Biblio

We present a simple approach for detecting brute force attacks in the CSE-CIC-IDS2018 Big Data dataset. We show our approach is preferable to more complex approaches since it is simpler, and yields stronger classification performance. Our contribution is to show that it is possible to train and test simple Decision Tree models with two independent variables to classify CSE-CIC-IDS2018 data with better results than reported in previous research, where more complex Deep Learning models are employed. Moreover, we show that Decision Tree models trained on data with two independent variables perform similarly to Decision Tree models trained on a larger number independent variables. Our experiments reveal that simple models, with AUC and AUPRC scores greater than 0.99, are capable of detecting brute force attacks in CSE-CIC-IDS2018. To the best of our knowledge, these are the strongest performance metrics published for the machine learning task of detecting these types of attacks. Furthermore, the simplicity of our approach, combined with its strong performance, makes it an appealing technique.

There are various Lightweight Block Ciphers (LBC) nowadays that exist to meet the demand on security requirements of the current trend in computing world, the application in the resource-constrained devices, and the Internet of Things (IoT) technologies. One way to evaluate these LBCs is to conduct a performance analysis. Performance evaluation parameters seek appropriate value such as encryption time, security level, scalability, and flexibility. Like SIMECK block cipher whose algorithm design was anchored with the SIMON and SPECK block ciphers were efficient in security and performance, there is a need to revisit its design. This paper aims to present a comparative study on the performance analysis of the enhanced round function of the SIMECK Family block cipher. The enhanced ARX structure of the round function on the three variants shows an efficient performance over the original algorithm in different simulations using the following methods of measurement; avalanche effect, runtime performance, and brute-force attack. Its recommended that the enhanced round function of the SIMECK family be evaluated by different security measurements and attacks.

Consider the problem of guessing a random vector X by submitting queries (guesses) of the form "Is X equal to x?" until an affirmative answer is obtained. A key figure of merit is the number of queries required until the right vector is guessed, termed the guesswork. The goal is to devise a guessing strategy which minimizes a certain guesswork moment. We study a universal, decentralized scenario where the guesser does not know the distribution of X, and is not allowed to prepare a list of words to be guessed in advance, or to remember its past guesses. Such a scenario is useful, for example, if bots within a Botnet carry out a brute-force attack to guess a password or decrypt a message, yet cannot coordinate the guesses or even know how many bots actually participate in the attack. We devise universal decentralized guessing strategies, first, for memoryless sources, and then generalize them to finite-state sources. For both, we derive the guessing exponent and prove its asymptotic optimality by deriving a matching converse. The strategies are based on randomized guessing using a universal distribution. We also extend the results to guessing with side information (SI). Finally, we design simple algorithms for sampling from the universal distributions.

In this work, we perform security analysis of using an e-mail as a self-service password reset point, and exploit some of the vulnerabilities of e-mail servers' forgotten password reset paths. We perform and illustrate three different attacks on a personal Email account, using a variety of tools such as: public knowledge attainable through social media or public records to answer security questions and execute a social engineering attack, hardware available to the public to perform a man in the middle attack, and free software to perform a brute-force attack on the login of the email account. Our results expose some of the inherent vulnerabilities in using emails as password reset points. The findings are extremely relevant to the security of mobile devices since users' trend has leaned towards usage of mobile devices over desktops for Internet access.

An information system is only as secure as its weakest point. In many information systems that remains to be the human factor, despite continuous attempts to educate the users about the importance of password security and enforcing password creation policies on them. Furthermore, not only do the average users' password creation and management habits remain more or less the same, but the password cracking tools, and more importantly, the computer hardware, keep improving as well. In this study, we performed a broad targeted attack combining several well-established cracking techniques, such as brute-force, dictionary, and hybrid attacks, on the passwords used by the students of a Slovenian university to access the online grading system. Our goal was to demonstrate how easy it is to crack most of the user-created passwords using simple and predictable patterns. To identify differences between them, we performed an analysis of the cracked and uncracked passwords and measured their strength. The results have shown that even a single low to mid-range modern GPU can crack over 95% of passwords in just few days, while a more dedicated system can crack all but the strongest 0.5% of them.

The integration of subset sum in the verifiable secret sharing scheme provides added security measure for a multiparty computation such as immediate identification of and removal of an imposter, avoidance or discourages man-in-the-middle attack and lattice-based attack, and lessens dealer's burden on processing monitoring the integrity of shareholders. This study focuses on the security assessment of a brute-force attack on the subset sum-based verifiable secret sharing scheme. With the simulation done using a generator of all possible fixed-length partition (which is k=3 as the least possible) summing up to the sum of the original subset generated by the dealer, it shows that it will already took 11,408 years to brute-force all possible values even on a small 32-bit-length value and 3.8455 years for a 128-bit length value thus proving that the resiliency on brute attack on the subset sum based VSSS can be discounted despite simplicity of the implementation. Zero knowledge on the number of threshold will also multiply to the impossibility of the brute force attack.

Establishing a digital identity plays a vital part in the digital era. It is crucial to authenticate and identify the users in order to perform online transactions securely. For example, internet banking applications normally require a user to present a digital identity, e.g., username and password, to allow users to perform online transactions. However, the username-password approach has several downsides, e.g., susceptible to the brute-force attack. Public key binding using Certificate Authority (CA) is another common alternative to provide digital identity. Yet, the public key approach has a serious drawback: all CAs in the browser/OS' CA list are treated equally, and consequently, all trusts on the certificates could be invalidated by compromising only a single root CA's private key. We propose a Real Digital Identity based approach, or RDI, on decentralized PKI scheme. The core idea relies on a combination of well-known parties (e.g., a bank, a government agency) to certify the identity, instead of relying on a single CA. These parties, collectively known as Trusted Source Certificate Authorities (TSCA), formed a network of CAs. The generated certificates are stored in the blockchain controlled by smart contract. RDI creates a digital identity that can be trusted based on the TSCAs' challenge/response and it is also robust against a single point of trust attack on traditional CAs.

IoT device usually has an associated application to facilitate customers' interactions with the device, and customers need to register an account to use this application as well. Due to the popularity of mobile phone, a customer is encouraged to register an account with his own mobile phone number. After binding the device to his account, the customer can control his device remotely with his smartphone. When a customer forgets his password, he can use his mobile phone to receive a verification code that is sent by the Short Message Service (SMS) to authenticate and reset his password. If an attacker gains this code, he can steal the victim's account (reset password or login directly) to control the IoT device. Although IoT device vendors have already deployed a set of security countermeasures to protect account such as setting expiration time for SMS authentication code, HTTP encryption, and application packing, this paper shows that existing IoT account password reset via SMS authentication code are still vulnerable to brute-force attacks. In particular, we present an automatic brute-force attack to bypass current protections and then crack IoT device user account. Our preliminary study on popular IoT devices such as smart lock, smart watch, smart router, and sharing car has discovered six account login zero-day vulnerabilities.