Detection Scheme

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The Experts below are selected from a list of 113472 Experts worldwide ranked by ideXlab platform

Tetsuya Kawanishi - One of the best experts on this subject based on the ideXlab platform.

Toshiaki Kuri - One of the best experts on this subject based on the ideXlab platform.

Takahide Sakamoto - One of the best experts on this subject based on the ideXlab platform.

Jingon Joung - One of the best experts on this subject based on the ideXlab platform.

  • a sybil attack Detection Scheme based on adas sensors for vehicular networks
    Consumer Communications and Networking Conference, 2020
    Co-Authors: Kiho Lim, Tariqul Islam, Hyunbum Kim, Jingon Joung
    Abstract:

    Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack Detection Scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object Detection technique is used to accurately identify nearby objects for Sybil attack Detection and the multi-step verification process minimizes the false positive of the Detection.

Iwao Sasase - One of the best experts on this subject based on the ideXlab platform.

  • android malware Detection Scheme based on level of ssl server certificate
    Global Communications Conference, 2019
    Co-Authors: Hiroya Kato, Shuichiro Haruta, Iwao Sasase
    Abstract:

    Detecting Android malware is imperative. As a promising Android malware Detection Scheme, we focus on the Scheme leveraging the differences of traffic patterns between benign apps and malware. Those differences can be captured even if the packet is encrypted. However, since such features are just statistic based ones, they cannot identify whether each traffic is malicious. Thus, it is necessary to design the Scheme which is applicable to encrypted traffic data and supports identification of malicious traffic. In this paper, we propose an Android malware Detection Scheme based on the level of SSL server certificate. Attackers tend to use an untrusted certificate to encrypt malicious payloads in many cases because passing rigorous examination is required to get a trusted certificate. Thus, we utilize SSL server certificate based features for Detection since their certificates tend to be untrusted. Furthermore, in order to obtain the more exact features, we introduce required permission based weight values because malware inevitably require permissions regarding malicious actions. By computer simulation with real dataset, we show our Scheme achieves an accuracy of 92.7 %. True positive rate and false positive rate are 5.6% higher and 3.3% lower than the previous Scheme, respectively. Our Scheme can cope with encrypted malicious payloads and 89 malware which are not detected by the previous Scheme.

  • traceroute based target link flooding attack Detection Scheme by analyzing hop count to the destination
    Asia-Pacific Conference on Communications, 2017
    Co-Authors: Kei Sakuma, Shuichiro Haruta, Hiromu Asahina, Iwao Sasase
    Abstract:

    Recently, the Detection of target link flooding attack which is a new type of DDoS (Distributed Denial of Service) is required. Target link flooding attack is used for disconnecting a specific area from the Internet. It is more difficult to detect and mitigate this attack than legacy DDoS since attacking flows do not reach the target region. Among several Schemes for target link flooding attack, the Scheme focusing on traceroute is gathering attention. The idea behind that is the attacker needs to send traceroute to investigate the topology around targeted region before attack starts. That Scheme detects the attack by finding rapid increase of traceroute. However, it cannot work when attacker's traceroute ratio is low. In this paper, we propose traceroute-based target link flooding attack Detection Scheme by analyzing hop count to the destination. Since the attacker must choose the link flooded to disconnect the target area, the destinations of attacker's traceroutes are concentrated within several hops from the target link while legitimate user's ones are distributed uniformly. By analyzing the number of traceroutes as per hop counts, the change can be emphasized and the attack symptom might be more easily captured. By computer simulations, we first prove the above hypotheses and show that our Scheme has more robustness compared with the conventional Scheme.

  • Visual Similarity-Based Phishing Detection Scheme Using Image and CSS with Target Website Finder
    GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017
    Co-Authors: Shuichiro Haruta, Hiromu Asahina, Iwao Sasase
    Abstract:

    The Detection of phishing websites and identifying their target are imperative. Among several phishing Detection Schemes, the Scheme using visual similarity is gathering attention. It takes a screenshot of website and stores it to the database. If the inputted website''s screenshot is similar to database''s one, it is judged as phishing. However, if multiple similar websites exist, the first inputted website is regarded as legitimate. As a result, it cannot correctly detect legitimate website and identifying phishing target becomes difficult. As a second shortcoming, if the screenshot of phishing website is locally different from ones in the database, false negative occurs. In this paper, we propose visual similarity-based phishing Detection Scheme using image and CSS with target website finder. To remedy first shortcoming, we focus on the fact that legitimate websites are often linked by other websites and regard such website as legitimate and store the screenshot and CSS in the database. Since CSS is a file which defines the websites visual contents, attackers often steal legitimate CSS to mimic the legitimate website. Thus, by detecting the website which plagiarizes appearance or CSS of legitimate website, we detect phishing website and its target simultaneously. Moreover, we can alleviate the second shortcoming by using CSS because it is probable that the websites which have locally different appearance use identical CSS. By computer simulation with real dataset, we demonstrate our Scheme improves Detection accuracy while finding phishing target.

  • fast target link flooding attack Detection Scheme by analyzing traceroute packets flow
    International Workshop on Information Forensics and Security, 2015
    Co-Authors: Takayuki Hirayama, Kentaroh Toyoda, Iwao Sasase
    Abstract:

    Recently, a botnet based DDoS (Distributed Denial of Service) attack, called target link flooding attack, has been reported that cuts off specific links over the Internet and disconnects a specific region from other regions. Detecting or mitigating the target link flooding attack is more difficult than legacy DDoS attack techniques, since attacking flows do not reach the target region. Although many mitigation Schemes are proposed, they detect the attack after it occurs. In this paper, we propose a fast target link flooding attack Detection Scheme by leveraging the fact that the traceroute packets are increased before the attack caused by the attacker's reconnaissance. Moreover, by analyzing the characteristic of the target link flooding attack that the number of traceroute packets simultaneously increases in various regions over the network, we propose a Detection Scheme with multiple Detection servers to eliminate false alarms caused by sudden increase of traceroute packets sent by legitimate users. We show the effectiveness of our Scheme by computer simulations.

  • unsupervised clustering based spitters Detection Scheme
    Journal of Information Processing, 2015
    Co-Authors: Kentaroh Toyoda, Iwao Sasase
    Abstract:

    VoIP /SIP is taking place of conventional telephony because of very low call charge but it is also attractive for SPITters who advertise or spread phishing calls toward many callees. Although there exist many feature-based SPIT Detection methods, none of them provides the flexibility against multiple features and thus complex threshold settings and training phases cannot be avoided. In this paper, we propose an unsupervised and threshold-free SPITters Detection Scheme based on a clustering algorithm. Our Scheme does not use multiple features directly to trap SPITters but uses them to find the dissimilarity among each caller pair and tries to separate the callers into a SPITters cluster and a legitimate one based on the dissimilarity. By computer simulation, we show that the combination of Random Forests dissimilarity and PAM clustering brings the best classification accuracy and our Scheme works well when the SPITters account for more than 20% of the entire caller.

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