during message delivery (security is the major problem). In the previous cases this problem was solved by cryptographic methods such as RSA public key cryptosystem. But due to expensive key distribution and improvement in decoding technology, the message transmitted is said to be unsecured. The problem can be overcome by using artificial noise generation. This paper investigates and studies how to deliver the message securely in the wireless network using artificial noise generation concept.
This document proposes a machine learning approach using the Naive Bayes algorithm to detect distributed denial of service (DDoS) attacks through network intrusion detection. It first discusses the issues with existing intrusion detection systems, including long training times and low accuracy. It then summarizes research on applying various machine learning techniques like neural networks, decision trees, and Naive Bayes to intrusion detection. The proposed system would build a classifier using Naive Bayes, which provides faster training than other methods, to distinguish normal and attack traffic. This approach aims to improve upon the training time and detection accuracy of existing intrusion detection systems.
Public key based approach to mitigate wormhole attack repaired prjpublications
This document summarizes several secure routing protocols for mobile ad hoc networks (MANETs). It analyzes protocols such as ARAN, SAR, SRP, SEAD, ARIADNE, and SAODV. Each protocol is described in 1-2 paragraphs outlining its key authentication and security features. The document also analyzes the types of attacks each protocol is susceptible to, such as wormhole attacks, rushing attacks, denial of service attacks, and black hole attacks. The overall purpose is to evaluate different secure routing approaches and their ability to mitigate various security threats in MANETs.
DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Vampire attacks draining life from w...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
We offer you free sample questions along answers prepared by the professionals of the IT field. You can easily pass your CISSP Test with our Training Kits. For more info please visit here: http://www.certsgrade.com/pdf/CISSP/
This document discusses using data mining techniques to classify and detect internet worms. It proposes a model that preprocesses network packet data to extract features, then uses three data mining algorithms (Random Forest, Decision Tree, Bayesian Network) to classify the data as normal, worm, or other network attacks. The model was able to detect internet worms with over 99% accuracy and less than 1% false alarm rate when classifying test data, outperforming Bayesian Network. In general, the document evaluates using machine learning for network-based internet worm detection.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Passive ip traceback disclosing the locations of ip spoofers from path backsc...Pvrtechnologies Nellore
Passive IP traceback (PIT) is a novel solution that can identify the locations of IP spoofers without deploying additional mechanisms. PIT analyzes Internet Control Message Protocol (ICMP) error messages, called path backscatter, that are generated and sent by routers when they fail to forward spoofing packets due to reasons like exceeding time-to-live (TTL). By tracking path backscatter messages based on topology and routing information, PIT can disclose locations closer to the spoofers. The paper demonstrates PIT's processes and effectiveness, and applies it to a path backscatter dataset to find spoofers in specific autonomous systems. PIT provides a useful mechanism to trace spoofers before an Internet-level traceback system is deployed.
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.
This document discusses the detection of "smart worms", which are malicious software programs that can intelligently manipulate their scanning behavior to avoid detection. The authors propose a novel spectrum-based scheme to detect smart worms using power spectral density analysis of traffic volumes. Their scheme analyzes the spectral flatness measure of worm traffic compared to background traffic. Evaluation results demonstrate the scheme can effectively detect smart worm propagation and outperforms existing detection methods. The authors also show it can detect traditional worms.
Self-propagating malware (e.g., an Internet worm) exploits security loopholes in software to infect servers and then use them to scan the Internet for more vulnerable servers. While the mechanisms of worm infection and their propagation models are well understood, defense against worms remains an open problem. One branch of defense research investigates the behavioral difference between worm-infected hosts and normal hosts to set them apart. One particular observation is that a worm-infected host, which scans the Internet with randomly selected addresses, has a much higher connection-failure rate than a normal host. Rate-limit algorithms have been proposed to control the spread of worms by traffic shaping based on connection failure rate. However, these rate-limit algorithms can work properly only if it is possible to measure failure rates of individual hosts efficiently and accurately. This paper points out a serious problem in the prior method. To address this problem, we first propose a solution based on a highly efficient double-bitmap data structure, which places only a small memory footprint on the routers, while providing good measurement of connection failure rates whose accuracy can be tuned by system parameters. Furthermore, we propose another solution based on shared register array data structure, achieving better memory efficiency and much larger estimation range than our double-bitmap solution.
This document proposes a novel method to defend against IP spoofing attacks using packet filtering and marking techniques. It involves a network architecture model with trusted nodes that can access each other after authentication. The proposed method uses packet tracing and cooperation between trusted adjacent nodes to detect and block spoofed packets entering the trusted network from external sources. It aims to effectively defend against distributed denial of service attacks and IP spoofing attacks.
This document summarizes a research paper that proposes techniques to detect and localize multiple spoofing attackers in wireless networks using received signal strength (RSS). It begins by introducing the problem of spoofing attacks and outlines three goals: detecting attacks, determining the number of attackers, and localizing multiple adversaries. It then reviews related work on secure routing protocols and key management schemes. An overview of the proposed techniques is provided, including a generalized attack detection model, determining the number of attackers as a multiclass detection problem, and an integrated detection and localization framework (IDOL). Several localization algorithms are also summarized. Experimental results showed the proposed methods can achieve over 90% accuracy in determining the number of attackers.
Passive IP Traceback: Disclosing the Locations of IP Spoofers from Path Backs...1crore projects
PIT is a passive IP traceback technique that tracks the locations of IP spoofers using path backscatter messages generated by routers. When spoofing traffic fails to be forwarded, routers may generate ICMP error messages containing path information and send them to the spoofed source address. PIT collects these path backscatter messages and uses topology and routing data to infer the locations of spoofers without any additional deployment. By applying PIT to an existing path backscatter dataset, several ASes where spoofers were located were identified, representing the first public disclosure of spoofer locations.
Trusted Routing Path Selection in WSNs through TARFEditor IJMTER
In wireless Sensor Networks data transfer is insecure, because the intruders may use
duplicate IP address to hack the confidential data. Hop by hop authentication is necessary for secured
communication to prevent such confidentiality Multi hop routing in Wireless sensor networks
(WSNs) offers little protection against the identity deception through replaying routing information.
This defect may take a chance of an adversary to misdirect significant network traffic, resulting in
disastrous consequences attacks against the routing protocols including Sinkhole, Worm hole and
Sybil attacks. The situation is further aggravated by mobile & harsh network condition. It cannot be
solved by traditional encryption or authentication techniques or efforts at developing trust aware
routing protocols do not effectively address this severe problem. Secure the WSNs against
adversaries misdirecting the multi-hop routing. So proposed a method is “Trusted Routing Path
Selection in WSNs through TARF”, a robust trust-aware routing framework for dynamic WSNs.
Without tight time synchronization or known geographic information. TARF provides trustworthy,
secure, time efficient & energy efficient route. Most importantly TARF proves effective against
those harmful attacks developed out of identity deception; the resilience of TARF is verified through
extensive evaluation with both simulation and empirical experiments on large scale WSNs under
various scenarios.
Identity Based Detection of Spoofing Attackers in Wireless Networks and Pract...Kumar Goud
Abstract: Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple adversaries masquerading as a same node identity; and (3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multi-class detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data is available, we explore using Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90% Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.
Keywords: Wifi, Spoofing, Wireless, RSS, MAX, WEP, WPA, ISP
IRJET- Detection of Spoofing and Jamming Attacks in Wireless Smart Grid Netwo...IRJET Journal
This document discusses detecting spoofing and jamming attacks in wireless smart grid networks using RSS (Received Signal Strength) algorithms. It proposes a traffic analysis method using RSS to infer contextual information from jamming and spoofing attacks. The method relies only on packet transmission times and eavesdropper locations. It is intended as a baseline for evaluating protection mechanisms with different assumptions. RSS values are correlated with physical location and widely used in localization algorithms. The proposed system aims to reduce communication overhead and delay compared to existing approaches by limiting injected dummy traffic through constructing minimum connected dominating sets and shortest path trees.
This document discusses a novel method called Early Detection of SYN Flooding Attack by Adaptive Thresholding (EDSAT) to detect SYN flooding attacks in mobile ad hoc networks. SYN flooding is a denial of service attack that exploits weaknesses in TCP by flooding a target with spoofed SYN requests, overwhelming its resources. EDSAT uses an optimized adaptive threshold algorithm that monitors the SYN arrival rate and raises an alarm if it increases above an adaptive threshold based on a moving average of past rates. This helps detect attacks early by accounting for normal variations in traffic. The paper aims to optimize tuning parameters to improve detection performance compared to standard adaptive thresholding methods.
Utilizing Data Mining Approches in the Detection of Intrusion in IPv6 Network...IDES Editor
The development of Internet protocols are greatly
needed as the network security becomes one of the most
important issues. This brings the need to develop IPv4 into
IPv6 in order to proceed towards increasing the network
capacity.
Now Intruders are considered as one of the most serious
threats to the internet security. Data mining techniques have
been successfully utilized in many applications. Many
research projects have applied data mining techniques to
intrusion detection. Furthermore different types of data
mining algorithms are very much useful to intrusion detection
such as Classification, Link Analysis and Sequence Analysis.
Moreover, one of the major challenges in securing fast
networks is the online detection of suspicious anomalies in
network traffic pattern. Most of the current security solutions
failed to perform the security task in online mode because of
the time needed to capture the packets and making decision
about it.
Practically, this study provides alliterative survey for the
enhancement associated with IPv6 in terms of its security
related functions. It is worthy mentioned that this study is
concurred with the data mining approaches that have been
used to detect intrusions.
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International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Combined Approach for Worm-Hole and Black-Hole Attack Detection in MANETIJERA Editor
Mobile ad hoc network is a kind of wireless network, in this network all nodes are connected through the wireless links and perform cooperative communication.Due to limited radio range of these devices any time can leave or join the network. Therefore the routing techniques are responsible for the network organization and communication flow. Due to this the performance of MANET is low as compared with the traditional wired communication networks. In addition of that network is suffers from the probability of attacks. Thus in this paper MANET routing strategy and their attacks are investigated and learned. In addition of that in order to secure the communication recent approaches of security in MANET also investigated. Finally a new algorithm for prevention of malicious attack in MANET is suggested. Additionally the based on the concluded facts, future extension of the proposed work is also suggested.
ASYMTOTIC ANALYSIS IN SECURED MESSAGE DELIVERYAM Publications
Wireless networking is a method by which homes, telecommunications networks and enterprise (business) installations avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. For such a reasons this technology has become popular. Though it is familiar, its wireless channel is vulnerable to the eavesdroppers during message delivery (security is the major problem). In the previous cases this problem was solved by cryptographic methods such as RSA public key cryptosystem. But due to expensive key distribution and improvement in decoding technology, the message transmitted is said to be unsecured. The problem can be overcome by using artificial noise generation. This paper investigates and studies how to deliver the message securely in the wireless network using artificial noise generation concept.
This document proposes a machine learning approach using the Naive Bayes algorithm to detect distributed denial of service (DDoS) attacks through network intrusion detection. It first discusses the issues with existing intrusion detection systems, including long training times and low accuracy. It then summarizes research on applying various machine learning techniques like neural networks, decision trees, and Naive Bayes to intrusion detection. The proposed system would build a classifier using Naive Bayes, which provides faster training than other methods, to distinguish normal and attack traffic. This approach aims to improve upon the training time and detection accuracy of existing intrusion detection systems.
Public key based approach to mitigate wormhole attack repaired prjpublications
This document summarizes several secure routing protocols for mobile ad hoc networks (MANETs). It analyzes protocols such as ARAN, SAR, SRP, SEAD, ARIADNE, and SAODV. Each protocol is described in 1-2 paragraphs outlining its key authentication and security features. The document also analyzes the types of attacks each protocol is susceptible to, such as wormhole attacks, rushing attacks, denial of service attacks, and black hole attacks. The overall purpose is to evaluate different secure routing approaches and their ability to mitigate various security threats in MANETs.
DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Vampire attacks draining life from w...IEEEGLOBALSOFTTECHNOLOGIES
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This document discusses using data mining techniques to classify and detect internet worms. It proposes a model that preprocesses network packet data to extract features, then uses three data mining algorithms (Random Forest, Decision Tree, Bayesian Network) to classify the data as normal, worm, or other network attacks. The model was able to detect internet worms with over 99% accuracy and less than 1% false alarm rate when classifying test data, outperforming Bayesian Network. In general, the document evaluates using machine learning for network-based internet worm detection.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Passive ip traceback disclosing the locations of ip spoofers from path backsc...Pvrtechnologies Nellore
Passive IP traceback (PIT) is a novel solution that can identify the locations of IP spoofers without deploying additional mechanisms. PIT analyzes Internet Control Message Protocol (ICMP) error messages, called path backscatter, that are generated and sent by routers when they fail to forward spoofing packets due to reasons like exceeding time-to-live (TTL). By tracking path backscatter messages based on topology and routing information, PIT can disclose locations closer to the spoofers. The paper demonstrates PIT's processes and effectiveness, and applies it to a path backscatter dataset to find spoofers in specific autonomous systems. PIT provides a useful mechanism to trace spoofers before an Internet-level traceback system is deployed.
CONTROLLING IP FALSIFYING USING REALISTIC SIMULATIONIJNSA Journal
This document discusses a proposal to develop a new distributed Internet simulator to study large-scale network events like distributed denial-of-service (DDoS) attacks and worm propagation. Existing network simulators have limited scalability and lack realistic Internet models. The proposed simulator would have a built-in Internet topology model and customizeable modules to simulate specific events while cutting down on unnecessary details. It aims to make large-scale network simulation more accessible to researchers and improve the realism of simulations compared to current tools.
Security Issues in Next Generation IP and Migration NetworksIOSR Journals
This document discusses security issues related to the migration from IPv4 to IPv6 networks. It analyzes common network attacks in IPv4 and how they may impact IPv6 networks. These attacks include reconnaissance attacks, host initialization attacks, broadcast amplification attacks, header manipulation attacks, routing attacks, and firewall evasion through fragmentation. The document provides guidelines to mitigate each of these attacks, such as using random node IDs, securing neighbor discovery protocols, ingress filtering of packets, and deep packet inspection. It addresses that while IPv6 aims to improve security over IPv4, vulnerabilities will still exist and many attacks will continue in a polymorphic manner during the migration process when both protocols must coexist.
This document discusses security issues related to the migration from IPv4 to IPv6 networks. It analyzes common network attacks in IPv4 and how they may impact IPv6 networks. These attacks include reconnaissance attacks, host initialization attacks, broadcast amplification attacks, header manipulation attacks, routing attacks, and firewall evasion through fragmentation. The document provides guidelines to mitigate these attacks, such as using random node IDs, securing neighbor discovery and DHCPv6, ingress filtering of packets, and parsing entire extension header chains. It addresses that while IPv6 introduces new vulnerabilities, existing IPv4 threats will also impact IPv6 networks, and secure migration techniques are needed as IPv4 and IPv6 networks coexist during the transition period.
Where are we with Securing the Routing System?APNIC
The document discusses security issues with the global routing system and Border Gateway Protocol (BGP). It notes that routing is built on trust but there are no effective defenses against abuse. The base problem is that while routing attacks could have massive effects, no entity has enough incentive to thoroughly audit routing integrity. Possible solutions proposed include securing routers against compromise, securing BGP sessions, and developing ways to verify the legitimacy of routing updates. However, fully solving routing security challenges may be extremely difficult.
A Survey on Cloud-Based IP Trace Back FrameworkIRJET Journal
This document summarizes a survey of cloud-based IP traceback frameworks. It proposes a cloud-based traceback architecture with three layers: an intra-AS layer where traceback servers in each Autonomous System (AS) collect and store traffic flow data; a traceback as a service layer where ASes expose their traceback capabilities; and an inter-AS logical links layer to facilitate efficient traceback across ASes. It then focuses on access control to prevent unauthorized users from requesting traceback information. To address this, it proposes a temporal token-based authentication framework called FACT that embeds tokens in traffic flows and delivers them to end hosts to authenticate traceback queries. The framework aims to ensure only actual recipients of packets can initiate traceback for those packets.
IRJET- Gray-Hole Attack Minimization based on contradiction for ad-hoc networksIRJET Journal
This document discusses minimizing the Gray-Hole attack in mobile ad-hoc networks. It proposes a method called Denial Contradictions with Fictitious Node Mechanism (DCFM) which uses internal network knowledge to identify potential malicious nodes trying to falsify routing information without relying on external trusted parties. DCFM works by checking for logical inconsistencies between a node's routing messages and the actual network topology to detect attacks. The paper analyzes how DCFM can help detect Gray-Hole attacks under the Optimized Link State Routing protocol and reduce packet dropping compared to other approaches through early identification of malicious nodes. Simulation experiments demonstrate the effectiveness of DCFM at minimizing the Gray-Hole attack in ad-hoc
A secure tunnel technique using i pv6 transition over ipv4 channelMade Artha
This document discusses secure tunneling techniques for IPv6 transition over IPv4 networks. It first provides background on the development of IPv6 and need to replace IPv4 due to limited address space. It then discusses three common approaches for IPv4-IPv6 transition: dual stack, translation, and tunneling. The document focuses on issues with the tunneling approach, such as one network attacking another or spoofing attacks. It proposes a solution using a test bed with two IPv6 networks connected via an IPv4 network and 6to4 routers, with outbound filtering to address security issues.
This document discusses some of the security advantages of IPv6 over IPv4. It begins with an introduction to IPv6 and outlines some of the improvements in the IPv6 header compared to IPv4. It then discusses types of attacks that existed in IPv4, such as reconnaissance, header manipulation, spoofing, and DHCP attacks. It analyzes security issues that exist in IPv6, such as the large address space and how threats can be dealt with. Finally, it discusses IPsec as the mandatory security mechanism in IPv6 and how it provides confidentiality, integrity, and authentication through the use of authentication headers and encrypted security payloads.
Passive ip traceback disclosing the locations of ip spoofers from path backsc...Shakas Technologies
It is long known attackers may use forged source IP address to conceal their real locations. To capture the spoofers, a number of IP traceback mechanisms have been proposed. However, due to the challenges of deployment, there has been not a widely adopted IP traceback solution, at least at the Internet level. As a result, the mist on the locations of spoofers has never been dissipated till now
Denial of service attack: an analysis to IPv6 extension headers security nig...IJECEIAES
Dealing with scarcity issues of internet protocol version 4 (IPv4), internet engineering task force (IETF) developed internet protocol version 6 (IPv6) to support the needs of IP addresses for future use of the internet, however, one challenge that must be faced while transitioning to IPv6 is in the area of security. IPv6 is a new protocol that has many new probabilities for attackers to exploit the protocol stack and one of them is through IPv6 extension headers. Mishandling of extension headers are the security nightmares for network administrators, allowing for new security threats that will cause denial of service (DoS). As a result, the mishandling of IPv6 extension Headers creates new attack vectors that could lead to DoS–which can be exploited for different purposes, such as creating covert channels, fragmentation attacks, and routing header 0 attacks. Furthermore, this paper becomes proof of concepts that even to this day our well-known network devices are still exploitable by these attack vectors.
A Deeper Look into Network Traffic Analysis using Wireshark.pdfJessica Thompson
This document discusses network traffic analysis using Wireshark. It begins with an introduction to how network traffic analysis is important for performance optimization, network forensics, penetration testing, and ensuring integrated systems work properly. It then discusses how traffic analysis can be used maliciously by attackers to obtain sensitive information like passwords and files. The document goes on to explain how Wireshark can be used for both legitimate network analysis and malicious attacks, and describes different types of network attacks like passive and active attacks. It also discusses methods attackers can use to sniff network traffic on a switch. The document concludes with recommendations for countermeasures like access restrictions, encryption, and switch security features.
AN EFFECTIVE PREVENTION OF ATTACKS USING GI TIME FREQUENCY ALGORITHM UNDER DDOSIJNSA Journal
This document summarizes an algorithm called the GI (Group Intruders) Time Frequency Algorithm that is proposed to identify hackers attempting distributed denial of service (DDoS) attacks on websites. The algorithm works by maintaining a history of all user access to the site that includes their IP address and time/date of each access. It identifies users that access the site repeatedly from the same IP address on a single date by calculating the average time between accesses. If the time frequency of accesses exceeds a predefined threshold, the user is added to an intruders list to deny future access. This aims to improve server performance by preventing hackers from overloading the server with requests.
BasepaperControlling IP Spoofing through Interdomain Packet Filtersbhasker nalaveli
This document discusses controlling IP spoofing through interdomain packet filters (IDPFs). It proposes an IDPF architecture that can mitigate IP spoofing without requiring global routing information. IDPFs are constructed using information from Border Gateway Protocol (BGP) route updates and deployed in border routers. Simulation results show that even partial deployment of IDPFs can limit spoofing capability of attackers and help localize the origin of attack packets.
Analytical Study on Network Security Breach’sijtsrd
Throughout the previous few years, Computer systems were principally utilized by association for correspondence between various divisions. Under these conditions security was not a significant concern and it didnt get part of consideration. Be that as it may, presently, there is an extraordinary effect of between organize job in every single residents life, from Banking – Hospitals Education Transportation and so forth. However, presently arrange has sprouted different security concerns. In any case, presently with the expanding utilization of Computer in everyday action there is a serious requirement for robotized devices for securing touchy information and data put away on the Computer. Especially for the situation for a mutual framework, for example, time sharing framework and where the need is significantly increasingly intense for frameworks that is available for an open phone or an information organize. The standard name for the assortment of devices to ensure information and to forestall Hackers is Computer Security . This proposition talk about and depicts spoofing , which is if an aggressor can tune in for a customers ask for and imitate an answer before the genuine location server can, at that point the customer will utilize the data gave by the hacker. This is known as spoofing. Siddiqui Sana Afreen "Analytical Study on Network Security Breach’s" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30403.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30403/analytical-study-on-network-security-breach%E2%80%99s/siddiqui-sana-afreen
IPv6 flood attack detection based on epsilon greedy optimized Q learning in s...IJECEIAES
Internet of things is a technology that allows communication between devices within a network. Since this technology depends on a network to communicate, the vulnerability of the exposed devices increased significantly. Furthermore, the use of internet protocol version 6 (IPv6) as the successor to internet protocol version 4 (IPv4) as a communication protocol constituted a significant problem for the network. Hence, this protocol was exploitable for flooding attacks in the IPv6 network. As a countermeasure against the flood, this study designed an IPv6 flood attack detection by using epsilon greedy optimized Q learning algorithm. According to the evaluation, the agent with epsilon 0.1 could reach 98% of accuracy and 11,550 rewards compared to the other agents. When compared to control models, the agent is also the most accurate compared to other algorithms followed by neural network (NN), K-nearest neighbors (KNN), decision tree (DT), naive Bayes (NB), and support vector machine (SVM). Besides that, the agent used more than 99% of a single central processing unit (CPU). Hence, the agent will not hinder internet of things (IoT) devices with multiple processors. Thus, we concluded that the proposed agent has high accuracy and feasibility in a single board computer (SBC).
A survey on evil twin detection methods for wireless local area networkIAEME Publication
This document summarizes a survey of recent methods for detecting evil twin access points in wireless local area networks (WLANs). It describes the background and challenges of evil twin attacks, where attackers create rogue access points that mimic authorized ones to steal user information. The document categorizes detection methods as network admin-side or client-side solutions. It reviews several specific detection techniques, such as those based on timing analysis, received signal strength, or separating one-hop from two-hop wireless connections. Finally, it discusses issues like the difficulty of completely detecting evil twins and challenges with wireless-only versus wired-side detection approaches.
IP spoofing is a method of attacking a network by disguising the source IP address of packets sent to a target. The attacker determines the IP address of a trusted machine and spoofs packets to appear to come from that machine. This allows the attacker to potentially gain unauthorized access to the target system. Successful IP spoofing exploits flaws in the TCP/IP protocol that allow modification of source IP addresses but rely on the destination address for routing responses. Defenses include packet filtering, filtering at routers, encryption, and cryptographic methods.
Similar to Advanced routing worm and its security challenges (20)
This document is the manual for PHP, the PHP Documentation Group's copyright from 1997 to 2002. It contains information about installing and configuring PHP on various operating systems like Unix, Linux, Windows, etc. It also covers PHP syntax, functions, classes, and other features. The manual is distributed under the GNU General Public License and parts of it are also distributed under the Open Publication License. It was translated into Italian with contributions from multiple people.
Broadband network virus detection system based on bypass monitorUltraUploader
The document describes a Broadband Network Virus Detection System (VDS) based on bypass monitoring that can detect viruses on high-speed networks. The VDS uses four detection engines to analyze network traffic for viruses based on binary content, URLs, emails, and scripts. It accurately logs statistical information on detected viruses like name, source/target IPs, and spread frequency. The VDS mirrors network traffic to a detection engine in real-time without needing to reassemble packets into files. This allows it to efficiently detect viruses directly in network packets or data streams on gigabit-speed networks.
This document discusses botnets and their applications. It begins with an overview of botnets, how they are controlled through command and control servers, and how rootkits can help conceal botnet activity. It then explores how botnets can be used for spam, phishing, click fraud, identity theft, and distributed denial-of-service attacks. Detection and mitigation techniques are also summarized, including network intrusion detection, honeynets, DNS monitoring, and modeling botnet propagation across timezones. Recent botnets like AgoBot, PhatBot, and Bobax are also examined in the context of spam distribution. Open research questions around botnet membership detection, click fraud detection, and phishing detection are presented.
Bot software spreads, causes new worriesUltraUploader
Bot software infects millions of computers worldwide without the owners' knowledge and turns them into zombies that perform malicious tasks as part of a bot network. These bot networks, which can include thousands of infected computers, are used to spread viruses and worms, send spam emails, install spyware, and launch denial-of-service attacks. While initially just an automated way to spread malware, bot networks are now also used for criminal activities like identity theft due to their ability to stealthily command a large number of compromised computers. Security experts warn that the proliferation of bot networks poses serious risks and is very difficult to stop given their automation and scale.
Blended attacks exploits, vulnerabilities and buffer overflow techniques in c...UltraUploader
The document discusses blended threats that combine exploits and vulnerabilities with computer viruses. It begins with definitions of blended attacks and buffer overflows. It then describes three generations of buffer overflow techniques as well as other vulnerabilities exploited by blended threats, such as URL encoding and MIME header parsing. The document also discusses past threats like the Morris worm and CodeRed that blended exploits with viruses, and techniques used to combat future blended threats through defense in depth.
Win32/Blaster was a worm that exploited a vulnerability in Windows RPC to infect systems running Windows 2000 and Windows XP. It installed itself to automatically run on startup and then attempted to infect other systems on the local network and randomly selected IP addresses. The infection process involved exploiting the RPC vulnerability to execute a remote shell, downloading the worm binary, and executing it. It also launched a SYN flooding DDoS attack against Windows Update sites each month after the 16th. The worm spread quickly after the vulnerability was disclosed and highlighted the increasing automation and harm of worms.
Bird binary interpretation using runtime disassemblyUltraUploader
The document describes BIRD (Binary Interpretation using Runtime Disassembly), a binary analysis and instrumentation infrastructure for the Windows/x86 platform. BIRD combines static and dynamic disassembly to guarantee that every instruction in a binary is analyzed before execution. It provides services to convert binary code to assembly and insert instrumentation code without affecting program semantics. The prototype took 12 student months to develop and can successfully analyze applications like Microsoft Office, Internet Explorer, and IIS with low overhead of below 4%.
Biologically inspired defenses against computer virusesUltraUploader
This document discusses two biologically inspired approaches to computer virus detection and removal: a neural network virus detector that learns to identify infected and uninfected programs, and a computer immune system that can automatically identify, analyze, and remove new viruses from a system. The neural network technique has been incorporated into an IBM commercial antivirus product, while the computer immune system is still in prototype form. Both aim to replace human analysis of viruses to allow faster response times needed to address increasing rates of new virus creation and spread.
1. The document discusses biological viruses and computer viruses, providing background on how biological viruses work by hijacking cellular mechanisms of DNA replication, transcription and translation. It defines a computer virus as a piece of code with self-replicating ability that relies on other programs to exist, similar to biological viruses. 2. Computer viruses can cause damage by infecting programs which then infect other programs, potentially spreading like an epidemic across connected computers. 3. The document argues that a better understanding of biological and computer mechanisms can help improve defenses against viruses.
Biological aspects of computer virologyUltraUploader
This document discusses biological aspects of computer viruses and how factors that influence the spread of biological pathogens can also affect the propagation of computer malware. It analyzes three major factors that influence the spread of a computer worm: the infection propagator, which examines characteristics of exploited vulnerabilities like prevalence and age; the target locator, which focuses on how worms find new targets; and the worm's virulence, which looks at aspects that increase its infectiousness. The document suggests studying computer virus propagation through the lens of epidemiology models used for infectious diseases.
Biological models of security for virus propagation in computer networksUltraUploader
This document discusses how biological models of disease propagation and defense mechanisms in living organisms can inspire new approaches to computer network security and virus detection. Specifically, it describes how genetic regulatory networks that turn off harmful genes, protein interaction networks that model cellular processes, and epidemiological models of disease spread can provide models for automatically detecting and containing computer viruses without relying solely on pre-defined virus signatures. The authors propose several new security models drawing on these biological analogies, such as using surrogate code to maintain system functionality when parts are shut off, modeling network interactions to determine how viruses propagate, and evolving network services in real-time to reconstitute functionality after attacks.