Preventing Distributed Denial-of-Service Attacks on the IMS Emergency Services Support through Adaptive Firewall Pinholing

Emergency services are vital services that Next Generation Networks (NGNs) have to provide. As the IP Multimedia Subsystem (IMS) is in the heart of NGNs, 3GPP has carried the burden of specifying a standardized IMS-based emergency services framework. Unfortunately, like any other IP-based standards, the IMS-based emergency service framework is prone to Distributed Denial of Service (DDoS) attacks. We propose in this work, a simple but efficient solution that can prevent certain types of such attacks by creating firewall pinholes that regular clients will surely be able to pass in contrast to the attackers clients. Our solution was implemented, tested in an appropriate testbed, and its efficiency was proven.Comment: 17 Pages, IJNGN Journa

PassGAN: A Deep Learning Approach for Password Guessing

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd

Optimal security-constrained power scheduling by Benders decomposition

This paper presents a Benders decomposition approach to determine the optimal day-ahead power scheduling in a pool-organized power system, taking into account dispatch, network and security constraints. The study model considers the daily market and the technical constraints resolution as two different and consecutive processes. The daily market is solved in a first stage subject to economical criteria exclusively and then, the constraints solution algorithm is applied to this initial dispatch through the redispatching method. The Benders partitioning algorithm is applied to this constraints solution process to obtain an optimal secure power scheduling. The constraints solution includes a full AC network and security model to incorporate voltages magnitudes as they are a critical factor in some real power systems. The algorithm determines the active power committed to each generator so as to minimize the energy redispatch cost subject to dispatch, network and security constraints. The solution also provides the reactive power output of the generators, the value of the transformers taps and the committed voltage control devices. The model has been tested in the IEEE 24-bus Reliability Test System and in an adapted IEEE 118-bus Test System. It is programmed in GAMS mathematical modeling language. Some relevant results are reported.Publicad

An Implementation of Intrusion Detection System Using Genetic Algorithm

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate