Biblio

The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.

Cloud computing dominates the information communication and technology landscape despite the presence of lingering security issues such as the interdependency problem. The latter is a co-residence conundrum where the attacker successfully compromises his target virtual machine by first exploiting the weakest (in terms of security) virtual machine that is hosted in the same server. To tackle this issue, we propose a novel virtual machine allocation policy that is based on the attacker's efficiency and coverage. By default, our allocation policy considers all legitimate users as attackers and then proceeds to host the users' virtual machines to the server where their efficiency and/or coverage are the smallest. Our simulation results show that our proposal performs better than the existing allocation policies that were proposed to tackle the same issue, by reducing the attacker's possibilities to zero and by using between 30 - 48% less hosts.

With rapid growth of network size and complexity, network defenders are facing more challenges in protecting networked computers and other devices from acute attacks. Traffic visualization is an essential element in an anomaly detection system for visual observations and detection of distributed DoS attacks. This paper presents an interactive visualization system called TVis, proposed to detect both low-rate and highrate DDoS attacks using Heron's triangle-area mapping. TVis allows network defenders to identify and investigate anomalies in internal and external network traffic at both online and offline modes. We model the network traffic as an undirected graph and compute triangle-area map based on incidences at each vertex for each 5 seconds time window. The system triggers an alarm iff the system finds an area of the mapped triangle beyond the dynamic threshold. TVis performs well for both low-rate and high-rate DDoS detection in comparison to its competitors.

The need to keep an attacker oblivious of an attack mitigation effort is a very important component of a defense against denial of services (DoS) and distributed denial of services (DDoS) attacks because it helps to dissuade attackers from changing their attack patterns. Conceptually, DDoS mitigation can be achieved by two components. The first is a decoy server that provides a service function or receives attack traffic as a substitute for a legitimate server. The second is a decoy network that restricts attack traffic to the peripheries of a network, or which reroutes attack traffic to decoy servers. In this paper, we propose the use of a two-stage map table extension Locator/ID Separation Protocol (LISP) to realize a decoy network. We also describe and demonstrate how LISP can be used to implement an oblivious DDoS mitigation mechanism by adding a simple extension on the LISP MapServer. Together with decoy servers, this method can terminate DDoS traffic on the ingress end of an LISP-enabled network. We verified the effectiveness of our proposed mechanism through simulated DDoS attacks on a simple network topology. Our evaluation results indicate that the mechanism could be activated within a few seconds, and that the attack traffic can be terminated without incurring overhead on the MapServer.