As cyber-crime grows, methods for preventing, detecting, and responding are growing as well. Research is examining new faster more automated methods for dealing with cyber-crime both from a technical and a behavioral standpoint. The articles cited here examine a number of facts of the problem and were published in the first half of 2014.

• Khobragade, P.K.; Malik, L.G., "Data Generation and Analysis for Digital Forensic Application Using Data Mining," Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on , vol., no., pp.458,462, 7-9 April 2014.  In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.
  Keywords: computer crime; data analysis; data mining; digital forensics; firewalls; storage management; FTK 4.0;Web browser; cyber-crime huge log data; cyber system; data analysis; data collection; data generation; data mining; data storage; digital forensic application; firewall logs; intrusion detection system; memory forensic analysis; network attack detection; network forensic analysis; network traces; network traffic; packet captures; remote system forensic; transactional data; Computers; Data mining; Data visualization; Databases; Digital forensics; Security; Clustering; Data Collection; Digital forensic tool; Log Data collection (ID#:14-2070)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6821438&isnumber=6821334

• Harsch, A; Idler, S.; Thurner, S., "Assuming a State of Compromise: A Best Practise Approach for SMEs on Incident Response Management," IT Security Incident Management & IT Forensics (IMF), 2014 Eighth International Conference on , vol., no., pp.76,84, 12-14 May 2014.  Up-to-date studies and surveys regarding IT security show, that companies of every size and branch nowadays are faced with the growing risk of cyber crime. Many tools, standards and best practices are in place to support enterprise IT security experts in dealing with the upcoming risks, whereas meanwhile especially small and medium sized enterprises(SMEs) feel helpless struggling with the growing threats. This article describes an approach, how SMEs can attain high quality assurance whether they are a victim of cyber crime, what kind of damage resulted from a certain attack and in what way remediation can be done. The focus on all steps of the analysis lies in the economic feasibility and the typical environment of SMEs.
  Keywords: computer crime; small-to-medium enterprises; SME; best practices ;cybercrime; economic feasibility; enterprise IT security experts; incident response management; small and medium sized enterprises; Companies; Computer crime; Forensics; Malware; IT Security; Incident Response; SME; cybercrime; remediation (ID#:14-2071)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6824083&isnumber=6824069

• Mukaddam, A; Elhajj, I; Kayssi, A; Chehab, A, "IP Spoofing Detection Using Modified Hop Count," Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on , vol., no., pp.512,516, 13-16 May 2014. With the global widespread usage of the Internet, more and more cyber-attacks are being performed. Many of these attacks utilize IP address spoofing. This paper describes IP spoofing attacks and the proposed methods currently available to detect or prevent them. In addition, it presents a statistical analysis of the Hop Count parameter used in our proposed IP spoofing detection algorithm. We propose an algorithm, inspired by the Hop Count Filtering (HCF) technique, that changes the learning phase of HCF to include all the possible available Hop Count values. Compared to the original HCF method and its variants, our proposed method increases the true positive rate by at least 9% and consequently increases the overall accuracy of an intrusion detection system by at least 9%. Our proposed method performs in general better than HCF method and its variants.
  Keywords: IP networks; Internet; computer network security; statistical analysis; HCF learning phase; IP address spoofing utilization; IP spoofing attacks; IP spoofing detection; Internet; hop count filtering technique; modified hop count parameter; statistical analysis; Computer crime; Filtering; IP networks; Internet; Routing protocols; Testing; IP spoofing; hop count; hop count filtering; statistical analysis  (ID#:14-2072)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6838707&isnumber=6838626

• Fachkha, C.; Bou-Harb, E.; Debbabi, M., "Fingerprinting Internet DNS Amplification DDoS Activities," New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on , vol., no., pp.1,5, March 30 2014-April 2 2014.  This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.
  Keywords: Internet; computer network security; Internet-scale DNS amplification DDoS attacks ;anti-spam organizations; attack duration; backscattered analysis; cyber security intelligence; darknet space; detection period; distributed denial of service; fingerprinting Internet DNS amplification DDoS activities; geolocation; network-layer; packet size; storage capacity 720 Gbit; Computer crime; Grippers; IP networks; Internet; Monitoring; Sensors (ID#:14-2073)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6814019&isnumber=6813963

• Sgouras, K.I; Birda, AD.; Labridis, D.P., "Cyber attack impact on critical Smart Grid infrastructures," Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES , vol., no., pp.1,5, 19-22 Feb. 2014.  Electrical Distribution Networks face new challenges by the Smart Grid deployment. The required metering infrastructures add new vulnerabilities that need to be taken into account in order to achieve Smart Grid functionalities without considerable reliability trade-off. In this paper, a qualitative assessment of the cyber attack impact on the Advanced Metering Infrastructure (AMI) is initially attempted. Attack simulations have been conducted on a realistic Grid topology. The simulated network consisted of Smart Meters, routers and utility servers. Finally, the impact of Denial-of-Service and Distributed Denial-of-Service (DoS/DDoS) attacks on distribution system reliability is discussed through a qualitative analysis of reliability indices.
  Keywords: computer network security; power distribution reliability; power engineering computing; power system security; smart meters; smart power grids; AMI; DoS-DDoS attacks; advanced metering infrastructure; critical smart grid infrastructures;cyber attack impact; distributed denial-of-service attacks; distribution system reliability; electrical distribution networks;grid topology; qualitative assessment; routers; smart grid deployment; smart meters; utility servers; Computer crime; Reliability; Servers; Smart grids; Topology; AMI; Cyber Attack; DDoS ;DoS; Reliability; Simulation; Smart Grid (ID#:14-2074)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6816504&isnumber=6816367

• Sung-Hwan Ahn; Nam-Uk Kim; Tai-Myoung Chung, "Big data analysis system concept for detecting unknown attacks," Advanced Communication Technology (ICACT), 2014 16th International Conference on , vol., no., pp.269,272, 16-19 Feb. 2014.  Recently, threat of previously unknown cyber-attacks are increasing because existing security systems are not able to detect them. Past cyber-attacks had simple purposes of leaking personal information by attacking the PC or destroying the system. However, the goal of recent hacking attacks has changed from leaking information and destruction of services to attacking large-scale systems such as critical infrastructures and state agencies. In the other words, existing defense technologies to counter these attacks are based on pattern matching methods which are very limited. Because of this fact, in the event of new and previously unknown attacks, detection rate becomes very low and false negative increases. To defend against these unknown attacks, which cannot be detected with existing technology, we propose a new model based on big data analysis techniques that can extract information from a variety of sources to detect future attacks. We expect our model to be the basis of the future Advanced Persistent Threat(APT) detection and prevention system implementations.
  Keywords: Big Data; computer crime; data mining; APT detection; Big Data analysis system; Big Data analysis techniques; advanced persistent threat detection; computer crime ;critical infrastructures; cyber-attacks; data mining; defense technologies; detection rate; future attack detection; hacking attacks; information extraction; large-scale system attacks; pattern matching methods; personal information leakage; prevention system; security systems; service destruction; state agencies; unknown attack detection; Data handling; Data mining; Data models; Data storage systems; Information management; Monitoring; Security; Alarm systems; Computer crime; Data mining; Intrusion detection (ID#:14-2075)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6778962&isnumber=6778899

• Yi-Lu Wang; Sang-Chin Yang, "A Method of Evaluation for Insider Threat," Computer, Consumer and Control (IS3C), 2014 International Symposium on , vol., no., pp.438,441, 10-12 June 2014.  Due to cyber security is an important issue of the cloud computing. Insider threat becomes more and more important for cyber security, it is also much more complex issue. But till now, there is no equivalent to a vulnerability scanner for insider threat. We survey and discuss the history of research on insider threat analysis to know system dynamics is the best method to mitigate insider threat from people, process, and technology. In the paper, we present a system dynamics method to model insider threat. We suggest some concludes for future research who are interested in insider threat issue The study.
  Keywords: cloud computing; security of data; cloud computing; cyber security; insider threat analysis ;insider threat evaluation; insider threat mitigation ;vulnerability scanner; Analytical models; Computer crime; Computers; Educational institutions; Organizations; Insider threat; System Dynamic (ID#:14-2076)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6845913&isnumber=6845429

• Djouadi, Seddik M.; Melin, Alexander M.; Ferragut, Erik M.; Laska, Jason A; Dong, Jin, "Finite energy and bounded attacks on control system sensor signals," American Control Conference (ACC), 2014 , vol., no., pp.1716,1722, 4-6 June 2014.  Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.
  Keywords: Closed loop systems; Computer crime; Cost function; Eigenvalues and eigenfunctions; Generators; Vectors; Control applications; Emerging control theory; Fault-tolerant systems (ID#:14-2077)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6859001&isnumber=6858556

Note:

Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) SecureDataBank.net for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.