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Research Direction Introduction Advisor: Frank, Yeong-Sung Lin Presented by Hui-Yu, Chung.

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Presentation on theme: "Research Direction Introduction Advisor: Frank, Yeong-Sung Lin Presented by Hui-Yu, Chung."— Presentation transcript:

1 Research Direction Introduction Advisor: Frank, Yeong-Sung Lin Presented by Hui-Yu, Chung

2 Agenda Paper review – Contest success function – Worm Characteristics – Worm propagation Problem descriptions – Defender attributes – Attacker attributes – Attack-defense scenarios

3 Contest success function (CSF) The idea of CSF came from the problem ofrent-seeking in economic field – Which refers to efforts to capture special monopoly privileges The phenomenon of rent-seeking in connection with monopolies was first formally identified in 1967 by Gordon Tullock – To identify the probability that certain party wins the privilege Tullock, Gordon (1967). "The Welfare Costs of Tariffs, Monopolies, and Theft". Western Economic Journal 5 (3): 224–232

4 Contest success function (CSF) For 2 players in Tullocks basic model Original form: (Ratio form) Since p 1 + p 2 = 1, the original form can be transferred to: In our scenario, CSF is transformed as follow:

5 About contest intensity Contest intensity m – m=0 The efforts have equal impact on the vulnerability regardless of their size – 0 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/6/1615198/slides/slide_5.jpg", "name": "About contest intensity Contest intensity m – m=0 The efforts have equal impact on the vulnerability regardless of their size – 0

6 About contest intensity Contest intensity m – m>1 Disproportional advantage of investing more than ones opponent. – m= A step function where winner-takes-all – The most popular versions of the Tullock CSF are the lottery (m = 1) and the all-pay auction (m = ) God is on the side of larger battalions Like Auction Jack Hirshleifer "Conflict and rent-seeking success functions - Ratio vs difference models of relative success," Proc. Public Choice 63, 1989, pp.101-112 Jack Hirshleifer "The Paradox of Power," Proc. Economics and Politics Volume 3 November 1993, pp.177-200

7 About contest intensity The result came from Lanchester's laws – Which is used to calculating the relative strengths of a predator/prey pair by Frederick Lanchester in 1916, during the height of World War I. Lanchester's Linear Law – for ancient combat which one man could only ever fight exactly one other man at a time. Lanchester's Square Law – for modern combat with long-range weapons such as firearms

8 About contest intensity Inflection Point

9 Worm Characteristics Information collection Collect information about the local or target network. Probing Scans and detects the vulnerabilities of the specified host, determines which approach should be taken to attack and penetrate. Communication Communicate between worm and hacker or among worms. Attack Makes use of the holes gained by scanning techniques to create a propagation path. Self-propagating Uses various copies of worms and transfers these copies among different hosts.

10 Worm propagation model Classical epidemic model – Does not consider any countermeasures – Used to analyze complicated scenario Su Fei, Lin Zhaowen, Ma Yan A survey of internet worm propagation models Proc. IC-BNMT2009, pp.453-457 Stefan Misslinger Internet worm propagation, Departement for Computer Science Technische UniversitÄat MÄunchen

11 Worm propagation model Kermack-Mckendrick model SIR model – Takes remove process into consideration susceptible susceptible infectious removed – But doesnt take network congestion into account # of infectious hosts including removed hosts

12 Worm propagation model Two-factor Model – Considers human countermeasures and network countermeasures into account Increasing removable rate Decreasing infectious rate – More accurate model # of removed host from susceptible hosts # of removed host from infectious hosts Peoples awareness of the worm

13 Worm propagation time Two-factor fit (Code Red Worm in July 2001) – Take both I R and S R into account – Decreased infectious rate – About 120,000 hosts are infected in 8 hours Cliff Changchun Zou, Weibo Gong, Don Towsley, "Code Red Worm Propagation Modeling and Analysis"

14 Node compromise time Using State-space predator model to be the attack model and estimate the MTTC (Mean Time-to-Compromise) of the system Three levels of attacker capabilities – Beginner – Intermediate attacker – Expert attacker David John Leversage, Eric James Estimating a Systems Mean Time-to-Compromise, IEEE Computer Security & Privacy Volume 6, Number 1 pp. 52-60, January/February 2008

15 Node compromise time Divide the attackers actions into three statistical processes – Process 1 – The attacker has identified one or more known vulnerabilities and has one or more exploits on hand – Process 2 – The attacker has identified one or more known vulnerabilities but doesnt have an exploit on hand – Process 3 – No known vulnerabilities or exploits are available Mean time-to-compromise

16 Node compromise time Time-to-compromise – t 1, t 2, t 3 : expected mean time of process 1,2,3 – P 1 : prob. of a finding a vulnerability – u: failure probability to find an exploit – t 1 is hypothesized to be 1 working day (8 hrs) – t 2 is hypothesized to be 5.8*(expected tries) working days – t 3 = ((1/s)-0.5)*30.42+5.8 days, where s = AM/V

17 Node compromise time Estimated number or tries, ET – AM: avg # of vulnerabilities for which an exploit can be found or created by the attacker whose skill level is given – V: avg # of vulnerabilities per node within a zone – NM: the # of vulnerabilities an attacker with given skill wont be able to use NM = V-AM Expected avg time needed in process 2: – ET*5.8 working days

18 Node compromise time Skill indicator s = AM/V Prob. that attacker in process 1: – M: # of exploits readily available to the attacker – K: total # of nonduplicate vulnerabilities Prob. That process 2 is unsuccessful

19 Node compromise time Results Measured in working days

20 Agenda Paper review – Contest success function – Worm Characteristics – Worm propagation Problem descriptions – Defender attributes – Attacker attributes – Attack-defense scenarios

21 Attack-Defense scenario Collaborative attack – One commander who has a group of attackers – Different attackers has different attributes Budget, Capability – The commander has to decide his attack strategy at every round ex. # of attackers, resource used Once the strategy is given, all the attackers will exercise the attack simultaneously

22 Defender attributes Objective – Protect provided services Budget – General defense resources(ex: Firewall, IDS) – Worm profile distribution mechanisms – Worm source identification methods

23 Defender attributes General defense mechanisms – Defense resource on each node – Dynamic topology reconfiguration If the QoS is not satisfied, the disconnected link must be reconnect back Worm defense mechanisms – Decentralized information sharing system Unknown worm detection & profile distribution – Worm origin identification – Rate limiting To slow down worm propagation – Firewall reconfiguration May decrease QoS at the same time

24 Defender attributes Fixed defense resource – General defense resource on each node – Detection system on specific nodes Dynamic defense resource – Generating worm signatures Without expending budget – Worm origin identification – Rate limiting – Firewall reconfiguration – Dynamic topology reconfiguration

25 Attacker attributes Objective – To decrease the QoS of the defender – To steal information (by attacking some specific nodes) Budget – Preparing Phase: worm injection – Attacking Phase: node compromising

26 Attacker attributes Attack mechanisms – Compromising Nodes The goal is to finally compromise core nodes, which reduce the QoS of those core nodes to below certain level or steal sensitive information – Worm injection The purpose is to get further topology information After a node is compromised, the commander will decide whether to inject worms

27 Attacker attributes Process Using the aggressiveness of risk avoidance to compromise several nodes, and find the nodes with large traffic link to inject worms After getting the topology information of the defender by the worms, try to find the shortest path to the core node and compromise the nodes along the path If the attacker find that the defender uses dynamic topology reconfiguration and cut down the link along the shortest path, then he can use pretend to attack strategy to make the link connected back

28 Compromising nodes How to select the attackers? – The commander has to select the attackers who have enough attack resource The resource required is computed via contest success function During decision phase, all that commander has to do is to find out the interval of defense resource whose values are near the defense resource on that node – After every round the table will be updated by the new resource owned by the attacker selected

29 How to select the attackers? A corresponding defense resource table is created right after the defender had constructed his network topology – The value of an attacker resource T is computed by the budget and attack time of that attacker Attack power Aggressiveness – The value of the defense resource t is the defense resource on a node in the network – The table is sorted in ascending order of t

30 How to select the attackers? Defense RscAttacker RscAggressiveness 102290.3 1952000.5 ……… 5989290.9 6014870.4 6028080.7 6099530.8 ……… 1036 11398050.2 AggressivenessDf RscAt Rsc 0.4601487 0.7602808 0.8609953 0.9598929 ……… The budget, capability, and aggressiveness of the attackers is predetermined. The value of contest intensity m is given

31 Aggressiveness High Aggressiveness (Risk avoidance) – Often used to compromise nodes – Before worm injection – Higher when approaching core nodes Low Aggressiveness (Risk tolerance) – Used to pretend to attack – Ex. To lower the risk level of certain core node

32 Worm injection Used to get more topology information behind nodes before compromising them – After compromising one node, the attacker can decide whether to inject a worm into it – Often choose a node with high link degree to inject worms Worm Immune – Once a worm is detected by the defender, the defender may take some defense mechanism to immune from it – In that case, the attacker has to inject another type worm to get new information Different types of worms – Scanning method, propagation rate, capability

33 Terminate Condition The QoS decreases to a certain level The attacker has got the sensitive information The attacker runs out of his budget

34 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System

35 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander One attacker to compromise node A Compromised

36 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Two attackers to compromise node C &D Compromised

37 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Inject Type I worm to node C Type I Worm

38 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Type I Worm Self-propagation of the worm

39 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Two attackers to compromise node I & F Type I Worm Compromised

40 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Type I Worm Compromised Detection alarm

41 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Two attackers to compromise node N & J Type I Worm Detection alarm Compromised

42 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Inject type II worm to node N and J Type I Worm Detection alarm Type II Worm

43 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Type I Worm Detection alarm Type II Worm

44 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Type I Worm Detection alarm Type II Worm Dynamic topology reconfiguration Firewall reconfiguration Worm origin identification Rate limiting

45 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Two attackers to compromise node Q & P Type I Worm Detection alarm Type II Worm Firewall reconfiguration Rate limiting

46 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Type I Worm Detection alarm Type II Worm Dynamic topology reconfiguration Reconnect to satisfy QoS Firewall reconfiguration Rate limiting

47 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander One attacker to compromise node O Type I Worm Detection alarm Type II Worm Firewall reconfiguration Rate limiting

48 Scenarios AS Node Core AS Node Firewall Decentralized Information Sharing System Attacker Commander Two attackers to compromise core node R & S Type I Worm Detection alarm Type II Worm Firewall reconfiguration Rate limiting

49 ~THANKS FOR YOUR ATTENTION~


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