DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

Detecting Zero-day Polymorphic Worms with Jaccard Similarity Algorithm

Zero-day polymorphic worms pose a serious threat to the security of Mobile systems and Internet infrastructure. In many cases, it is difficult to detect worm attacks at an early stage. There is typically little or no time to develop a well-constructed solution during such a worm outbreak. This is because the worms act only to spread from node to node and they bring security concerns to everyone using Internet via any static or mobile node. No system is safe from an aggressive worm crisis. However, many of the characteristics of a worm can be used to defeat it, including its predictable behavior and shared signatures. In this paper, we propose an efficient signature generation method based on string similarity algorithms to generate signatures for Zero-day polymorphic worms. Then, these signatures are practically applied to an Intrusion Detection System (IDS) to prevent the network from such attacks. The experimental results show the efficiency of the proposed approach compared to other existing mechanisms

Moving target defense for securing smart grid communications: Architectural design, implementation and evaluation

Supervisory Control And Data Acquisition (SCADA) communications are often subjected to various kinds of sophisticated cyber-attacks which can have a serious impact on the Critical Infrastructure such as the power grid. Most of the time, the success of the attack is based on the static characteristics of the system, thereby enabling an easier profiling of the target system(s) by the adversary and consequently exploiting their limited resources. In this thesis, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, which leverages the existing communication network with an end-to-end IP Hopping technique among the trusted peer devices. This offers a proactive L3 layer network defense, minimizing IP-specific threats and thwarting worm propagation, APTs, etc., which utilize the cyber kill chain for attacking the system through the SCADA network. The main contribution of this thesis is to show how MTD concepts provide proactive defense against targeted cyber-attacks, and a dynamic attack surface to adversaries without compromising the availability of a SCADA system. Specifically, the thesis presents a brief overview of the different type of MTD designs, the proposed MTD architecture and its implementation with IP hopping technique over a Control Center–Substation network link along with a 3-way handshake protocol for synchronization on the Iowa State’s Power Cyber testbed. The thesis further investigates the delay and throughput characteristics of the entire system with and without the MTD to choose the best hopping rate for the given link. It also includes additional contributions for making the testbed scenarios more realistic to real world scenarios with multi-hop, multi-path WAN. Using that and studying a specific attack model, the thesis analyses the best ranges of IP address for different hopping rate and different number of interfaces. Finally, the thesis describes two case studies to explore and identify potential weaknesses of the proposed mechanism, and also experimentally validate the proposed mitigation alterations to resolve the discovered vulnerabilities. As part of future work, we plan to extend this work by optimizing the MTD algorithm to be more resilient by incorporating other techniques like network port mutation to further increase the attack complexity and cost

Search Based Clustering for Protecting Software with Diversified Updates

Reverse engineering is usually the stepping stone of a variety of attacks aiming at identifying sensitive information (keys, credentials, data, algorithms) or vulnerabilities and flaws for broader exploitation. Software applications are usually deployed as identical binary code installed on millions of computers, enabling an adversary to develop a generic reverse-engineering strategy that, if working on one code instance, could be applied to crack all the other instances. A solution to mitigate this problem is represented by Software Diversity, which aims at creating several structurally different (but functionally equivalent) binary code versions out of the same source code, so that even if a successful attack can be elaborated for one version, it should not work on a diversified version. In this paper, we address the problem of maximizing software diversity from a search-based optimization point of view. The program to protect is subject to a catalogue of transformations to generate many candidate versions. The problem of selecting the subset of most diversified versions to be deployed is formulated as an optimisation problem, that we tackle with different search heuristics. We show the applicability of this approach on some popular Android apps