A cyber range, a practical and highly educational information security exercise system, is difficult to implement in educational institutions because of the high cost of implementing and maintaining it. Therefore, there is a need for a cyber range that can be adopted and maintained at a low cost. Recently, container type virtualization is gaining attention as it can create a high-speed and high-density exercise environment. However, existing researches have not clearly shown the advantages of container virtualization for building exercise environments. And it is not clear whether the sufficient vulnerabilities are reproducible, which is required to conduct incident scenarios in cyber range. In this paper, we compare container virtualization with existing virtualization type and confirm that the amount of memory, CPU, and storage consumption can be reduced to less than 1/10 of the conventional virtualization methods. We also compare and verify the reproducibility of the vulnerabilities used in common exercise scenarios and confirm that 99.3% of the vulnerabilities are reproducible. The container-based cyber range can be used as a new standard to replace existing methods.

In recent years, cyberattack techniques have become more and more sophisticated each day. Even if defense measures are taken against cyberattacks, it is difficult to prevent them completely. It can also be said that people can only fight defensively against cyber criminals. To address this situation, it is necessary to predict cyberattacks and take appropriate measures in advance, and the use of intelligence is important to make this possible. In general, many malicious hackers share information and tools that can be used for attacks on the dark web or in the specific communities. Therefore, we assume that a lot of intelligence, including this illegal content exists in cyber space. By using the threat intelligence, detecting attacks in advance and developing active defense is expected these days. However, such intelligence is currently extracted manually. In order to do this more efficiently, we apply machine learning to various forum posts that exist on the dark web, with the aim of extracting forum posts containing threat information. By doing this, we expect that detecting threat information in cyber space in a timely manner will be possible so that the optimal preventive measures will be taken in advance.

In this research, we examine and develop an expert system with a mechanism to automate crime category classification and threat level assessment, using the information collected by crawling the dark web. We have constructed a bag of words from 250 posts on the dark web and developed an expert system which takes the frequency of terms as an input and classifies sample posts into 6 criminal category dealing with drugs, stolen credit card, passwords, counterfeit products, child porn and others, and 3 threat levels (high, middle, low). Contrary to prior expectations, our simple and explainable expert system can perform competitively with other existing systems. For short, our experimental result with 1500 posts on the dark web shows 76.4% of recall rate for 6 criminal category classification and 83% of recall rate for 3 threat level discrimination for 100 random-sampled posts.

In recent years, cyber attack techniques are increasingly sophisticated, and blocking the attack is more and more difficult, even if a kind of counter measure or another is taken. In order for a successful handling of this situation, it is crucial to have a prediction of cyber attacks, appropriate precautions, and effective utilization of cyber intelligence that enables these actions. Malicious hackers share various kinds of information through particular communities such as the dark web, indicating that a great deal of intelligence exists in cyberspace. This paper focuses on forums on the dark web and proposes an approach to extract forums which include important information or intelligence from huge amounts of forums and identify traits of each forum using methodologies such as machine learning, natural language processing and so on. This approach will allow us to grasp the emerging threats in cyberspace and take appropriate measures against malicious activities.

We analyze the theoretical vulnerability of maximum a posteriori(MAP) speaker adaptation, which is widely used in practical speaker recognition systems. First, we proved that there exist a set of feature vectors, what are called wolves, which can impersonate almost all the registered speakers with probability asymptotically close to 1 with at most two trials. Second, our experiment shows that the wolves with appropriate parameters achieved 0.99 of successful impersonation rate on Spear speaker recognition toolkit with ATR speech database.

Impersonation by impostors is one of the representative security issues on biometric authentication system. A wolf attack is an attack on biometrics system using a wolf that can be falsely accepted as a match with multiple templates. False acceptance rate (FAR) which has been a conventional standard measure to quantify the average error rates of detecting the impersonation has not taken into consideration that impostors could use artefacts instead of templates generated from an individual. The wolf attack probability (WAP) is thus used as a new measure for evaluating the security of biometric authentication. In this paper, we focus on the vulnerability of likelihood-ratio-based biometric verification scheme that is known as optimal similarity measure in terms of average error rates. First, we present theoretical analysis of a likelihood-ratio-based biometric verification system and show the existence of wolf features under the assumption that there is an approximation error between background model and true feature distribution. Second, we propose a new wolf attack scheme that can achieve 60% of WAP. Furthermore, we empirically evaluate the proposed wolf attack using real biometric data from ATR speech database.

We propose a new cooperation mechanism, denoted by cooperative channel segmentation (CCS), between IEEE 802.11 WLANs and Bluetooth based WPANs, which operate in the 2.4 GHz ISM band. The CCS avoids false detection of WLAN carrier sense, which usually happens when WLAN and Bluetooth antenna are nearly deployed. In CCS, WLAN and Bluetooth system share the mutual interference channel information and devices operation channels between Bluetooth and WLAN. We evaluated the performance of CCS by simulation, and the simulation result showed that the performance of CCS is better than legacy cooperation mechanisms.

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Explosive progress in networking and outsourcing storage increases the use of information retrieval technologies, in massive datasets. Nowadays, there are varieties of storage-providers through the internet, such as e-mail accounts and public database, which are convenient to store and exchange electronic files and medias. Typically, the storage-provider offers users the capability to collect, retrieve and search, however, privacy issues are rarely considered at the same time. For example, it is unknown how to prevent some curious storage- provider from learning the private information of the user, such as, searching criterion and access pattern, as well as contents. In CRYPTO'07, Boneh et al. put forward a privacy-preserving solution to this problem, with the help of public key cryptography. In their work, the authors made use of PIR (Private Information Retrieval) and several combinatoric techniques, which are theoretically interesting and likely to be the best approach in the literature. In this paper, however, we show that their proposal seems unlikely to be implementable with the latest technology, due to a large amount of computation cost involved. Then, we provide an improved method to turn the keyword search more practical, which cannot only avoid the expensive computation cost caused by operations of public key encryption, but enable the privacy-preserving information retrieval, as well.

This paper presents a novel security architecture for in-vehicle communication. The ratio of electronics to vehicle equipment is steadily increasing. And novel vehicles will also have connectibility to public networks to provide many kinds of services. Therefore, they are expected to suffer from a wide variety of threats and the electronic control units (ECUs) embedded in them may execute execute malicious programs because of tampering. The remote attestation scheme with the trusted platform module (TPM) has been attracting a great deal of attention to cope with such issues. However, it is not feasible for vehicle systems because the conventional attestation process cannot adapt to in-vehicle communication and TPM cannot adapt to time-constrained vehicle systems. We propose an attestation based security architecture that is suitable for novel vehicles.

In this paper, we survey the recent research results on privacy-preserving Identification suitable for limited-resource devices such as RFID, contactless smartcards, and introduce our recent results on a light-weight privacy-preserving identification scheme. The proposed scheme only requires (1) random bit generators, (2) simple bit-wise operations and (3) short storage for keys less than 1 Kbits. No cryptographic algorithms such as SHA-1 are required. On the other hand, security of the scheme is reducible to learning parity -with noise problem (LPN problem) which is further reducible to a problem in NP-complete.

This paper proposes a human-assisting manipulator teleoperated by electromyographic (EMG) signals and arm motions. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller. A person whose forearm has been amputated can use this manipulator as a personal assistant for desktop work. The control system consists of a hand and wrist control part and an arm control part. The hand and wrist control part selects an active joint in the manipulator's end-effector and controls it based on EMG pattern discrimination. The arm control part measures the position of the operator's wrist joint or the amputated part using a three-dimensional position sensor, and the joint angles of the manipulator's arm, except for the end-effector part, are controlled according to this position, which, in turn, corresponds to the position of the manipulator's joint. These control parts enable the operator to control the manipulator intuitively. The distinctive feature of our system is to use a novel statistical neural network for EMG pattern discrimination. The system can adapt itself to changes of the EMG patterns according to the differences among individuals, different locations of the electrodes, and time variation caused by fatigue or sweat. Our experiments have shown that the developed system could learn and estimate the operator's intended motions with a high degree of accuracy using the EMG signals, and that the manipulator could be controlled smoothly. We also confirmed that our system could assist the amputee in performing desktop work.

Summary form only given. We discuss information-theoretic methods to prove the security of cryptosystems. We study what is called, unconditionally secure (or information-theoretically secure) cryptographic schemes in search for a system that can provide long-term security and that does not impose limits on the adversary's computational power.

This paper proposes a training system based on EMG signals for prosthetic control and the development of its prototype. This system aims to enhance three kinds of control ability: muscular contraction, cooperation among several muscles, and the timing of EMG generation. For EMG signal processing a statistical neural network is used which can adapt itself to changing EMG patterns according to the differences among individuals, the different locations of the electrodes, the time variation caused by fatigue or sweat, and so on. During training, EMG signal information is displayed in the feedback monitor. The experiments have been conducted using the prototype system. The subject is a 51 year old man who had his forearm amputated when he was 18 years old. After training for five days, the ability to manipulate the EMG signal of the subject has been enhanced and the effectiveness of this system is shown.

This paper proposes the concept of a human-robot interface as rehabilitation aid and develops the prototype system. The prototype system aims to be used as a controller for the robotic manipulator and as rehabilitation system for the handicapped person. In order to adapt the system to the characteristics of the operator's electromyogram (EMG) signal, the EMG pattern discrimination method using the neural network is utilized as an essential technique of our system. In the experiments, it can be seen that the robotic manipulator can be controlled with high accuracy using the operator's EMG signal, and that the adaptive learning of the neural network improves the discrimination ability of the EMG signal. The rehabilitation program and biofeedback are also discussed.